Healthy software companies typically don’t suddenly eliminate one-fifth of their workforce, but monday.com is doing just that as it bets on flatter teams, AI agents, and customer implementation expertise as the winning combination in the AI era.

Monday.com co-founder and co-CEO Eran Zinman today announced the “very difficult decision” to reduce the AI work platform company’s global workforce by about 20%, or 620 people.

The move has nothing to do with increasing margins or replacing humans with AI, he insisted in his post on LinkedIn; rather, it’s a calculated decision to trim down and hone the company’s focus as AI becomes integral to day-to-day workflows.

“This is not a distress signal; it is a deliberate reset, disclosed with its price attached,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “The industry has quietly swapped the meaning of productivity, and this filing is the clearest exhibit yet.”

A ‘significant opportunity’ in technology

In a SEC filing this week, monday.com said its restructuring plan reflects the “ongoing transformation of its product, marketing, and go-to-market strategy.” The move is intended to support a “leaner, more focused operating model” as the company continues to invest in its AI-driven strategy.

Zinman noted in his post that the company has shifted to “doing the work with AI and not just managing it,” and is focused on building environments where “people and AI agents [work] together in one workspace.”

In recent months, monday.com has evolved its products, strategy, and the way it serves its customers, and Zinman contended that “the organization we built for our previous chapter is not the organization that fits the new AI era.” Monday.com needs to “execute more decisively,” take on new challenges, and quickly respond to market changes, he said.

“We have never seen such a significant opportunity in software, driven by such exciting technology,” Zinman noted. He emphasized that the reduction is not to replace people with AI, nor to improve margins; the “vast majority” of savings will be reinvested into talent, products, and AI.

The restructuring will result in a “flatter organization” with fewer management layers and smaller, more autonomous teams, and monday.com also has a new go-to-market model, Zinman explained. Customers expect “deeper implementation support” as they deploy AI, and the company will work more closely with customers, increase its on-site presence, create new roles, and “adapt many existing ones.” In its SEC filing, the company said it expects to continue hiring in “key strategic areas” throughout 2026.

Workers will be expected to work better, “not harder,” Zinman noted. He pointed to several past examples where work could have been done in a few days, but instead took many months with “multiple meetings and endless friction.”

“This wasn’t people’s fault and everyone was frustrated by this,” he said. “Our new org changes ownership to allow people to make decisions and move fast.”

A spokesperson for monday.com declined to comment further on the staff reductions.

Monday.com’s key market advantages

Monday.com certainly isn’t struggling; the company expects 19% to 20% year-over-year growth in 2026.

“Companies in that position do not restructure because they must,” Greyhound’s Gogia noted. “They restructure because they have decided to become something else.”

Melody Brue, VP and principal analyst at Moor Insights & Strategy, pointed out that organizational redesign is important for real AI transformation, but while it can signal confidence to the market, it can still be “devastating” to humans.

While the company looks as though it’s trying to do right, that ultimately remains to be seen, she said. “There are often hidden internal bruises that can surface long after layoffs.”

Monday.com’s advantage is in its “structured substrate,” Gogia noted; its boards, permissions and typed workflows give agents something firmer to act on than just documents and chat history. The company highlights its natively built agents that can be configured by any team member, as well as connectors with Claude, Microsoft Copilot, and ChatGPT, and dedicated routes for external agents to authenticate and operate.

“For some time, the sharper enterprise question has been shifting from who has an agent to who owns the governed runtime in which an agent can safely act,” he said. “Structured work is a serious claim on that runtime.”

But parts of monday.com’s agent estate remain in staged release, and its product is ultimately “mid-transition,” Gogia pointed out; its agent builder carried a beta label as recently as March,. Also, the company’s pricing model changed in May to a hybrid model charging for seats as well as mandatory AI credits. And, while its AI-powered no-code builder monday vibe passed $1 million in annual recurring revenue within two and a half months, monday.com has not released subsequent outcomes, usage volumes, or attach rates.

Further, there’s an element of “gravity” with its competitors, he observed. Asana is reorganizing teams around agents, Atlassian is wiring agents into the developer estate, and others are simply bundling them into their offerings: Microsoft is doing so across the productivity stack, and ServiceNow across enterprise operations, each with identity and procurement built in.

“Their pull is strongest exactly where monday.com wants to grow, in the largest accounts, where control-plane depth and administrative reach decide the deal,” said Gogia.

Actions for the near-term

Going forward, buyers should focus on operating risk, not headline risk, Moor’s Brue noted. In practice, that’s continuity of service, roadmap consistency, and strength of enterprise support. Productivity should be valued as better outcomes per unit of organizational effort, not mere activity.

“It should be a measure of how much smoother, faster, and more effective the operating model becomes when AI is built into the work,” said Brue.

Gogia noted that strain surfaces first in customer service, and monday.com’s attention is being redistributed. The company’s annual report disclosed that its focus is now concentrated on the largest accounts, with support for medium-sized clients moved to an AI-first and human-supported model.

During the first month of the transition, buyers should track named account continuity and escalation times, he advised. By the first quarter, keep an eye on whether credit governance and admin controls mature on schedule, and if the roadmap beyond the AI estate keeps pace. By the half-year mark, determine whether promised implementation depth is producing outcomes or “simply more billable engagement.”

Support tiers should be enumerated in writing before renewal, and buyers should contract for “side exits,” Gogia emphasized, with overage pricing fixed in advance, the right to pause consumption, and portability for workflows and agent configuration “if the relationship sours.” Finance should also insist on monthly consumption reporting by capability. Further, integration efforts, partner dependency, and change management should be considered first-class costs of the agent era, “not as afterthoughts to a license.”

“A license was a known cost,” said Gogia. “A meter is a behavior, and behavior is harder to forecast than headcount.”

This article originally appeared on CIO.com.

Healthy software companies typically don’t suddenly eliminate one-fifth of their workforce, but monday.com is doing just that as it bets on flatter teams, AI agents, and customer implementation expertise as the winning combination in the AI era.

Monday.com co-founder and co-CEO Eran Zinman today announced the “very difficult decision” to reduce the AI work platform company’s global workforce by about 20%, or 620 people.

The move has nothing to do with increasing margins or replacing humans with AI, he insisted in his post on LinkedIn; rather, it’s a calculated decision to trim down and hone the company’s focus as AI becomes integral to day-to-day workflows.

“This is not a distress signal; it is a deliberate reset, disclosed with its price attached,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “The industry has quietly swapped the meaning of productivity, and this filing is the clearest exhibit yet.”

A ‘significant opportunity’ in technology

In a SEC filing this week, monday.com said its restructuring plan reflects the “ongoing transformation of its product, marketing, and go-to-market strategy.” The move is intended to support a “leaner, more focused operating model” as the company continues to invest in its AI-driven strategy.

Zinman noted in his post that the company has shifted to “doing the work with AI and not just managing it,” and is focused on building environments where “people and AI agents [work] together in one workspace.”

In recent months, monday.com has evolved its products, strategy, and the way it serves its customers, and Zinman contended that “the organization we built for our previous chapter is not the organization that fits the new AI era.” Monday.com needs to “execute more decisively,” take on new challenges, and quickly respond to market changes, he said.

“We have never seen such a significant opportunity in software, driven by such exciting technology,” Zinman noted. He emphasized that the reduction is not to replace people with AI, nor to improve margins; the “vast majority” of savings will be reinvested into talent, products, and AI.

The restructuring will result in a “flatter organization” with fewer management layers and smaller, more autonomous teams, and monday.com also has a new go-to-market model, Zinman explained. Customers expect “deeper implementation support” as they deploy AI, and the company will work more closely with customers, increase its on-site presence, create new roles, and “adapt many existing ones.” In its SEC filing, the company said it expects to continue hiring in “key strategic areas” throughout 2026.

Workers will be expected to work better, “not harder,” Zinman noted. He pointed to several past examples where work could have been done in a few days, but instead took many months with “multiple meetings and endless friction.”

“This wasn’t people’s fault and everyone was frustrated by this,” he said. “Our new org changes ownership to allow people to make decisions and move fast.”

A spokesperson for monday.com declined to comment further on the staff reductions.

Monday.com’s key market advantages

Monday.com certainly isn’t struggling; the company expects 19% to 20% year-over-year growth in 2026.

“Companies in that position do not restructure because they must,” Greyhound’s Gogia noted. “They restructure because they have decided to become something else.”

Melody Brue, VP and principal analyst at Moor Insights & Strategy, pointed out that organizational redesign is important for real AI transformation, but while it can signal confidence to the market, it can still be “devastating” to humans.

While the company looks as though it’s trying to do right, that ultimately remains to be seen, she said. “There are often hidden internal bruises that can surface long after layoffs.”

Monday.com’s advantage is in its “structured substrate,” Gogia noted; its boards, permissions and typed workflows give agents something firmer to act on than just documents and chat history. The company highlights its natively built agents that can be configured by any team member, as well as connectors with Claude, Microsoft Copilot, and ChatGPT, and dedicated routes for external agents to authenticate and operate.

“For some time, the sharper enterprise question has been shifting from who has an agent to who owns the governed runtime in which an agent can safely act,” he said. “Structured work is a serious claim on that runtime.”

But parts of monday.com’s agent estate remain in staged release, and its product is ultimately “mid-transition,” Gogia pointed out; its agent builder carried a beta label as recently as March,. Also, the company’s pricing model changed in May to a hybrid model charging for seats as well as mandatory AI credits. And, while its AI-powered no-code builder monday vibe passed $1 million in annual recurring revenue within two and a half months, monday.com has not released subsequent outcomes, usage volumes, or attach rates.

Further, there’s an element of “gravity” with its competitors, he observed. Asana is reorganizing teams around agents, Atlassian is wiring agents into the developer estate, and others are simply bundling them into their offerings: Microsoft is doing so across the productivity stack, and ServiceNow across enterprise operations, each with identity and procurement built in.

“Their pull is strongest exactly where monday.com wants to grow, in the largest accounts, where control-plane depth and administrative reach decide the deal,” said Gogia.

Actions for the near-term

Going forward, buyers should focus on operating risk, not headline risk, Moor’s Brue noted. In practice, that’s continuity of service, roadmap consistency, and strength of enterprise support. Productivity should be valued as better outcomes per unit of organizational effort, not mere activity.

“It should be a measure of how much smoother, faster, and more effective the operating model becomes when AI is built into the work,” said Brue.

Gogia noted that strain surfaces first in customer service, and monday.com’s attention is being redistributed. The company’s annual report disclosed that its focus is now concentrated on the largest accounts, with support for medium-sized clients moved to an AI-first and human-supported model.

During the first month of the transition, buyers should track named account continuity and escalation times, he advised. By the first quarter, keep an eye on whether credit governance and admin controls mature on schedule, and if the roadmap beyond the AI estate keeps pace. By the half-year mark, determine whether promised implementation depth is producing outcomes or “simply more billable engagement.”

Support tiers should be enumerated in writing before renewal, and buyers should contract for “side exits,” Gogia emphasized, with overage pricing fixed in advance, the right to pause consumption, and portability for workflows and agent configuration “if the relationship sours.” Finance should also insist on monthly consumption reporting by capability. Further, integration efforts, partner dependency, and change management should be considered first-class costs of the agent era, “not as afterthoughts to a license.”

“A license was a known cost,” said Gogia. “A meter is a behavior, and behavior is harder to forecast than headcount.”

This article originally appeared on CIO.com.

Healthy software companies typically don’t suddenly eliminate one-fifth of their workforce, but monday.com is doing just that as it bets on flatter teams, AI agents, and customer implementation expertise as the winning combination in the AI era.

Monday.com co-founder and co-CEO Eran Zinman today announced the “very difficult decision” to reduce the AI work platform company’s global workforce by about 20%, or 620 people.

The move has nothing to do with increasing margins or replacing humans with AI, he insisted in his post on LinkedIn; rather, it’s a calculated decision to trim down and hone the company’s focus as AI becomes integral to day-to-day workflows.

“This is not a distress signal; it is a deliberate reset, disclosed with its price attached,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “The industry has quietly swapped the meaning of productivity, and this filing is the clearest exhibit yet.”

A ‘significant opportunity’ in technology

In a SEC filing this week, monday.com said its restructuring plan reflects the “ongoing transformation of its product, marketing, and go-to-market strategy.” The move is intended to support a “leaner, more focused operating model” as the company continues to invest in its AI-driven strategy.

Zinman noted in his post that the company has shifted to “doing the work with AI and not just managing it,” and is focused on building environments where “people and AI agents [work] together in one workspace.”

In recent months, monday.com has evolved its products, strategy, and the way it serves its customers, and Zinman contended that “the organization we built for our previous chapter is not the organization that fits the new AI era.” Monday.com needs to “execute more decisively,” take on new challenges, and quickly respond to market changes, he said.

“We have never seen such a significant opportunity in software, driven by such exciting technology,” Zinman noted. He emphasized that the reduction is not to replace people with AI, nor to improve margins; the “vast majority” of savings will be reinvested into talent, products, and AI.

The restructuring will result in a “flatter organization” with fewer management layers and smaller, more autonomous teams, and monday.com also has a new go-to-market model, Zinman explained. Customers expect “deeper implementation support” as they deploy AI, and the company will work more closely with customers, increase its on-site presence, create new roles, and “adapt many existing ones.” In its SEC filing, the company said it expects to continue hiring in “key strategic areas” throughout 2026.

Workers will be expected to work better, “not harder,” Zinman noted. He pointed to several past examples where work could have been done in a few days, but instead took many months with “multiple meetings and endless friction.”

“This wasn’t people’s fault and everyone was frustrated by this,” he said. “Our new org changes ownership to allow people to make decisions and move fast.”

A spokesperson for monday.com declined to comment further on the staff reductions.

Monday’s key market advantages

Monday.com certainly isn’t struggling; the company expects 19% to 20% year-over-year growth in 2026.

“Companies in that position do not restructure because they must,” Greyhound’s Gogia noted. “They restructure because they have decided to become something else.”

Melody Brue, VP and principal analyst at Moor Insights & Strategy, pointed out that organizational redesign is important for real AI transformation, but while it can signal confidence to the market, it can still be “devastating” to humans.

While the company looks as though it’s trying to do right, that ultimately remains to be seen, she said. “There are often hidden internal bruises that can surface long after layoffs.”

Monday.com’s advantage is in its “structured substrate,” Gogia noted; its boards, permissions and typed workflows give agents something firmer to act on than just documents and chat history. The company highlights its natively built agents that can be configured by any team member, as well as connectors with Claude, Microsoft Copilot, and ChatGPT, and dedicated routes for external agents to authenticate and operate.

“For some time, the sharper enterprise question has been shifting from who has an agent to who owns the governed runtime in which an agent can safely act,” he said. “Structured work is a serious claim on that runtime.”

But parts of monday.com’s agent estate remain in staged release, and its product is ultimately “mid-transition,” Gogia pointed out; its agent builder carried a beta label as recently as March,. Also, the company’s pricing model changed in May to a hybrid model charging for seats as well as mandatory AI credits. And, while its AI-powered no-code builder monday vibe passed $1 million in annual recurring revenue within two and a half months, monday.com has not released subsequent outcomes, usage volumes, or attach rates.

Further, there’s an element of “gravity” with its competitors, he observed. Asana is reorganizing teams around agents, Atlassian is wiring agents into the developer estate, and others are simply bundling them into their offerings: Microsoft is doing so across the productivity stack, and ServiceNow across enterprise operations, each with identity and procurement built in.

“Their pull is strongest exactly where monday.com wants to grow, in the largest accounts, where control-plane depth and administrative reach decide the deal,” said Gogia.

Actions for the near-term

Going forward, buyers should focus on operating risk, not headline risk, Moor’s Brue noted. In practice, that’s continuity of service, roadmap consistency, and strength of enterprise support. Productivity should be valued as better outcomes per unit of organizational effort, not mere activity.

“It should be a measure of how much smoother, faster, and more effective the operating model becomes when AI is built into the work,” said Brue.

Gogia noted that strain surfaces first in customer service, and monday.com’s attention is being redistributed. The company’s annual report disclosed that its focus is now concentrated on the largest accounts, with support for medium-sized clients moved to an AI-first and human-supported model.

During the first month of the transition, buyers should track named account continuity and escalation times, he advised. By the first quarter, keep an eye on whether credit governance and admin controls mature on schedule, and if the roadmap beyond the AI estate keeps pace. By the half-year mark, determine whether promised implementation depth is producing outcomes or “simply more billable engagement.”

Support tiers should be enumerated in writing before renewal, and buyers should contract for “side exits,” Gogia emphasized, with overage pricing fixed in advance, the right to pause consumption, and portability for workflows and agent configuration “if the relationship sours.” Finance should also insist on monthly consumption reporting by capability. Further, integration efforts, partner dependency, and change management should be considered first-class costs of the agent era, “not as afterthoughts to a license.”

“A license was a known cost,” said Gogia. “A meter is a behavior, and behavior is harder to forecast than headcount.”

Geht es um die richtige Notiz-App, fällt die Entscheidung im Regelfall zwischen OneNote von Microsoft und Evernote, das inzwischen dem italienischen Softwareunternehmen Bending Spoons gehört.

• OneNote ist seit dem Jahr 2003 auf dem Markt und wurde 2007 in Microsofts Office-Suite aufgenommen (die meisten Versionen laufen inzwischen unter dem Namen Microsoft 365). Zudem wird OneNote auch mit Windows 10 und 11 gebündelt und als eigenständiges Produkt kostenlos angeboten. Das eröffnet eine potenzielle Nutzerbasis von rund einer Milliarde Systemen.

• Evernote feierte im Jahr 2008 sein Marktdebüt und erfreut sich seither stetig wachsender Nutzerzahlen. Unternehmensangaben zufolge sind es inzwischen weltweit rund 225 Millionen User.

Sowohl OneNote als auch Evernote sind für alle wichtigen Desktop- und Mobile-Betriebssysteme verfügbar. Beide sind in der Lage, Notizen mit allen Geräten und dem Internet zu synchronisieren und beide versprechen, die einzige notwendige App für Notizen zu sein. Stellt sich die Frage: Was ist die bessere Wahl für Business-Nutzer?

Wir haben uns die jeweils aktuellen Versionen beider Notiz-Apps für Windows, macOS, iPadOS, iOS und Android angesehen und sagen Ihnen, worin sich OneNote und Evernote im Wesentlichen unterscheiden.

One Note: Organisationskrösus

OneNote ist eine vollwertige Anwendung. Mit ihr können Sie einfache oder komplexe Notizen von Grund auf neu erstellen, sie in durchsuchbaren Notizbüchern organisieren und mit einer Vielzahl von Plattformen synchronisieren – zum Beispiel Windows-PCs, Macs, iPads und iPhones sowie Android-Geräten.

Außerdem strotzt Microsofts Notiz-App nur so vor Werkzeugen – beispielsweise um:

• Notizen zu erstellen und zu bearbeiten,

• zu zeichnen,

• Audio- und Videoaufnahmen zu erstellen,

• Bilder zu scannen oder

• Tabellenkalkulationen einzubetten,

OneNote wird allmählich immer stärker in die Microsoft-365-Suite integriert. Für Unternehmen ist die Live-Zusammenarbeit besonders wichtig und sie funktioniert hier ebenso so wie in anderen Microsoft-365-Anwendungen. Besonders gut lässt sich OneNote dabei in Microsoft Teams integrieren: Notizbücher lassen sich Teams hinzufügen – jeder innerhalb des Kanals kann diese anschließend anzeigen und bearbeiten (entsprechende Zugriffsrechte vorausgesetzt).

Web-Clipping

So gut OneNote auch sein mag, wenn es darum geht Notizen zu erstellen: Es bleibt hinter den beträchtlichen Möglichkeiten von Evernote zurück, wenn es um das Clipping von Webinhalten geht. Dazu bietet OneNote ein Browser-Addon (Microsoft Edge, Google Chrome und Mozilla Firefox) namens OneNote Web Clipper. Mit diesem Tool können Sie Screenshots in OneNote speichern – wenn es denn so funktioniert wie es soll.

Versionsunterschiede

Die Windows-Version von OneNote kann mit der vollen Bandbreite an Tools glänzen. Im Vergleich bietet Evernote nicht annähernd so viele ausgefeilte Tools. Die Versionen für Web, Android, iPadOS und macOS fallen hingegen ab. Sie sehen zwar ähnlich aus wie die Windows-App, bieten im Vergleich aber weniger und teilweise eingeschränkte Funktionen. So können Sie hier etwa keine Tabellenkalkulationen integrieren oder Videoinhalte aufzeichnen. Die iOS-Version hebt sich hingegen mit einer schlanken Benutzeroberfläche ab, die darauf optimiert ist, schnell Notizen zu erstellen oder zu bearbeiten.

KI-Funktionen

Seit Mitte Januar 2024 bietet Microsoft mit Copilot Pro KI-Integration im Abomodell an – auch für OneNote.

🧠 Work smarter with Copilot Notebooks in OneNote — your new AI-powered space to think, organize, and create. Bring all your content together and let Copilot help you get to insights, faster.

Learn more: https://t.co/c63JbghHcH

— Microsoft OneNote (@msonenote) July 3, 2025

Storage und Preise

OneNote synchronisiert seine Inhalte mit all Ihren Geräten und mit dem Internet über Microsoft OneDrive oder SharePoint. OneNote ist in Microsoft 365 enthalten. Ohne M365-Abo erhalten Sie bis zu 5 GB Cloud-Speicherplatz. Zusätzlichen Speicherplatz können Sie zukaufen. Wenn Sie (oder Ihr Unternehmen) ein Microsoft-365-Abonnement abschließen, erhalten Sie wesentlich mehr Speicherplatz: zwischen 100 GB und 6 TB – je nachdem, für welchen Plan Sie sich entscheiden.

Evernote: Web-Clipping-King

Evernote ist ein ganz anderes Kaliber als OneNote. Es bietet zwar die gleichen Grundfunktionalitäten: die Möglichkeit, Notizen zu erstellen, zu organisieren und mit mehreren Plattformen (Windows, macOS, iPadOS, iOS, Android, Web) zu synchronisieren. Dabei bietet die App im Vergleich zu OneNote allerdings eine signifikant farbenfrohere und einladendere Benutzeroberfläche – und eignet sich hervorragend, um Web-Content auszuschneiden und abzuspeichern.

Der Startbildschirm von Evernote bietet diverse Widgets für den schnellen Zugriff auf Notizen, Notizbücher, kürzlich aufgenommene Bilder und vieles mehr. Ein zusätzliches Suchfeld garantiert zudem, dass Sie immer finden wonach Sie suchen. Die Widgets auf Ihrem Startbildschirm können Sie ganz nach Ihren Wünschen hinzufügen oder entfernen.

Evernote bietet allerdings nicht annähernd so viele Werkzeuge zur Erstellung von Notizen wie OneNote. Zu den verfügbaren Features gehören zum Beispiel:

• Texte erstellen, bearbeiten und formatieren,

• Tabellen, Dateien und Bilder einbetten,

• Unterstützung für Touch-Devices und -Stifte,

• Integrierbare Audio- und Videoaufnahmen und

• Integration mit Google Calendar (für Outlook geplant) und Google Drive;

Web Clipping

Die Stärke von Evernote liegt wie eingangs bereits erwähnt darin, Inhalte aus dem weltweiten Netz zu erfassen und zu organisieren. Das dazu integrierte Web-Clipping-Tool funktioniert beispielhaft und läuft als Browser-Addon (für Chrome, Firefox, Internet Explorer, Microsoft Edge, Safari und Opera). Die Funktionen variieren dabei je nach Browser leicht. Im Allgemeinen bieten Google Chrome und Microsoft Edge dabei das zuverlässigste Erlebnis.

Die Inhalte, die Sie mit Evernote aus dem Netz ziehen, lassen sich auch mit Tags versehen und zu Notizen hinzufügen. Darüber hinaus verfügt der Clipper auch über nützliche Markierungswerkzeuge für Screenshots. Erstellte Notizen dürfen selbstverständlich auf Knopfdruck mit spezifischen Kollegen oder auch über soziale Medien geteilt werden.

Versionsunterschiede

Die Windows-, Mac-, iPadOS-, iOS-, Android– und Web-Versionen von Evernote weisen alle ein ähnliches Erscheinungsbild auf und verfügen über die gleichen Widgets und dasselbe Layout.

KI-Funktionen

Auch Evernote integriert inzwischen künstliche Intelligenz. Zum Beispiel in Form von KI-Transkriptionen, semantischen Suchen und auch in Form eines Assistenten:

You’ve heard about Evernote v11, now see it in action. ⚡📹

Watch Product Lead @fedesimio demo the new AI Assistant, Semantic Search, and AI Meeting Notes, and share the story of how Evernote got to v11.

We’re excited to start this new chapter together. V11 is available for… pic.twitter.com/9d8DMmRVLF

— Evernote (@evernote) January 27, 2026

Storage und Preisgefüge

Die Basisversion von Evernote ist kostenlos, jedoch auf 60 MB neue Notizen pro Monat sowie die Synchronisierung zwischen zwei Geräten limitiert – und enthält keine erweiterten Funktionen.

Abonnement-Pläne erschließen weitere Features, beispielsweise die Möglichkeit, Notizen in Präsentationen umzuwandeln, PDFs und Anhänge zu durchsuchen und die Integration mit weiteren Services wie Slack und Microsoft Teams.

• Das Starter-Abo richtet sich dabei an Einzel- beziehungsweise Privatpersonen und kostet 6,65 Euro pro Monat (oder 79,90 Euro pro Jahr).

• Das Advanced-Abo ist auf Power User ausgelegt und kostet 16,66 Euro pro Monat (oder 199,99 Euro jährlich).

• Enterprise-Pläne werden hingegen individuell auf das jeweilige Unternehmen zugeschnitten und bieten unter anderem gemeinsame Arbeitsbereiche, dedizierten Support und zentrale Management-Tools.

OneNote oder Evernote?

Wenn Sie in erster Linie ein Tool suchen, mit dem Sie auf einfache Weise Inhalte aus dem Internet erfassen, organisieren und finden können, sollten Sie zu Evernote greifen. Wollen Sie einfach nur Notizen erstellen und diese bestmöglich organisieren – oder nutzen bereits Microsoft 365 – dann dürften Sie mit OneNote glücklich werden. Oder Sie verwenden beide Apps einfach in Kombination. (fm)

Dieser Beitrag ist im Original bei unserer Schwesterpublikation Computerworld.com erschienen.

This is Part 2 of a series on tech worker unionization. See Part 1: “A brewing battle: More IT workers want unions. The industry doesn’t.”

The best time for tech workers to unionize was 20 years ago, when they had plenty of leverage. The second-best time is now, when they don’t.

Mass layoffs, AI-driven displacement, corporate surveillance, workplace disillusionment have created conditions that have made organizing compelling for tech professionals. But the federal labor board that has historically protected workers’ right to organize has been weakened, and the companies that once feared it are openly defying it.

Here’s how organizers and labor experts describe the pros and cons to organizing — and how you can get started.

What unions can — and can’t — do for you

The single biggest benefit of a union contract for most tech workers isn’t pay — it’s protection against arbitrary termination, especially in the wake of recent mass layoffs in tech. In the United States, nonunion “at-will” workers can be fired at any time without a stated reason, while unionized workers negotiate protections written into their contracts.

“That fear of the company letting you go for anything at any time…with a union they just can’t do that,” says Zak Thompson, a senior software engineer at Kickstarter and union steward at Kickstarter United. Now that Kickstarter employees are unionized, people are less worried that saying something negative will result in termination.

“I’ve been shocked at the willingness of my co-workers to speak up against what they see as poor or controversial business decisions,” Thompson says.

Beyond job security, unions can deliver concrete material gains. Kickstarter United was formed in 2020, although getting there wasn’t easy: two employees were fired during the organizing campaign — which itself became a galvanizing event. And while the union hasn’t been able to prevent layoffs, it did negotiate better terms: four months of severance pay and four to six months of continued health insurance, versus the two to three weeks per year of work that management had initially proposed.

Other benefits include a four-day work week; AI protections; a minimum pay floor; and standards for raises, promotions, and time off for the company’s 59 employees.

Unions can give tech workers a voice in decisions that affect their daily work — including how AI tools are deployed. “Nobody I’ve spoken to is against new technology or getting trained in it,” says Max Belasco, a business systems analyst at the University of California Los Angeles School of Law and co-chair of the UCLA chapter of the University Professional and Technical Employees/Communications Workers of America (UPTE-CWA) Local 9119.

“But when new technology is being implemented, we want to know: what’s the five-year vision, the 10-year vision? Are we implementing this in a way that betters staffing, increases efficiency, or eases the lives of people already working? Or are we trying to take away jobs, automate people out of their pension or paycheck?” Belasco says.

The challenges are real. Tech professionals are less inclined to leave their jobs in the current market because wages haven’t been increasing as fast as they once were, and it can take longer to land another job.

“Tech moved from a very tight labor market in 2022 (1.85% unemployment rate) to a noticeably weaker one in 2024–2026 (3.49%),” although that’s still better than the national unemployment rate of 4.36% through May of this year, says Liya Palagashvili, senior research fellow and director of the Labor Policy Project at the Mercatus Center at George Mason University.

Flexibility is a concern. The more substantive challenge, raised by economists including Palagashvili, is that traditional union contracts impose uniform terms across an entire bargaining unit, limiting the flexibility that many tech workers — and their employers —currently enjoy. Tech firms need to move fast, adjusting teams, products, and roles on the fly.

“Collective bargaining agreements can make those adjustments much more difficult, whether by making them slower, costlier, or inconsistent with the contract,” she says.

Workers skeptical of unions in a survey of 1,900 tech professionals conducted by the career site Blind cited specific concerns: that unions are “not meritocratic,” “prevent innovation,” and “hold back earnings of top performers.”

Thompson from Kickstarter United pushes back: “We have nothing in our contract about ‘you can’t bend down and pick up a piece of trash because that’s someone else’s job.’ The company is free to give bonuses and individual raises as much as they like. This is all just up to the people who are bargaining the contract from the union side.”

Organizing carries potentially serious personal risks. During negotiations for a second three-year contract in 2025, Kickstarter United went on strike for 42 days. A few months later, the company announced layoffs.

“They let go strong union leaders, including a person who had bargained our last contract,” Thompson says. The union appealed, and the issue is now going to arbitration.

If you form a union, don’t expect much support from the National Labor Relations Board, the agency that certifies US labor unions and protects workers’ right to organize, in terms of prosecuting complaints of unfair labor practices, Thompson warns. “We’re in a political moment in this country with a pretty weakened NLRB. You have to be ready to organize and withhold worker power without any guarantee of safety.”

Organizers are up against an enormous union avoidance industry. Organizers can expect fierce pushback as soon as the business discovers that organizing is underway.

“There’s a multi-billion-dollar industry in union avoidance,” says Alan McAvinney, a Google software engineer and organizing chair, Alphabet Workers Union-CWA, a 1,400-member minority union of Alphabet employees. (Google is a subsidiary of Alphabet.)

US employers spend roughly $1.7 billion a year on union avoidance consultants and law firms, according to a May 2026 report by the Economic Policy Institute and LaborLab.

Expect hardball tactics. Management may play hardball during the time between when organizers announce their intention to unionize and the actual vote. For example, management can threaten to fire foreign-born workers in the US on H-1B visas if they support the union. Those workers would then have just 60 days to find a new sponsoring employer or lose their H-1B status, according to a recent Tech Workers Coalition blog post.

And at venture capital-backed startups, investment agreements sometimes require management to attest there is no union activity — meaning a public organizing drive can trigger funding withdrawal. Or, if a unionized company is acquired, the new management can dissolve the union overnight by reclassifying unionized workers as new hires.

With these sobering facts in mind, here is how organizers who have done it describe the process of creating a union.

Step 1: Start a conversation with your co-workers

At the University of California, a two-tier system had evolved where some tech workers were unionized and some weren’t, Belasco says. Management created new titles that fell outside the union even though they had similar job descriptions and responsibilities to those in the union. Those nonunion employees received lower pay and benefits than their unionized peers, which created resentment and instability.

Belasco and other organizers wanted to eliminate that division by bringing everyone under the same contract. But when they began their unionization drive, “the biggest barrier we faced wasn’t management opposition — it was that people felt this was just the best-case scenario realistically available: ‘We have this job at the university, we have concerns about automation and layoffs, but what can we really do about it?'” he says.

The antidote to that fatalism, organizers say, is simple: “Just start talking to your immediate co-workers. Are they experiencing the same challenges you are experiencing?” says McAvinney. “There’s no need to start talking about a union at this point.”

Just get a consensus and start building a group of like-minded individuals, Thompson advises. “Always start with one-on-one conversations, and that’s what you should do the whole time. That’s the key to organizing,” he says.

Tech workers often think they’re a special case, says Thompson, and therefore that unionization isn’t a good fit. “You’re not special. You are a company of workers, you are organizing, and there is a playbook for that. Trust the process, because it tends to work pretty well,” he says.

Step 2: Who’s on board, and who’s not? Map your workplace, but keep it quiet

Once there’s a consensus, continue to grow your network. Keep a list of everyone you’ve spoken with and note their disposition: “Is this person union-friendly or anti-union? Would they be a strong organizer?” Thompson says.

Maintaining secrecy early on is essential, because anti-union tactics will start immediately, and that can stop union organizing before it can gain momentum.

“Generally, employers do not want to share power with their workforce,” McAvinney says. Employers will deploy every means at their disposal to stop organizing efforts and peel away potential yes votes.

“If you look at historical examples, having 70% approval before the employer finds out about you results in a high percentage of wins when you actually cast the vote. Historically, that’s an effective buffer,” he says.

There’s a real threat of firing and layoffs. The traditional tech worker belief that job mobility makes collective action unnecessary is now being tested by a tighter job market, McAvinney says, noting that workers who many believe were fired for speaking out back in 2019 were a galvanizing factor in his union’s formation.

“You generally don’t want to be in a situation where the employer feels comfortable firing everyone. Part of that is thinking from a cynical standpoint about what the consequences would be to the employer if they did fire everyone,” he says.

One-on-one conversations that include personally asking co-workers to keep conversations confidential are key to keeping things quiet, Belasco says. When 2,100 UC tech workers voted to unionize in May, 96% voted in favor. To stay out of earshot of managers, avoid employee surveillance tools, and sidestep conference calls that could be recorded, organizers met with workers in their homes.

“That tactic is probably what made the difference between winning the election and getting the majority we got,” he says.

Step 3: Find the right union affiliation or go it alone

“Running a campaign against major employers requires the resources and expertise of the larger labor movement, even if workers publicly present as independent,” says Kate Bronfenbrenner, director of labor education research and senior lecturer emeritus at Cornell University’s School of Industrial and Labor Relations.

Options include the Communications Workers of America (CWA), Service Employees International Union (SEIU), and the Office and Professional Employees International Union (OPEIU), among others. Another resource, the Tech Workers Coalition (TWC), provides training on organizing tactics, AI-in-workplace issues, and contract negotiation, and can match workers to the right unions for their needs.

The Alphabet Workers Union decided early on to affiliate with CWA. “They gave us a bunch of support early on in our campaign with no strings attached,” McAvinney says.

Kickstarter is organized through OPEIU, Thompson says. “They’ll usually have resources and staff that can help you through the next steps: collecting signatures in support of a union, bringing that to management, holding a vote — the more formalized things that interact with US labor law. They’ll also help with organizing along the way,” he says.

For workers at institutions where a union already exists, there may be a faster path. Organizers at UCLA did what’s called a “unit modification,” aligning with UPTE. By organizing under UPTE, the workers didn’t have to negotiate a new contract from scratch — they joined an already-negotiated contract covering existing UPTE tech members, which put them in “a much stronger position” than starting fresh, Belasco says.

Step 4: Choose your union model: majority vs. pre-majority or minority

Assess what’s practical for your organizing effort. In a majority union, more than 50% of all workers in a defined bargaining unit must vote to join the union through an NLRB-supervised election in the private sector, or a Public Employment Relations Board (PERB)-supervised election for public sector workers.

The NLRB must certify the union, which then operates under its legal protections. This means, for example, that the employer must bargain, negotiated contracts are enforceable, violations must go to the NLRB or arbitration, and workers can’t be dismissed without just cause.

A pre-majority or minority union is a minority labor organization operating without NLRB protections or collective bargaining agreements. “Pre-majority means that workers are able to demonstrate majority support — through signed cards, petitions, a walkout, or everyone wearing solidarity T-shirts — without going through a formal election,” Bronfenbrenner says.

The Alphabet Workers Union-CWA (AWU-CWA) formed as a pre-majority union because achieving majority status across a globally distributed workforce of over 100,000 was not a realistic near-term goal. “An underground model where you try to reach 70% support across a workforce of over 100,000 people isn’t realistic,” McAvinney says.

A pre-majority union can still make a difference, he says. For example, the Alphabet Workers Union-CWA convinced management to offer voluntary exit packages — buyouts — prior to announcing layoffs.

For smaller organizations, a majority union may be the more practical option — it’s more attainable, McAvinney says. “I don’t think [the pre-majority union model] is the correct thing to do in all situations. I certainly would not recommend it to a 200-person shop.”

Kickstarter, which had fewer than 100 employees, was able to form a majority union, with 55% voting to organize.

Ultimately, says McAvinney, “there’s no inflection point where you go from being able to win nothing to winning everything, even with a contract and a supermajority. But the more people you have who are willing and able to fight for what they want, the more you’ll be able to get.”

Step 5: Who should — and should not — be in your union?

Belasco’s situation at UCLA illustrates a broader strategic choice that every organizing campaign must make. He had been in a union position in educational technology when he was told his role would be reclassified as a non-union position.

“I was given a choice: apply to the new non-union position to continue doing the work I’d trained for, or stay in my union position doing service desk work I wasn’t used to,” he says. “Essentially, it was a choice between job security and career progression.”

Belasco joined a “wall-to-wall” union, which represents a broad range of university professional and technical employees across the UC system rather than a single job category, such as engineers or tech professionals.

Kickstarter United is another example of a wall-to-wall union. “It’s not just the engineers who are unionized, but also customer support, designers — everyone,” Thompson says.

Wall-to-wall unions are more powerful, but they’re also more difficult to achieve. Under US labor law, “professionals have to vote separately on whether they want to be combined with other workers,” says Bronfenbrenner. “You can never have a wall-to-wall unit without giving professionals the chance to decide whether they want to be separate.”

The law’s “professional employees” category includes roles like software engineers and developers but not necessarily others. For example, customer support specialists and QA analysts would fall into the “non-professional workers” category.

“For decades, the pattern was either to organize everybody except the engineers, or manage to organize the engineers and fail to bring in everybody else — neither of which builds real worker power,” says Simone Robutti, an organizer with Tech Workers Coalition Global, an international branch of TWC based in Berlin.

Step 6: You won the vote. Get ready for what comes next

Winning a union vote means having a seat at the table, says Thompson. “Once the workers have come together and agreed they want that seat, you bring that to management, and they have a chance to voluntarily recognize a union,” he says.

But in most cases employers contest the results, which must be certified by the NLRB or PERB. That process, in which the employer uses various tactics to challenge the legitimacy of the outcome, can take weeks or months.

Unfortunately, the legal framework that is supposed to protect workers during this process has been significantly weakened in the last few years. In a potentially more ominous development, SpaceX, Amazon, Trader Joe’s, Starbucks, and the University of Southern California have in separate legal actions challenged the constitutionality of the NLRB, arguing that the agency’s structure violates the separation of powers. The Fifth Circuit Court of Appeals upheld injunctions against the NLRB in SpaceX’s case in August 2025 — a serious challenge to the agency’s authority.

In the meantime, some companies may disregard negotiated contracts, which can lead to lengthy legal appeals or extended arbitration.

“The NLRB can still force an election, but it can’t force a contract, and companies are saying they simply won’t comply,” Bronfenbrenner says. This is where the expertise and resources of affiliation with a major union can help, she adds.

As a result, contract negotiations can take far longer than workers might expect. At Kickstarter, for example, two years and four months elapsed from the time of the union vote to the first contract, and that was at a 59-person company with a relatively cooperative employer. At larger companies with more aggressive legal teams, the timeline will be longer.

Forming a union is hard work, Robutti says. “It’s not a service you pay for and they protect you. It doesn’t happen spontaneously, and it doesn’t happen magically. It’s the choice to take responsibility for improving your workplace.”

If you’re a software developer or architect, you might wonder if programming certifications are still worth the effort, especially in the era of rapid AI-driven evolution. The short answer is, it depends.

“Certifications are shifting from a checkbox to a compass. They’re less about proving you memorized syntax and more about proving you can architect systems, instruct AI coding assistants, and solve problems end-to-end,” says Faizel Khan, lead AI engineer at Landing Point, an executive search and recruiting firm.

“In the AI era, fewer students will get trained on the job, which means they have to train themselves,” Khan says. “Certifications—especially architectural ones like AWS, Kubernetes, Terraform—are still the clearest path to do that.”

Pros and cons of programming certifications

It’s not all black and white when it comes to deciding whether to pursue programming certifications. The effort involves both pros and cons.

“In terms of pros, certifications concretely demonstrate that you have a skillset at a documented level,” says Chris Riccio, vice president of engineering at Uplevel, an engineering optimization system provider. “They also show that you’ve put in the time and effort to learn, study, and prepare.”

Programming certifications are “a useful way to validate foundational skills and show that someone understands core concepts,” says Greg Fuller, vice president of Skillsoft’s training provider, Codecademy. “They’re especially helpful for people entering the field or shifting from adjacent roles.”

Certifications offer a structured path to demonstrate proficiency, and they can confirm your ability to build and deploy in various environments, Fuller says.

These types of certifications often demonstrate baseline proficiency and continuous learning, says Reshmi Ramachandran, head of partnerships and GTM strategy for Cprime, a consultancy. “These are often key indications of proficiency for companies looking to filter large candidate pools,” she says.

Certifications really do two things, Khan adds. “First, they force you to learn by doing,” he says. “If you’re taking AWS Solutions Architect or Terraform, you don’t pass by guessing—you plan, build, and test systems. That practice matters. Second, they act as a public signal. Think of it like a micro-degree. You’re not just saying, ‘I know cloud.’ You’re showing you’ve crossed a bar that thousands of other engineers recognize.”

But there are cons, too. “In tech, employers don’t just want credentials, they want proof you can deliver,” says Kevin Miller, CTO at IFS, a maker of factory automation software. “Programming certifications can be a valuable indicator of your baseline knowledge and competencies, especially if you’re early in your career or pivoting into tech, but their importance is dwindling.”

AI tools that can generate, debug, and optimize code are already performing tasks once done by entry-level developers, “which means fewer traditional programming roles are available,” Miller says. “As a result, the job market is becoming more competitive, and certifications aren’t seen as the noteworthy achievement they once were.”

What’s more, not all certifications carry the same weight, Riccio says. “Some may reflect only familiarity rather than true expertise,” he says. “Certifications also often measure ‘book knowledge’ rather than practical experience, and they don’t always map clearly to the requirements of a specific role.”

Programming certifications “can be a helpful signal, especially for confirming baseline knowledge in areas like cloud, security, or devops, but they’re not the full picture,” says Morgan Watts, vice president of IT at 8×8, a contact center platform developer.

“I’m more interested in a candidate’s attitude and aptitude: what problems they’ve solved, what they’ve built, and how they’ve approached challenges,” Watts says. “Certifications can show commitment and discipline, and they’re especially useful in highly specialized roles. But I’m cautious when someone presents a laundry list of certifications with little evidence of real-world application.”

A certification without experience doesn’t carry much weight, Watts says, and over-certification can sometimes signal the wrong focus. “Ultimately, it’s the ability to apply knowledge, collaborate, and adapt that sets great developers apart,” he says.

Finally, certifications can age fast, Khan says. “Tech stacks evolve and a badge from two years ago may already feel dusty,” he says. “And some certifications are paper-thin—multiple-choice exams that don’t prove you can debug production at 2 a.m. So, the risk is you collect badges but still can’t ship.”

Which certifications will get you noticed?

Despite the drawbacks, certifications are still very much in demand, and some carry more weight than others.

The most in-demand certifications are typically platform-based—Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, and others, Riccio says. “Many of these platforms provide managed services that integrate with existing systems or serve as the glue between them,” he says. “Today’s engineering teams aren’t just building standalone systems in isolation; they’re using other systems to store data, orchestrate business workflows, and connect applications.”

A certification that demonstrates the ability to build solutions on these platforms can put a development professional ahead of the competition, Riccio says.

“The certifications I see in highest demand tend to reflect the evolving tech landscape,” Watts says. “Cloud certifications from AWS, Azure, and GCP are incredibly valuable, especially as distributed systems become the norm.”

Also in demand are certifications for devops and CI/CD tools including Kubernetes, Docker, and Jenkins, Watts says, “because deployment automation and reliability are critical at scale. Also, with AI reshaping development, we’re seeing growing interest in certifications around machine learning, data science, and AI model integration. These certifications stand out because they align directly with the skills that teams need to move faster and more intelligently.”

On the AI front, certifications in TensorFlow and other machine learning platforms are gaining traction as organizations look to embed AI across the development process, Watts says. “These are the certifications that align closely with where modern engineering is headed—scalable, secure, and AI-enabled,” he says.

And then there are cybersecurity credentials that continue to be in high demand. Security certifications, such as CompTIA Security+ or Certified Ethical Hacker, “have become essential as every company faces increasing cyber threats and compliance requirements,” Miller says.

“Core programming certifications are still a bit niche, but the adjacent skills, like those that help developers deploy, secure, and scale their code, are driving demand,” Fuller says. “Companies want developers who understand the full lifecycle, not just how to write code.”

Also see: The best Java certifications for software developers.

Certifications in the hiring process

Experts are clear that programming certifications alone will not get you the job. But they do play a role in the hiring process.

“The information technology world is characterized by rapid and continuous evolution, including the skills and knowledge required to work in the field,” says Diane Rafferty, managing director of the National Technology Group at Atrium, a global talent solutions and extended workforce management firm.

“Certifications not only prove that you have the skills and knowledge needed, but they also show employers that you’re invested in your education and career growth,” Rafferty says. “They can give you a competitive edge when looking for a job, as many companies now require candidates to have them.”

Certifications are one part of the hiring equation, “but never the only part,” Watts says. “They help validate that a candidate has taken the time to build foundational knowledge, and that’s a good sign. But I put more weight on how a person thinks, solves problems, and contributes to the team. I look for people who are curious and proactive, who are learning because they want to, not just because a course told them to.”

Certifications can also play a valuable role in retention, Watts says. “I encourage team members to pursue growth, and when they invest in their own development, the whole organization benefits,” he says. “But again, it’s that balance of knowledge, attitude, and applied experience that really moves the needle.”

Certifications “may allow you to breeze through the initial résumé screening process, potentially getting you to the next stage faster,” Riccio says. “At a minimum, they will set your profile apart from the rest of the pack. They also demonstrate that you’ve reached a baseline level of expertise, allowing hiring managers to quickly evaluate whether you have the skills for the role.”

Employers today “care far less about whether someone has passed an exam and far more about whether they can apply knowledge effectively in real-world situations, leverage AI tools, and solve complex problems,” Miller says. “A certification might get someone an interview, but being able to demonstrate problem-solving skills, teamwork, and adaptability will really make them stand out.”

Popular programming certifications

The following certifications consistently rose to the top in my conversations with tech leaders and hiring managers.

AWS Certified Developer—Associate

Showcases skills and knowledge in developing, optimizing, packaging, and deploying applications, using CI/CD workflows, and identifying and resolving application issues, according to AWS. This certification is said to be a good starting point on the AWS certification journey for professionals in IT or cloud developer job roles.

Azure Developer Associate

This certificate from Microsoft is intended for developers participating in all phases of cloud development, including design, deployment, maintenance, and monitoring. The course teaches developers how to create end-to-end solutions in Microsoft Azure, using the Microsoft Learn Sandbox environment to access Azure resources and services.

Certified Kubernetes Application Developer (CKAD)

This certification was created by the Linux Foundation and Cloud Native Computing Foundation. It demonstrates that candidates can design, build, and deploy cloud-native applications for Kubernetes.

Certified Secure Software Lifecycle Professional (CSSLP)

This certification, from ISC2, focuses on secure software development practices. It recognizes leading application security skills and demonstrates advanced technical skills and knowledge needed for authentication, authorization, and auditing throughout the software development lifecycle.

Databricks Certified Machine Learning Professional

Professionals learn about the latest data and AI techniques and how they can use the Databricks Data Intelligence Platform to build a variety of solutions across data engineering, data warehousing, data science, and AI.

Professional Cloud Architect

This certification from Google assesses the ability to design and plan a cloud solution architecture, manage and provision the cloud solution infrastructure, design for security and compliance, analyze and optimize technical and business processes manage implementations of cloud architecture, and ensure solution and operations reliability.

Terraform Associate

This certification from HashiCorp is for cloud engineers specializing in operations, IT, or development who know the basic concepts and skills associated with Terraform. It validates foundational skills in using Terraform for infrastructure as code development.

A portmanteau of “development” and “operations,” devops emerged as a way of bringing together two previously separate groups responsible for the building and deploying of software.

In the old world, developers (devs) typically wrote code before throwing it over to the system administrators (operations, or ops) to deploy and integrate that code. But as the industry shifted towards agile development and cloud-native computing, many organizations reoriented around modern, cloud-native practices in the pursuit of faster, better releases.

This required a new way to perform these key functions in a more streamlined, efficient, and cohesive way, one where the old frustrations of disconnected dev and ops functions would be eliminated. With two groups working together, developers can rapidly roll out small code enhancements via continuous integration and delivery rather than spending years on “big bang” product releases.

Devops was born at cloud-native companies like Facebook, Netflix, Spotify, and Amazon; but it’s become one of the defining technology industry trends of the past decade, primarily because it bridges so many of the changes that have shaped modern software development.

As agile development and cloud-native computing have become ubiquitous, devops has enabled the entire industry to speed up its software development cycles. Thus, devops has now thoroughly infiltrated the enterprise, especially in organizations that rely on software to run their business, such as banks, airlines, and retailers. And it’s spawned a host of other “ops” practices, some of which we’ll touch on here.[JF1] 

Devops practices

Devops requires a shift in mindset from both sides of the dev and ops divide. Development teams should focus on learning and adopting agile processes, standardizing platforms, and helping drive operational efficiencies. Operations teams must now focus on improving stability and velocity, while also reducing costs by working hand in hand with the developer team.

Broadly speaking, these teams need to all speak a common language and there needs to be a shared goal and understanding of each other’s key skills for devops to thrive.

More specifically, engineers Damon Edwards and John Willis created the CALMS model to bring together what are commonly understood to be the key principles of devops:

• Culture: One that embraces agile methodologies and is open to change, constant improvement, and accountability for the end-to-end quality of software.
• Automation: Automating away toil is a key goal for any devops team.
• Lean: Ensuring the smooth flow of software through key steps as quickly as possible.
• Measurement: You can’t improve what you don’t measure. Devops pushes for a culture of constant measurement and feedback that can be used to improve and pivot as required, on the fly.
• Sharing: Knowledge sharing across an organization is a key tenet of devops.

“Who could go back to the old way of trying to figure out how to get your laptop environment looking the same as the production environment? All these things make it so clear that there’s a better way to work. I think it’s very tough to turn back once you’ve done things like continuous integration, like continuous delivery. Once you’ve experienced it, it’s really tough to go back to the old way of doing things,” Kim told InfoWorld.

What is a devops engineer?

Naturally, the emergence of devops has spawned a whole new set of job titles, most prominent of which is the catch-all devops engineer.

Generally speaking, this role is the natural evolution of the system administrator — but in a world where developers and ops work in close tandem to deliver better software. This person should have a blend of programming and system administrator skills so that he or she can effectively bridge those two sides of the team.

That bridging of the two sides requires strong social skills more than technical. As Kim put it, “one of the most important skills, abilities, traits needed in these pioneering rebellions — using devops to overthrow the ancient powerful order, who are very happy to do things the way they have for 30 to 40 years — are the cross-functional skills to be able to reach across the table to their business counterparts and help solve problems.”

This person, or team of people, will also have to be a born optimizer, tasked with continually improving the speed and quality of software delivery from the team, be that through better practices, removing bottlenecks, or applying automation to smooth out software delivery.

The good news is that these skills are valuable to the enterprise. Salaries for this set of job titles have risen steadily over the years, with 95% of devops practitioners making more than $75,000 a year in salary in 2020 in the United States. In Europe and the UK, where salaries are lower across the board, 71% made more than $50,000 a year in 2020, up from 67% in 2019.

Key devops tools

While devops is at its heart a cultural shift, a set of tools has emerged to help organizations adopt devops practices.

This stack typically includes infrastructure as code, configuration management, collaboration, version control, continuous integration and delivery (CI/CD), deployment automation, testing, and monitoring tools.

Here are some of the tools/categories that are increasingly relevant in 2025, and what is changing:

• CI/CD and delivery automation: Traditional tools like Jenkins remain in many stacks, but newer orchestration tools and CLI-driven or GitOps-centric platforms are growing in importance (e.g. ArgoCD, Flux, Tekton). Also, platforms that integrate more tightly with monitoring, secrets management, drift detection, and policy enforcement are gaining traction.
• Security, compliance, and devsecops tooling: Security tools are increasingly integrated into devops pipelines. Expect to see more use of static analysis (SAST), dynamic testing (DAST), dependency and supply chain scanning (SCA), secret management, and policy as code. The push is toward embedding security earlier and bridging gaps between dev, security, and machine learning teams. (InfoWorld:)
• AI  and automation augmentation: AI-assisted tools are increasingly part of tooling stacks: auto-suggestions in CI/CD, anomaly detection, predictive scaling, intelligent test suite selection, and more. The hope is that these tools will reduce manual interventions and improve reliability. Tools that are “AI ready”—that is, they integrate well with AI or have mature built-in automation or assistance—increasingly stand out from the pack.

Devops challenges

Even as devops becomes more widely adopted, there remain real obstacles that can slow progress or limit impact. One major challenge is the persistent skills gap. The modern devops engineer (or team) is expected to master not just source control, CI/CD, and scripting, but also cloud architecture, infrastructure as code, security best practices, observability, and strong cross-team communication. In many organizations these capabilities are uneven: some teams excel, others lag behind. A 2024 survey showed that while 83% of developers report participating in devops activities, using multiple CI/CD tools was correlated with worse performance — a sign that complexity without deep expertise can backfire.

Toolchain fragmentation and complexity is a related issue. Devops toolchains have sprouted into a sometimes bewildering array of packages and techniques to master: version control, CI build/test, security scanning, artifact management, monitoring, observability, deployment, secret management, and more.

The more tools you have, the more difficult it becomes to integrate them cleanly, manage their versions, ensure compatibility, and avoid duplicated effort. Organizations often get stuck with “tool sprawl” — tools chosen by different teams, legacy systems, or overlapping functionalities — which introduce friction, maintenance burden, and sometimes vulnerabilities.

Finally, although devops has spread far and wide, there is still cultural resistance and alignment. Devops isn’t just about tools and processes; it’s about collaboration, shared responsibility, and continuous feedback. Teams rooted in traditional silos (dev vs ops, or security separate) may resist changes to roles and workflows. Leadership support, communication of shared goals, trust, and allowance for continuous learning are all necessary.

Many CIOs focus too much on tools or implementation first, rather than organizational culture and behaviors; but without addressing culture, even the best tools or processes may not yield the hoped-for velocity, quality, or reliability. Organizations that succeed here tend to have proactive strategies: dedicated training programs, mentorship, internal “guilds,” pairing junior and senior engineers, and making sure leadership supports ongoing learning rather than one-off bootcamps.

Why do devops?

Whoever you ask will tell you that devops is a major culture shift for organizations, so why go through that pain at all?

Devops aims to combine the formerly conflicting aims of developers and system administrators. Under its principles, all software development aims to meet business demands, add functionality, and improve the usability of applications while also ensuring those applications are stable, secure, and reliable. Done right, this improves the velocity and quality of your output, while also improving the lives of those working on these outcomes.

Does devops save money — or add cost?

Devops teams are recognizing that speed and agility are only part of success — unchecked cloud bills and waste undermine long-term sustainability. Waste in devops often comes in the form of “devops debt”— idle cloud capacity, dead code, or false-positive security alerts—which was called a “hidden tax on innovation” in recent Java-environment studies.

 Embedding finops practices can help fight these costs. Teams should shift left on cost: estimating costs when spinning up new environments, resizing instances, and scaling down unused resources before they become runaway expenses.

How to start with devops

There are lots of resources for help getting started with devops, including Kim’s own Devops Handbook, or you can enlist the help of external consultants. But you have to be methodical and focus on your people more than on the tools and technology you will eventually use if you want to ensure lasting buy-in across the business.

A proven route to achieving this is a “land and expand” strategy, where a small group starts by mapping key value streams and identifying a single product team or workload for trialing devops practices. If this team is successful in proving the value of the shift, you will likely start to get interest from other teams and from senior leadership.

If you are at the start of your devops journey, however, make sure you are prepared for the disruption a change like this can have on your organization, and keep your eye on the prize of building better, faster, stronger software.

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More on devops:

• Devops debt: The hidden tax on innovation
• 10 big devops mistakes and how to avoid them
• Smarter devops: How to avoid deployment horrors

What is cloud native? Cloud native defined

The term “cloud-native computing” encompasses the modern approach to building and running software applications that exploit the flexibility, scalability, and resilience of cloud computing. The phrase is a catch-all that encompasses not just the specific architecture choices and environments used to build applications for the public cloud, but also the software engineering techniques and philosophies used by cloud developers.

The Cloud Native Computing Foundation (CNCF) is an open source organization that hosts many important cloud-related projects and helps set the tone for the world of cloud development. The CNCF offers its own definition of cloud native:

Cloud native practices empower organizations to develop, build, and deploy workloads in computing environments (public, private, hybrid cloud) to meet their organizational needs at scale in a programmatic and repeatable manner. It is characterized by loosely coupled systems that interoperate in a manner that is secure, resilient, manageable, sustainable, and observable.

Cloud native technologies and architectures typically consist of some combination of containers, service meshes, multi-tenancy, microservices, immutable infrastructure, serverless, and declarative APIs — this list is not exhaustive.

This definition is a good start, but as cloud infrastructure becomes ubiquitous, the cloud native world is beginning to spread behind the core of this definition. We’ll explore that evolution as well, and look into the near future of cloud-native computing.

Cloud native architectural principles

Let’s start by exploring the pillars of cloud-native architecture. Many of these technologies and techniques were considered innovative and even revolutionary when they hit the market over the past few decades, but now have become widely accepted across the software development landscape.

Microservices. One of the huge cultural shifts that made cloud-native computing possible was the move from huge, monolithic applications to microservices: small, loosely coupled, and independently deployable components that work together to form a cloud-native application. These microservices can be scaled across cloud environments, though (as we’ll see in a moment) this makes systems more complex.

Containers and orchestration. In could-native architectures, individual microservices are executed inside containers — lightweight, portable virtual execution environments that can run on a variety of servers and cloud platforms. Containers insulate the developers from having to worry about the underlying machines on which their code will execute. That is, all they have to do is write to the container environment. 

Getting the containers to run properly and communicate with one another is where the complexity of cloud native computing starts to emerge. Initially, containers were created and managed by relatively simple platforms, the most common of which was Docker. But as cloud-native applications got more complex, container orchestration platforms that augmented Docker’s functionality emerged, such as Kubernetes, which allows you to deploy and manage multi-container applications at scale. Kubernetes is critical to cloud native computing as we know it — it’s worth noting that the CNCF was set up as a spinoff of the Linux Foundation on the same day that Kubernetes 1.0 was announced — and adhering to Kubernetes best practices is an important key to cloud native success. 

Open standards and APIs. The fact that containers and cloud platforms are largely defined by open standards and open source technologies is the secret sauce that makes all this modularity and orchestration possible, and standardized and documented APIs offer the means of communication between distributed components of a larger application. In theory, anyway, this standardization means that every component should be able to communicate with other components of an application without knowing about their inner workings, or about the inner workings of the various platform layers on which everything operates.

DevOps, agile methodologies, and infrastructure as code. Because cloud-native applications exist as a series of small, discrete units of functionality, cloud-native teams can build and update them using agile philosophies like DevOps, which promotes rapid, iterative CI/CD development. This enables teams to deliver business value more quickly and more reliably.

The virtualized nature of cloud environments also make them great candidates for infrastructure as code (IaC), a practice in which teams use tools like Terraform, Pulumi, and AWS CloudFormation, to manage infrastructure declaratively and version those declarations just like application code. IaC boosts automation, repeatability, and resilience across environments—all big advantages in the cloud world. IaC also goes hand-in-hand with the concept of immutable infrastructure—the idea that, once deployed, infastructure-level entities like virtual machines, containers, or network appliances don’t change, which makes them easier to manage and secure. IaC stores declarative configuration code in version control, which creates an audit log of any changes.

How the cloud-native stack is expanding

As cloud-native development becomes the norm, the cloud-native ecosystem is expanding; the CNCF maintains a graphical representation of what it calls the  cloud native landscape that hammers home to expansive and bewildering variety of products, services, and open source projects that contribute to (and seek to profit from) to cloud-native computing. And there are a number of areas where new and developing tools are complicating the picture sketched out by the pillars we discussed above.   

An expanding Kubernetes ecosystem. Kubernetes is complex, and teams now rely on an entire ecosystem of projects to get the most out of it: Helm for packaging, ArgoCD for GitOps-style deployments, and Kustomize for configuration management. And just as Kubernetes augmented Docker for enterprise-scale deployments. Kubernetes itself has been augmented and expanded by service mesh offerings like Istio and Linkerd, which offer fine-grained traffic control and improved security

Observability needs. The complex and distributed world of cloud-native computing requires in-depth observability to ensure that developers and admins have a handle on what’s happening with their applications. Cloud-native observability uses distributed tracing and aggregated logs to provide deep insight into performance and reliability. Tools like Prometheus, Grafana, Jaeger, and OpenTelemetry support comprehensive, real-time observability across the stack.

Serverless computing.  Serverless computing, particularly in its function-as-a-service guise, offers to strip needed compute resources down to their bare minimum, with functions running on service provider clouds using exactly as much as they need and no more. Because these services can be exposed as endpoints via APIs, they are increasingly integrated into distributed applications, operating side-by-side with functionality provided by containerized microservices. Watch out, though: the big FaaS providers (Amazon, Microsoft, and Google) would love to lock you in to their ecosystems.  

FinOps. Cloud computing was initially billed as a way to cut costs — no need to pay for an in-house data center that you barely use — but in practice it replaces capex with opex, and sometimes you can run up truly shocking cloud service bills if you aren’t careful. Serverless computing is one way to cut down on those costs, but financial operations, or FinOps, is a more systematic discipline that aims to aligns engineering, finance, and product to optimize cloud spending. FinOps best practices make use of those observability tools to best determine what departments and applications are eating up resources.

How cloud-native architecture is adapting to AI workloads

Enterprises deploy larger AI models and make use of more and more real-time inference services. That’s putting demands on cloud-native systems and forcing them to adapt to remain scalable and reliable.

For instance, organizations are re-engineering cloud environments around GPU-accelerated clusters, low-latency networking, and predictable orchestration. These needs align with established cloud-native patterns: containers package AI services consistently, while Kubernetes provides resilient scheduling and horizontal scale for inference workloads that can spike without warning.

Kubernetes itself is changing to better support AI inference, adding hardware-aware scheduling for GPUs, model-specific autoscaling behavior, and deeper observability into inference pipelines. These enhancements make Kubernetes a more natural platform for serving generative AI workloads.

AI’s resource demands are amplifying traditional cloud-native challenges. Observability becomes more complex as inference paths span GPUs, CPUs, vector databases, and distributed storage. FinOps teams contend with cost volatility from training and inference bursts. And security teams must track new risks around model provenance, data access, and supply-chain integrity.

Application frameworks for building distributed cloud-native apps

Microsoft’s Aspire is one of the most visible examples of a shift towards application frameworks to simplify how teams build distributed systems. Opinionated frameworks like Aspire provide structure, observability, and integration out of the box so developer don’t need to stitch together containers, microservices, and orchestration tooling by hand.

Aspire in particular is a prescriptive framework for cloud-native applications, bundling containerized services, environment configuration, health checks, and observability into a unified development model. Aspire provides defaults for service-to-service communication, configuration, and deployment, along with a built-in dashboard for visibility across distributed components.

While Aspire was originally aligned with Microsoft’s .NET platform,Redmond now sees it as having a  polyglot future. This positions Aspire as part of a broader trend: frameworks that help teams build cloud-native, service-oriented systems without being locked into a single language ecosystem. Several other frameworks are gaining traction: Dapr provides a portable runtime that abstracts many of the plumbing tasks in cloud-native distributed applications, and Orleans offers an actor-model-based framework for large-scale systems in the .NET world, and Akka gives JVM teams a mature, reactive toolkit for elastic, resilient services.

Frameworks and tools in the expanding cloud-native ecosystem

While frameworks like Aspire simplify how developers compose and structure distributed applications, most cloud-native systems still depend on a broader ecosystem of platforms and operational tooling. This deeper layer is where much of the complexity—and innovation—of cloud-native computing lives, particularly as Kubernetes continues to serve as the industry’s control plane for modern infrastructure.

Kubernetes provides the core abstractions for deploying and orchestrating containerized workloads at scale. Managed distributions such as Google Kubernetes Engine (GKE), Amazon EKS, Azure AKS, and Red Hat OpenShift build on these primitives with security, lifecycle automation, and enterprise support. Platform vendors are increasingly automating cluster operations—upgrades, scaling, remediation—to reduce the operational burden on engineering teams.

Surrounding Kubernetes is a rapidly expanding ecosystem of complementary frameworks and tools. Service meshes like Istio and Linkerd provide fine-grained traffic management, policy enforcement, and mTLS-based security across microservices. GitOps platforms such as Argo CD and Flux bring declarative, version-controlled deployments to cloud-native environments. Meanwhile, projects like Crossplane turn Kubernetes into a universal control plane for cloud infrastructure, letting teams provision databases, queues, and storage through familiar Kubernetes APIs. These tools illustrate how cloud-native development now spans multiple layers: developer-focused application frameworks like Aspire at the top, and a powerful, evolving Kubernetes ecosystem underneath that keeps modern distributed applications running.

Advantages and challenges for cloud-native development

Cloud native has become so ubiquitous that its advantages are almost taken for granted at this point, but it’s worth reflecting on the beneficial shift the cloud native paradigm represents. Huge, monolithic codebases that saw updates rolled out once every couple of years have been replaced by microservice-based applications that can be improved continuously. Cloud-based deployments, when managed correctly, make better use of compute resources and allow companies to offer their products as SaaS or PaaS services. 

But cloud-native deployments come with a number of challenges, too:

• Complexity and operational overhead: You’ll have noticed by now that many of the cloud-native tools we’ve discussed, like service meshes and observability tools, are needed to deal with the complexity of cloud-native applications and environments. Individual microservices are deceptively simple, but coordinating them all in a distributed environment is a big lift.
• Security: More services executing on more machines, communicating by open APIs, all adds up to a bigger attack surface for hackers. Containers and APIs each have their own special security needs, and a policy engine can be an important tool for imposing a security baseline on a sprawling cloud-native app. DevSecOps, which adds security to DevOps, has become an important cloud-native development practice to try to close these gaps.
• Vendor lock-in: This may come as a surprise, since cloud-native is based on open standards and open source. But there are differences in how the big cloud and serverless providers works, and once you’ve written code with one provider in mind, it can be hard to migrate elsewhere.
• A persistent skills gap: Cloud-native computing and development may have years under its belt at this point, but the number of developers who are truly skilled in this arena is a smaller portion of the workforce than you’d think. Companies face difficult choices in bridging this skills gap, whether that’s bidding up salaries, working to upskill current workers, or allowing remote work so they can cast a wide net. 

Cloud native in the real world

Cloud native computing is often associated with giants like Netflix, Spotify, Uber, and AirBNB, where many of its technologies were pioneered in the early ’10s. But the CNCF’s Case Studies page provides an in-depth look at how cloud native technologies are helping companies. Examples include the following:

• A UK-based payment technology company that can switch between data centers and clouds with zero downtime
• A software company whose product collects and analyzes data from IoT devices — and can scale up as the number of gadgets grows
• A Czech web service company that managed to improve performance while reducing costs by migrating to the cloud

Cloud-native infrastructure’s capability to quickly scale up to large workloads also make it an attractive platform for developing AI/ML applications: another one of those CNCF case studies looks at how IBM uses Kubernetes to train its Watsonx assistant. The big three providers are putting a lot of effort into pitching their platforms as the place for you to develop your own generative AI tools, with offerings like Azure AI Foundry,Google Firebase Studio, and Amazon Bedrock. It seems clear that cloud native technology is ready for what comes next.

Learn more about related cloud-native technologies:

• Platform-as-a-service (PaaS) explained
• What is cloud computing
• Multicloud explained
• Agile methodology explained
• Agile development best practices
• Devops explained
• Devops best practices
• Microservices explained
• Microservices tutorial
• Docker and Linux containers explained
• Kubernetes tutorial
• CI/CD (continuous integration and continuous delivery) explained
• CI/CD best practices

Over the past decade, software development has been shaped by two closely related transformations. One is the rise of devops and continuous integration and continuous delivery (CI/CD), which brought development and operations teams together around automated, incremental software delivery.

The other is the shift from monolithic applications to distributed, cloud-native systems built from microservices and containers, typically managed by orchestration platforms such as Kubernetes.

While Kubernetes and similar platforms simplify many aspects of running distributed applications, operating these systems at scale is still complicated. Configuration sprawl, environment drift, and the need for rapid, reliable change all introduce operational challenges. GitOps emerged as a way to address those challenges by extending familiar devops and CI/CD techniques beyond application code and into infrastructure and system configuration.

At the heart of GitOps is the concept of infrastructure as code (IaC). In a GitOps model, not only application code but also infrastructure definitions, deployment configurations, and operational settings are described in files stored in a version control system. Automated processes continuously compare the running system with those declarations and work to bring the live environment back into alignment when differences appear.

In this approach, the version control repository serves as the system of record for how applications and their supporting infrastructure should look in production. Changes flow through the same review, approval, and automation pipelines that developers already use for software, bringing greater consistency, traceability, and repeatability to cloud-native operations.

At a high level, GitOps refers to a set of operational practices for managing cloud-native systems using declarative configuration, version control, and automated reconciliation. Rather than treating infrastructure and application configuration as mutable runtime state, GitOps treats them as versioned artifacts that move through the same review, testing, and deployment processes as application code.

GitOps defined

The term GitOps was originally coined and popularized by Weaveworks, which helped formalize the approach in the context of Kubernetes operations. While that early work shaped the way GitOps was discussed and implemented, GitOps has since evolved into a broadly adopted, vendor-neutral pattern. Today, it describes a shared set of ideas rather than a specific product or platform.

The defining characteristic of GitOps is its reliance on declarative configuration stored in a version control system. Instead of issuing imperative commands to change live systems, teams describe the desired state of applications and infrastructure in configuration files. Automated agents then continuously compare that declared state with what is actually running and work to reconcile any differences. This pull-based model—where systems converge toward the desired state defined in version control—provides built-in drift detection, repeatability, and a clear audit trail for every change.

Because GitOps centers on configuration files stored in a version control system, familiar software development practices carry over naturally. Changes are proposed through commits, reviewed before being accepted, and tracked over time. Rollbacks are accomplished by reverting to known-good versions, and the history of how a system evolved is preserved alongside the configuration itself.

While the use of Git as the version control system is not strictly required, it has become the default choice because of its ubiquity in modern devops workflows and its strong support for collaboration and change management, so its place in the name has stuck.

What is the CI/CD process?

A complete look at CI/CD is beyond the scope of this article—see the InfoWorld explainer on the subject—but we need to say a few words about CI/CD because it’s at the core of how GitOps works. The continuous integration half of CI/CD is enabled by version control repositories like Git: Developers can make constant small improvements to their codebase, rather than rolling out huge, monolithic new versions every few months or years. The continuous deployment piece is made possible by automated systems called pipelines that build, test, and deploy the new code to production.

Again, we keep talking about code here, and that usually summons up visions of executable code written in a programming language such as C or Java or JavaScript. But in GitOps, the “code” we’re managing is largely made up of configuration files. This isn’t just a minor detail — it’s at the heart of what GitOps does. These config files are, as we’ve said, the “single source of truth” describing what our system should look like. They are declarative rather than instructive. That means that instead of saying “start up ten servers,” the configuration file will simply say, “this system includes ten servers.”

GitOps and Kubernetes

GitOps first took hold in the Kubernetes ecosystem, where declarative configuration and continuous reconciliation are core design principles. As a result, Kubernetes remains the most common and best-understood environment for applying GitOps practices. A typical GitOps-driven update process for a Kubernetes application looks like this:

1. A developer proposes a change by committing updated application code or configuration to a version control repository, usually through a pull request.
2. That change is reviewed and approved, then merged into the main branch.
3. The merge triggers an automated CI/CD pipeline that tests the change, builds new artifacts if needed, and publishes them to a registry.
4. A GitOps controller or similar automated agent detects the updated desired state stored in version control.
5. The controller compares that desired state with the current state of the Kubernetes cluster and applies the necessary changes to bring the cluster back into alignment.

This pull-based reconciliation loop—where the cluster continuously converges toward the desired state defined in version control—is central to how GitOps works in practice. While Kubernetes provides a natural fit for this model, it represents just one canonical use case. The same patterns increasingly apply to infrastructure provisioning, policy enforcement, and multi-cluster operations beyond Kubernetes itself.

GitOps tooling in practice: Argo CD, Flux, and the ecosystem

GitOps is enabled by a set of tools that embody the principles we’ve outlined, with some open-source projects emerging as de facto standards in cloud-native environments.

At the center of the GitOps ecosystem is Argo CD, an open-source controller that continuously monitors a version control repository and ensures that the state of running systems matches the declared desired state. Argo CD is widely used in Kubernetes environments because it directly implements pull-based reconciliation: it compares the desired state stored in Git with the cluster’s actual state and applies changes to correct any drift.

Alongside Argo CD, Flux is another prominent open source GitOps engine. Both Flux and Argo CD help teams adopt GitOps workflows by managing the synchronization loop between code and runtime, but they differ in operational philosophy, integration surfaces, and ecosystem fit.

GitOps tooling often appears as part of broader platforms or integrated stacks rather than as isolated utilities. For example, multicloud and cluster management solutions now routinely include GitOps support, with Argo CD or compatible controllers bundled alongside deployment, policy, and governance capabilities.

In addition to Flux and Argo CD, a range of auxiliary tools contribute to a complete GitOps ecosystem: policy as code engines (e.g., Open Policy Agent), drift detection systems, and infrastructure provisioning tools that mesh with Git-centric workflows.

GitOps, devops, and normalization

GitOps grew out of the same forces that drove devops into mainstream IT practice, and in its early days, GitOps was often discussed as a distinct extension of devops, specifically tailored to managing declarative infrastructure and Kubernetes-centric systems. At the time, GitOps was still relatively new and not yet widely adopted outside cloud-native pioneers.

Over the last several years, however, GitOps practices have become deeply woven into how teams operate modern cloud environments. Rather than being treated as an optional add-on or marketing term, the core ideas of GitOps — using version-controlled, declarative configuration and automated reconciliation loops to continuously align running systems with intended state — are now part of standard operational practice in many Kubernetes-centric shops. In this sense, GitOps has shifted from a buzzword about what might be possible to a baseline pattern for cloud-native operations, much like devops itself did years earlier.

In environments where Kubernetes and declarative systems are the norm, GitOps workflows are the default way teams manage and deploy change. Many organizations now implement these patterns without explicitly calling them “GitOps,” just as few teams today explicitly say they do “CI/CD” even though continuous pipelines are taken for granted. The term has become less prominent in marketing, but its practices are often embedded in pipelines, controllers, and platform tooling.

That normalization shows up in how GitOps workflows are woven into broader operational frameworks. For example, platform engineering teams frequently build internal developer platforms that encapsulate GitOps patterns behind standardized developer APIs, making the pattern invisible to most application teams while still providing the auditability and automation that GitOps promises.

GitOps beyond Kubernetes: infrastructure, policy, and drift

While GitOps first gained traction as a way to manage Kubernetes deployments, its core principles apply broadly to infrastructure and operational concerns beyond any single orchestration platform. GitOps treats desired state as declarative configuration stored in version control and uses automated reconciliation to ensure running systems align with that state. That pattern naturally extends to infrastructure provisioning, policy enforcement, configuration drift detection, and governance workflows across diverse environments.

In modern operational stacks, infrastructure is increasingly defined declaratively, whether through Kubernetes manifests, Terraform modules, or other infrastructure-as-code formats. Storing these declarations in version control enables the same peer-review, auditability, and rollback practices developers already use for application code. Automated tooling then continuously detects when the live infrastructure diverges from the declared state and works to bring it back into alignment, reducing the risk of configuration drift and inadvertent misconfigurations.

Configuration drift — the state where an environment has diverged from what’s declared in version control — remains a major operational headache, especially in complex, dynamic systems. Drift can arise from ad hoc fixes, emergency updates, or manual changes made outside normal pipelines, and it can lead to inconsistencies, outages, and security gaps. By continually checking running systems against the desired state in Git and reconciling deviations automatically, GitOps workflows help teams keep environments predictable and auditable.

Policy enforcement and compliance are another natural extension of GitOps patterns. As organizations adopt declarative practices, policy-as-code engines and drift detection systems can be woven into GitOps pipelines to validate that proposed configurations meet security, compliance, or operational standards before they’re ever applied to running systems. Embedding policy checks into declarative workflows brings consistency to governance while preserving the automation and speed that devops teams expect.

GitOps – beyond Kubernetes

GitOps began as a way to bring devops discipline to Kubernetes operations, but its longer-term impact has been more subtle. In many ways, it’s been absorbed into the fabric of modern cloud-native operations, where declarative configuration, version control, and automated reconciliation are taken for granted. Today, GitOps is less about a specific set of tools or a named practice and more about an operational mindset. By treating infrastructure and configuration as versioned, auditable artifacts and relying on automation to enforce consistency, GitOps helps teams manage complexity at scale. Even as the term itself fades from the spotlight, the practices it introduced continue to shape how distributed systems are built, deployed, and operated.

AI tools today enable faster processes, leaner operations and lower costs, making efficiency wins the new baseline. However, for many businesses, the strategy stops at those first wins.

This has created a growing leadership blind spot: Once you achieve AI ROI, how do you make the most of it? If there is no clear reinvestment strategy, AI gains burn out quickly and disappear into the business without meaningfully compounding their value.

For CIOs, the next challenge is not just proving AI can make the business more efficient but deciding how those gains can build a stronger company and sustain growth over the long term.

Start by investing in a crystal ball

One of the smartest ways to reinvest AI gains is to improve how the business evaluates what is worth building in the first place.

Leaders who chase “cool” use cases without defining the business impact or path to ROI upfront often end up with systems that drain funds without creating compounding returns. Instead, a clear reinvestment strategy uses AI to assess the strongest use cases before scaling up.

AI tools today can help teams move from idea to prototype to impact analysis much faster than before. That makes it easier to identify which projects have a credible path to ROI and which ones can be filed away. Access to these quick insights allows businesses to test whether a use case has real value before committing larger engineering or model costs.

This is especially crucial right now as AI is becoming more costly as businesses scale it. What looked inexpensive in early pilots can become far pricier once it is embedded in day-to-day work and as AI providers tokenize and meter its use. The more central AI becomes, the more intentional leaders need to be about where it is used, what it actually returns and how to reinvest those gains.

Not every workflow belongs in the same model. Not every task needs an agent. As AI vendors mature and monetization models evolve, the businesses that will win will be the ones that make those distinctions early, reinvest accordingly and keep building ahead of customer needs rather than reacting to them. Not every workflow belongs in the same model. Not every task needs an agent.

Cycle ROI gains back into tooling

Once AI activations start to show dividends, it’s time to reinvest in stronger tooling. This should include new AI tools that continue to advance the business, as well as continued investment in what has already worked. That compounding effect is ultimately what separates businesses that sustain AI-driven growth from those that plateau after early wins.

I’ve seen firsthand the benefits of investing in new tools that make AI more usable, repeatable and valuable in workflows. For example, automated product management tools enable rapid prototyping and product rationalization. Decision intelligence platforms can help teams simulate scenarios. Customer behavior modeling tools can help predict churn and shift customer demand patterns. These advanced solutions can help teams move from an idea to a working concept in days instead of months.

Smart reinvestment is about building the right technical mix for the outcomes the business needs, rather than funding more AI for its own sake. To maximize impact, start with tooling for governance and upskilling.

1. (Re)invest in governance

As AI usage spreads and matures across teams, products and functions, a strategic policy framework becomes all the more vital. CIOs should work to reinforce the governance foundations already in place so they can support broader adoption, rather than rebuilding new policy from scratch each time AI usage expands. This means reinvesting in shared standards, oversight mechanisms and supporting roles that make governance more durable and practical over time.

Without doubling down on governance, businesses risk creating siloed, disconnected pockets of experimentation. Those pockets quickly become expensive to monitor and difficult to secure, creating further risk to consistency, compliance and trust. The consequence is often wasted spend as experiments stall or overlap, or outcomes that are too fragmented to scale.

When businesses keep governance investment at the center of their reinvestment strategy, it becomes a force multiplier. It reduces duplication across teams, creates more commonality across products and makes it easier to expand AI use without increasing fragmentation or risk.

2. Empower employees to grow

Smart tools only create real value when people are equipped to use them well. That is why reinvestment should go beyond technology alone.

As AI tools become more powerful and accurate, the skills barrier to building something useful is dropping. Employees can get much closer to a viable concept much faster with AI, but that only works if businesses create learning pathways, academies and practical enablement that help teams use these tools well.

Smarter tooling can help product, operations and technology teams collaborate with fewer layers between idea and execution. As employees build new skills, they can stay closer to a single initiative from start to finish. That reduces handoffs, empowers employees to learn new skills and offers a more direct path from the original idea to the final result.

Let AI ROI fund your fight against siloes

Over the next few years, the businesses that pull ahead are not simply going to be the ones with the most AI pilots or the biggest efficiency gains. They will be the ones that invest AI ROI in bridging what has long been disconnected: systems, teams, workflows and ecosystems.

In telecom, for example, AI is already creating savings inside billing operations and other back-office work tied to the BSS layer. The smart move for telcos is not to stop at those savings, but to reinvest them in connecting their BSS and OSS, where fragmentation and siloes have long slowed telcos down.

Think about what that means in practice: instead of billing, service configuration and network operations functioning as separate systems with separate handoffs, AI can help orchestrate them. That makes it easier to move from order to activation to support with less internal friction, better visibility and fewer breakdowns between what was sold and what is actually delivered.

For the customer, that means a broadband outage, plan change or installation appointment is handled as one connected journey rather than a chain of handoffs. The outcome is a more connected operating model that makes the customer experience feel far less complex.

The same logic applies across industries. In banking, a customer with a mortgage, checking account and credit card at the same institution is often still treated as three separate relationships – because the underlying systems do not communicate. AI orchestration can change that, giving banks a unified view of the customer and employees the context to act on it.

Not using AI to do the same work faster, but using AI dividends to build a business that works better. That is what smart investment looks like.

ROI is just the start

AI can absolutely free up capital. That, however, is only the first chapter.

The bigger story is what leaders choose to do next: reinvest in better tooling, more consistent governance, smarter workforce enablement and operating models built to connect across silos. The payoff will be a more resilient, agile business ready for what’s next.

This article is published as part of the Foundry Expert Contributor Network.
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Until recently, many tech professionals viewed themselves as a special and respected worker class: highly educated, hard-working, well paid, and in demand.

“They considered themselves above unions,” says Zak Thompson, senior software engineer at Kickstarter and union steward at Kickstarter United.

Big Tech issued aspirational mission statements that motivated workers, workplaces were seen as meritocracies, and employees were encouraged to speak out if they were unhappy. If workers didn’t like where they worked, they just moved on: other employers would be falling over themselves to hire them.

How times have changed.

Now, fed up with mass layoffs, disillusioned with Big Tech’s direction, and stunned by bold management proclamations that AI will displace huge numbers of people in many tech jobs — starting with programmers — interest in unions has risen sharply among tech professionals. Workers in some organizations, including Kickstarter, have already taken the plunge.

A surge in interest

“Starting in 2022, the industry as a whole started seeing very large layoffs across the board [and] that has dramatically shifted the balance of power. I think most people in the industry have experienced that one way or another,” says Google software engineer Alan McAvinney.

But not everyone is convinced that the layoffs have changed the power dynamic. “I wouldn’t say the balance has definitively shifted… some things point to workers losing ground and others point to improvement,” says Liya Palagashvili, senior research fellow and director of the Labor Policy Project at the Mercatus Center at George Mason University.

What matters, she says, is not the size of the layoffs but worker options: how easily laid off workers can find alternative work in their field.

For McAvinney, the decision to support a union was about a culture change at his employer.

“In 2019, Google fired four people (the ‘Thanksgiving Four’) after they organized and spoke out internally against the company’s work with the anti-union firm IRI and US Customs and Border Protection. That was a big turning point for me,” he says. “Historically, we had a pretty robust culture that actually encouraged us to speak up internally.”

Google said the employees were fired for violating data security policies, but many workers believed the move was retaliatory. It became a galvanizing event that contributed to the launch of the Alphabet Workers Union (AWU) in 2021, says McAvinney, organizing chair, Alphabet Workers Union-CWA. (Alphabet is the parent company of Google.)

So far, tech worker interest in unions hasn’t translated into higher membership numbers nationally. According to the US Census Bureau’s Current Population Survey (CPS), union membership in tech occupations was about 3.5% in 2025, says Palagashvili. “While there have been some high-profile organizing efforts, they do not yet show up as a broad national increase in tech-sector unionization.”

Overall, only 10% of American workers belonged to a union in 2025, the Bureau of Labor Statistics (BLS) reported — near an all-time low — but interest in labor unions is rising. A 2025 Gallup survey found that 68% of Americans approved of unions, up from 48% in 2009. Interest is particularly strong among younger workers, the Economic Policy Institute reports, and in a 2024 online survey of 1,900 tech professionals on the career site Blind, 67% of respondents said they’d be “very likely” or “somewhat likely” to join a union if their company had one.

Nonetheless, for most tech professionals, those positive perceptions have not so far translated into widespread union membership.

Fear, uncertainty, and doubt

In the wake of mass layoffs that began in 2022, the primary driver toward tech worker unionization may well be job security.

“I think a greater concern is that their work and skills have been devalued at the same time their jobs become less secure and their wages and benefits have declined,” says Kate Bronfenbrenner, director of labor education research and senior lecturer emeritus at Cornell University’s School of Industrial and Labor Relations.

The fear that AI will displace IT workers en masse is palpable, says Google’s McAvinney. Whether the mass layoffs to date were actually driven by AI or if AI was used as a pretext, “Large numbers of people have that concern, and that is absolutely part of the interest in collective action — getting organized, joining a union, or forming a union,” he says.

The second motivator is ideological disillusionment. “Workers recruited with promises that they would be changing the world discovered that they were really building surveillance systems or military technology,” Bronfenbrenner says.

The insidious use of AI surveillance is another concern, says Bronfenbrenner. For example, Meta’s announcement that it would use AI to track US-based workers’ computer activities, including clicks, keystrokes, mouse movements, and screen snapshots to train AI agents had a dystopian feel to it. Were these workers training AI to take over their jobs, just as US workers were asked to train their lower-cost foreign replacements during the offshoring craze in the mid-2000s? (Meta later paused the tracking program after employees twice demonstrated the inadequacy of privacy protections for the collected data.)

But Bronfenbrenner argues that AI’s bigger threat may be its use as a surveillance tool to prevent organizing. “My research on surveillance in organizing campaigns found that it tripled from 11% in the early 2000s to one third in 2021,” she says.

“The deeper pattern is the same one inherent in Taylorism — management trying to know everything that’s under the worker’s cap, to monitor every step so workers have no control and no secrets,” Bronfenbrenner says. “Now they have even more technology to do it, and they can potentially replace you entirely with AI.”

Simone Robutti, an organizer with Tech Workers Coalition Global, calls the current wave of tech layoffs “a prequel to whatever AI-driven layoffs are coming.” It’s part of the trend of “lowering the cost of knowledge workers, of cognitive workers, of office workers in general — because that’s the bet on AI,” he says.

Whether employers can in fact replace workers with AI (or will be able to soon) is an open question. “If Amazon lays off a hundred workers, and ninety of them find comparable jobs within a few months, that mitigates the concern,” says Palagashvili from George Mason University. “If most of them have to sell their houses and move across the country, or end up underemployed — not just unemployed, but working in warehouses instead of at a competitor — that’s a different picture.”

So far that hasn’t been a big issue for former Google employees, says McAvinney. “It used to be that if you left Google, you could get a job anywhere in tech instantly. That’s no longer the case, but most people I talk to are still finding work in the industry,” he says.

But for those newly entering the workforce, it’s much harder to find a job. According to the Stanford HAI 2026 AI Index Report, released in April, “employment for software developers ages 22 to 25 has fallen nearly 20% from 2024.”

Successes and setbacks

While organizing can be an uphill battle and workers often face aggressive pushback from their employers, there have been a few notable successes.

In the UK, workers can join a union as individual members before their employer formally recognizes that union for collective bargaining purposes. That’s how 300 workers in Google DeepMind’s London office initially joined the Communication Workers Union. In April, 98% of the 300 CWU members voted in favor of pursuing union recognition, formally requesting that management recognize the CWU and Unite the Union as representatives for approximately 1,000 staff. (Google DeepMind disputed characterizing the action as a vote to unionize).

And in May, some 2,100 tech workers at the University of California joined the University Professional and Technical Employees union, which is affiliated with Communications Workers of America (UPTE-CWA), with 96% of the workers voting yes.

“A lot of tech workers right now are extremely concerned about job security and about their work being automated,” says Max Belasco, a business systems analyst at the UCLA School of Law and co-chair of the UCLA chapter of UPTE-CWA, Local 9119.

The CWA described the organizing initiative as “the largest tech industry organizing campaign in US history.” But it wasn’t the first.

“Between 2022 and 2024, CWA organized nearly 400 different digital media companies,” Bronfenbrenner says, including the game developer Activision and the New York Times.

Kickstarter’s 85 employees voted in 2020 to form Kickstarter United — the vote was 55% in favor — and most recently the union negotiated a contract that includes a four-day workweek, AI protections, and a minimum pay floor for 59 employees, including tech workers.

“What we ended up winning was yearly benchmarking of all employee salaries to the 60th percentile, along with yearly cost of living adjustments,” says Thompson.

But the way forward has been rocky. Shortly after the union was ratified, Kickstarter announced layoffs. The union, which is affiliated with the Office and Professional Employees International Union (OPEIU), wasn’t able to reverse that decision but did negotiate better severance terms, including four months of severance pay (versus 2 to 3 weeks for every year worked) and six months of health benefits.

When contract negotiations faltered in October 2025, the union went on strike for 42 days. By December, a new contract was ratified. Shortly thereafter, the company announced another round of layoffs that included four union leaders, one of whom had helped to negotiate the new contract. The union is currently fighting those dismissals and will be arguing its case in third-party arbitration.

The National Labor Relations Act of 1935 codified American workers’ rights to unionize and take collective action, and it established the National Labor Relations Board to protect those rights. Unfortunately for Kickstarter, NLRA enforcement under the Trump administration isn’t what it once was.

“Cases brought up for violations of the NLRA can go for months or years without ever seeing a hearing or having any sort of judgment. That gives companies more power to flagrantly ignore it,” Thompson says.

Tech firms have other weapons to dissuade employees from unionizing. Researchers from Carnegie Mellon University and Princeton University in 2025 interviewed 44 US-based tech worker-organizers, who cited additional pressure tactics including threats to withdraw venture capital funding — essentially killing venture-backed firms if employees vote to unionize — and threats of being fired that put tech workers with H-1B visas in an impossible position.

Kickstarter United is a majority union — one that has won NLRB certification. While that’s possible in smaller organizations, success in larger tech firms has been much more limited.

Alphabet is a prime example: the Alphabet Workers Union-CWA is a “pre-majority” union that lacks NLRB certification and has no formally recognized bargaining unit. Formed in 2021 with fewer than 400 members, today it represents 1,400 members, still a small fraction of Alphabet’s US-based workforce, estimated at over 100,000.

The challenge, says McAvinney, lies in trying to organize a distributed workforce of in-office, remote, and contract workers. “Large tech companies don’t split easily into discrete segments — there’s no strong geographic component to teams, and a single team is often spread across many locations,” which makes getting majority support unrealistic, he says.

But that doesn’t mean the union has had no impact. “In January, we launched our Googlers for Job Security campaign. Today we’re organizing around four demands: a guaranteed minimum severance package for everyone who’s laid off, voluntary buyouts before any mandatory layoffs, an end to GRAD quotas (GRAD being Google’s performance review system) so ratings reflect actual performance and aren’t given or changed to force a particular distribution, and the option to take severance as leave, giving workers, especially those on visas, more time on payroll,” McAvinney says.

“In response, Google did start offering voluntary exit packages,” he says. The union was also able to negotiate one contract, for Google Help workers. However, those workers aren’t actually Google employees: they’re contractors who report to Google management but work for Accenture.

The counterargument

Do unions get what they bargain for? Conservative business and labor economists say union contracts typically have rigid pay structures that restrict merit-based pay in favor of seniority-based wage increases, and that unions, as certified by the NLRB, create labor monopolies that limit worker choice and push up wages to levels detrimental to both workers and business.

The collective bargaining model is not well suited to the highly dynamic and innovation-driven tech sector, Palagashvili argues. “Firms often need to reorganize teams, redesign products, adjust roles, and redeploy talent quickly,” she says.

Collective bargaining agreements make those adjustments much more difficult by imposing uniform terms for an entire bargaining unit, regardless of individual preferences and circumstances. The contracts, she says, “are more about higher pay and less about flexibility.”

But Thompson says that hasn’t been his experience. “The thing with a union is you get to write the contract,” he says. “At Kickstarter we care about recognizing individual contributions, merit, and having a clear career progression.”

Kickstarter United pushed the company to clearly define what it takes to get a promotion, advocated for no “at will” employment, where employees can be fired any time without a stated reason; won standards for minimum pay, raises, promotions, and time off; secured AI protections; and codified a four-day work week.

Yes, some tech professionals voiced concerns about unions, such as that they stifle innovation and limit compensation for top performers, Thompson says, but “a lot of those people came around. They said ‘I was wrong. I feel way more protected, more secure, and I see the benefits.’”

Bronfenbrenner says it’s a mistake to think that tech workers are inherently different from other workers, adding that the two industries with the highest union density are entertainment and professional sports. “These are professionals with unique talents and capabilities, and they’ve organized successfully under the exclusive representation system.”

Will we see a unionized tech workforce?

If unions eventually prevail in tech, it will happen in the face of intense pressure from employers not to organize.

“The Alphabet Workers Union is a case study of the limits of the first wave of tech labor organizing,” says Robutti from the Tech Workers Coalition. “They hit a threshold beyond which they couldn’t fight the union busting anymore, and they became entrenched at that size.”

No one should expect large-scale unionization to occur overnight, Bronfenbrenner says. “The auto and steel industries weren’t organized in months. It took decades. Organizing global tech companies will take the same.”

While McAvinney acknowledges that a traditional majority union may be difficult to achieve any time soon in a company as large as Alphabet, he’s still bullish on his pre-majority union’s ability to make a difference. “Ultimately, regardless of which type of union you are, you can only win as much as you have leverage to win. Your leverage is inherently limited, but that doesn’t mean you can’t win anything,” he says.

Attitudes about unions appear to be changing rapidly. “Interest in unions is high, and I expect that will continue,” McAvinney says. “Now is an excellent time for people to start getting organized. I have seen lots of evidence of that.”

Thompson agrees. “We are seeing an uptick of people in tech reaching out, trying to get help organizing. When people have their job conditions continue to deteriorate, they are going to start organizing,” he says. “We’re definitely seeing a shift from ‘it’d be nice if we had a union’ to ‘okay, how can I actually do this now?’”

Come back next week for Part 2: How to unionize your tech workplace

Revised transaction structure enables immediate closing, accelerating the Company’s strategic pivot toward AI data centers, enterprise GPU compute, and digital power infrastructure.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) (“EVTV” or the “Company”) today announced the successful completion of its merger with Azio AI Corporation (“Azio AI”) on July 2, 2026, paving the way for the Company to transform to an AI Datacenter Provider and meeting the growing market demand for artificial intelligence (“AI”) infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; a market that the International Data Corporation (IDC) projects will reach $487 billion in global spending in 2026 and exceed $1 trillion by 2029.[1] The transaction marks a defining milestone in the Company’s strategic transformation and establishes the foundation for its next phase of commercial execution and long-term growth.

The parties amended the proposed transaction structure to expedite the closing timeline, allowing the combined company to begin operating as a fully integrated public company significantly sooner than originally anticipated. The accelerated closing enables management to immediately focus on commercialization across its expanding AI Datacenter strategy.

With the merger complete and the combined company operating as one organization, management is now fully focused on commercial execution, infrastructure deployment, strategic growth initiatives, and creating long-term shareholder value.

Over the past several months, the Company advanced development activities at its South Texas site and deployed six megawatts of off-grid power for its modular data centers. The Company further secured rights to a 548-acre site with the capacity to scale up to 500 MW, supporting the future development of AI hyperscale data centers.

Management believes these achievements demonstrate that the combined company is entering its next phase with meaningful operational momentum already in place rather than beginning from a standing start. Infrastructure deployment is underway, customer commitments have already been established, commercial execution is actively progressing, and the Company’s corporate structure is now aligned with an operating platform built to support long-term expansion.

The completion of the merger comes at a time when investment in AI infrastructure continues to accelerate globally as enterprises increasingly require access to high-performance computing resources, GPU infrastructure, and scalable digital power solutions. Management believes the combined company is well positioned to capitalize on these long-term industry trends through a diversified infrastructure strategy designed to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations.

Following the closing of the transaction, the Company intends to continue expanding its AI Infrastructure strategy through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships designed to maximize utilization of its power resources while creating multiple long-term revenue opportunities.

In connection with the closing of the merger, Phillip Oldridge has stepped down as Chief Executive Officer. Jason Maddox vacates the President position and is now the Chief Financial Officer. The Company’s Board of Directors appointed Simon Yu as President and Chris Young as Chief Executive Officer, effective immediately.

Mr. Yu is a serial entrepreneur and public markets operator with almost a decade of experience taking companies public, executing capital raises, and scaling businesses. He has previously served in founder, C-suite, and board roles at three publicly traded companies, two of which reached market capitalizations in excess of $1 billion. Mr. Yu has led legal, accounting, and advisory teams through Regulation A+ Tier 2 offerings, PCAOB audits, and public company reporting, alongside leading M&A transactions. As an active early-stage venture investor, he has evaluated investment opportunities across artificial intelligence, SaaS, and B2B technology.

Mr. Young brings extensive experience in launching and leading public companies and investing in and advising emerging technology companies, with a particular focus on artificial intelligence, software innovation, and strategic growth initiatives. Prior to joining EVTV, he served as Chief Executive Officer of Clubhouse Media Group, a publicly traded social media company and an Entrepreneur in Residence at Amplify, where he worked alongside founders and venture-backed technology companies to accelerate commercialization and support the development of high-growth technology businesses.

“Today’s announcement represents far more than the completion of a merger—it marks the beginning of our next chapter,” said Chris Young, Chief Executive Officer of EVTV. “Over the past several months, our teams have been building the operational foundation of this business while simultaneously working toward completing this transaction. With the merger now finalized, we move forward as one company with one leadership team and one strategy, focused on executing against the opportunities in front of us. We believe demand for AI infrastructure, enterprise compute, and digital infrastructure will continue expanding for years to come. Our objective is to build a scalable platform capable of serving that demand while creating long-term value for our shareholders.”

Jason Maddox, Chief Financial Officer of EVTV, added, “Completing this transaction under the amended merger structure allows us to immediately focus on execution. We have already established meaningful operational momentum, and we believe operating as a unified public company enhances our ability to deploy infrastructure, serve customers, pursue strategic growth opportunities, and continue building long-term shareholder value.”

The transaction establishes a unified operating platform designed to support the Company’s long-term growth strategy through continued investment in AI infrastructure, enterprise computing, digital power assets, and digital infrastructure development. Management believes the completion of the merger provides the operational and organizational foundation necessary to pursue the next phase of commercialization while expanding its presence across some of the fastest-growing sectors of the global technology market.

Transaction and Operational Highlights

• Successfully completed the merger with Azio AI pursuant to an amended and restated merger agreement.
• Approximately six megawatts of off-grid digital infrastructure deployed at the Company’s South Texas development site.
• Development footprint exceeding 548 acres with the potential to support up to 500 MW of AI infrastructure capacity.
• Combined company positioned to accelerate commercialization across AI infrastructure, enterprise GPU compute, digital power solutions, and digital asset mining operations.
• Merger consideration consisted of 2,655,157 shares of common stock and 973,450 shares of non-voting convertible preferred stock in exchange for 100% of outstanding capital stock of Azio AI, of which 194,807 shares of common stock were reserved for convertible notes of Azio AI assumed by the Company upon closing.
• Each share of preferred stock convertible into 100 shares of Company common stock subject to stockholder approval.
• Chris Young appointed Chief Executive Officer and Chairman of the Board.
• Simon Yu appointed President.
• Jason Maddox appointed Chief Financial Officer.
• Phillip Oldridge stepped down as Chief Executive Officer.

About Envirotech Vehicles, Inc.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) is a technology infrastructure company focused on developing, owning, and operating artificial intelligence data centers, enterprise GPU compute infrastructure, digital power solutions, and digital asset mining operations. Following its acquisition of Azio AI, the Company operates an integrated AI infrastructure business encompassing AI data center development, the sale and distribution of enterprise GPU systems and server infrastructure, high-performance computing solutions, power hosting, and strategic technology investments, serving enterprise and institutional customers across domestic and international markets. Through this diversified AI infrastructure strategy, the Company is positioned to capitalize on the rapidly expanding global demand for AI infrastructure, compute capacity, digital power, and next-generation AI technologies.

For more information please visit: www.azioai.ai and for potential partnerships contact: AI@PhoenixMGMTconsulting.com

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. In some cases, you can identify forward-looking statements by words such as “may,” “will,” “could,” “expect,” “anticipate,” “believe,” “estimate,” “project,” “intend,” “continue,” “potential,” “ongoing,” or the negative of these terms or other comparable terminology, although not all forward-looking statements contain these words. Forward-looking statements include statements regarding the Company’s ability to capitalize on accelerating demand for AI infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; the Company’s plans to continue expanding its digital infrastructure platform through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships; the Company’s ability to maximize utilization of its power resources while creating multiple long-term revenue opportunities; the ability to continue deploying modular digital infrastructure at the Company’s South Texas site; the anticipated deployment and scaling of NVIDIA B200 and B300 GPU systems; the ability to advance and execute against the Company’s commercial infrastructure pipeline; the anticipated development of the Company’s footprint; the ability to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations; customer demand for AI infrastructure, enterprise compute, and digital infrastructure; the Company’s ability to build a scalable platform designed to serve that demand and create long-term shareholder value; and the Company’s broader business strategy and long-term growth objectives.

These statements are based on current expectations and assumptions that involve risks and uncertainties that could cause actual results to differ materially. Most of these factors are outside the Company’s control and are difficult to predict. Factors that may affect actual results include, but are not limited to, the Company’s limited operating history within AI infrastructure and compute operations, project scope, engineering challenges, supply chain constraints, installation timelines, energy availability, finalization of site usage rights, regulatory considerations, equipment performance, ability to raise capital required for expansion activities, changes in digital asset markets, evolving compute demand, market conditions, the Company’s ability to successfully integrate the combined business following the completion of the merger, the risk that the anticipated benefits and synergies of the merger are not realized, the risk of unexpected costs, charges, or expenses resulting from or relating to the merger, potential adverse reactions or changes to business relationships resulting from the completion of the merger, risks related to the diversion of management’s attention from ongoing business operations during the post-closing integration period, the risk that required stockholder approval for the conversion of preferred stock issued in the merger as required by rules of The Nasdaq Stock Market LLC (the “Conversion Proposal”) is not obtained, and additional risks and uncertainties described in the Company’s most recent Annual Report on Form 10-K and subsequent Quarterly Reports on Form 10-Q filed with the SEC, which are available at www.sec.gov. The Company undertakes no obligation to update forward-looking statements except as required by law.

Important Information About the Merger and Where to Find it

The Company expects to file a proxy statement with the SEC relating to the Conversion Proposal. The definitive proxy statement will be sent to all Company stockholders. Before making any voting decision, investors and security-holders of the Company are urged to read the proxy statement and all other relevant documents filed or that will be filed with the SEC in connection with the Conversion Proposal as they become available because they will contain important information about the amended and restated merger agreement between the parties and the related transactions and the Conversion Proposal to be voted upon by the Company’s stockholders. Investors and security-holders will be able to obtain free copies of the proxy statement and all other relevant documents filed or that will be filed with the SEC by the Company through the website maintained by the SEC at www.sec.gov.

Participants in the Solicitation

The Company and its directors and executive officers may be considered participants in the solicitation of proxies from EVTV’s stockholders with respect to the Conversion Proposal under the rules of the SEC. Information about the directors and executive officers of EVTV is set forth in its Annual Report on Form 10-K for the year ended December 31, 2025, which was filed with the SEC on April 13, 2026, and in subsequent Quarterly Reports on Form 10-Q and other documents filed by the Company from time to time with the SEC. Additional information regarding the persons who may be deemed participants in the proxy solicitation and a description of their direct and indirect interests, by security holdings or otherwise, will also be included in the proxy statement, and other relevant materials to be filed with the SEC when they become available. You may obtain free copies of these documents as described above.

¹ Source: International Data Corporation (IDC), “AI Infrastructure Spending Caps Historic Year at ~$90 Billion in Q4 2025; 2029 Spending to Eclipse $1 Trillion,” April 16, 2026. The Company has not independently verified the data or projections contained in this report, and there can be no assurance that the projections will be realized.

Contact

Phoenix MGMT & Consulting

Press@PhoenixMGMTConsulting.com

Revised transaction structure enables immediate closing, accelerating the Company’s strategic pivot toward AI data centers, enterprise GPU compute, and digital power infrastructure.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) (“EVTV” or the “Company”) today announced the successful completion of its merger with Azio AI Corporation (“Azio AI”) on July 2, 2026, paving the way for the Company to transform to an AI Datacenter Provider and meeting the growing market demand for artificial intelligence (“AI”) infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; a market that the International Data Corporation (IDC) projects will reach $487 billion in global spending in 2026 and exceed $1 trillion by 2029.[1] The transaction marks a defining milestone in the Company’s strategic transformation and establishes the foundation for its next phase of commercial execution and long-term growth.

The parties amended the proposed transaction structure to expedite the closing timeline, allowing the combined company to begin operating as a fully integrated public company significantly sooner than originally anticipated. The accelerated closing enables management to immediately focus on commercialization across its expanding AI Datacenter strategy.

With the merger complete and the combined company operating as one organization, management is now fully focused on commercial execution, infrastructure deployment, strategic growth initiatives, and creating long-term shareholder value.

Over the past several months, the Company advanced development activities at its South Texas site and deployed six megawatts of off-grid power for its modular data centers. The Company further secured rights to a 548-acre site with the capacity to scale up to 500 MW, supporting the future development of AI hyperscale data centers.

Management believes these achievements demonstrate that the combined company is entering its next phase with meaningful operational momentum already in place rather than beginning from a standing start. Infrastructure deployment is underway, customer commitments have already been established, commercial execution is actively progressing, and the Company’s corporate structure is now aligned with an operating platform built to support long-term expansion.

The completion of the merger comes at a time when investment in AI infrastructure continues to accelerate globally as enterprises increasingly require access to high-performance computing resources, GPU infrastructure, and scalable digital power solutions. Management believes the combined company is well positioned to capitalize on these long-term industry trends through a diversified infrastructure strategy designed to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations.

Following the closing of the transaction, the Company intends to continue expanding its AI Infrastructure strategy through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships designed to maximize utilization of its power resources while creating multiple long-term revenue opportunities.

In connection with the closing of the merger, Phillip Oldridge has stepped down as Chief Executive Officer. Jason Maddox vacates the President position and is now the Chief Financial Officer. The Company’s Board of Directors appointed Simon Yu as President and Chris Young as Chief Executive Officer, effective immediately.

Mr. Yu is a serial entrepreneur and public markets operator with almost a decade of experience taking companies public, executing capital raises, and scaling businesses. He has previously served in founder, C-suite, and board roles at three publicly traded companies, two of which reached market capitalizations in excess of $1 billion. Mr. Yu has led legal, accounting, and advisory teams through Regulation A+ Tier 2 offerings, PCAOB audits, and public company reporting, alongside leading M&A transactions. As an active early-stage venture investor, he has evaluated investment opportunities across artificial intelligence, SaaS, and B2B technology.

Mr. Young brings extensive experience in launching and leading public companies and investing in and advising emerging technology companies, with a particular focus on artificial intelligence, software innovation, and strategic growth initiatives. Prior to joining EVTV, he served as Chief Executive Officer of Clubhouse Media Group, a publicly traded social media company and an Entrepreneur in Residence at Amplify, where he worked alongside founders and venture-backed technology companies to accelerate commercialization and support the development of high-growth technology businesses.

“Today’s announcement represents far more than the completion of a merger—it marks the beginning of our next chapter,” said Chris Young, Chief Executive Officer of EVTV. “Over the past several months, our teams have been building the operational foundation of this business while simultaneously working toward completing this transaction. With the merger now finalized, we move forward as one company with one leadership team and one strategy, focused on executing against the opportunities in front of us. We believe demand for AI infrastructure, enterprise compute, and digital infrastructure will continue expanding for years to come. Our objective is to build a scalable platform capable of serving that demand while creating long-term value for our shareholders.”

Jason Maddox, Chief Financial Officer of EVTV, added, “Completing this transaction under the amended merger structure allows us to immediately focus on execution. We have already established meaningful operational momentum, and we believe operating as a unified public company enhances our ability to deploy infrastructure, serve customers, pursue strategic growth opportunities, and continue building long-term shareholder value.”

The transaction establishes a unified operating platform designed to support the Company’s long-term growth strategy through continued investment in AI infrastructure, enterprise computing, digital power assets, and digital infrastructure development. Management believes the completion of the merger provides the operational and organizational foundation necessary to pursue the next phase of commercialization while expanding its presence across some of the fastest-growing sectors of the global technology market.

Transaction and Operational Highlights

• Successfully completed the merger with Azio AI pursuant to an amended and restated merger agreement.
• Approximately six megawatts of off-grid digital infrastructure deployed at the Company’s South Texas development site.
• Development footprint exceeding 548 acres with the potential to support up to 500 MW of AI infrastructure capacity.
• Combined company positioned to accelerate commercialization across AI infrastructure, enterprise GPU compute, digital power solutions, and digital asset mining operations.
• Merger consideration consisted of 2,655,157 shares of common stock and 973,450 shares of non-voting convertible preferred stock in exchange for 100% of outstanding capital stock of Azio AI, of which 194,807 shares of common stock were reserved for convertible notes of Azio AI assumed by the Company upon closing.
• Each share of preferred stock convertible into 100 shares of Company common stock subject to stockholder approval.
• Chris Young appointed Chief Executive Officer and Chairman of the Board.
• Simon Yu appointed President.
• Jason Maddox appointed Chief Financial Officer.
• Phillip Oldridge stepped down as Chief Executive Officer.

About Envirotech Vehicles, Inc.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) is a technology infrastructure company focused on developing, owning, and operating artificial intelligence data centers, enterprise GPU compute infrastructure, digital power solutions, and digital asset mining operations. Following its acquisition of Azio AI, the Company operates an integrated AI infrastructure business encompassing AI data center development, the sale and distribution of enterprise GPU systems and server infrastructure, high-performance computing solutions, power hosting, and strategic technology investments, serving enterprise and institutional customers across domestic and international markets. Through this diversified AI infrastructure strategy, the Company is positioned to capitalize on the rapidly expanding global demand for AI infrastructure, compute capacity, digital power, and next-generation AI technologies.

For more information please visit: www.azioai.ai and for potential partnerships contact: AI@PhoenixMGMTconsulting.com

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. In some cases, you can identify forward-looking statements by words such as “may,” “will,” “could,” “expect,” “anticipate,” “believe,” “estimate,” “project,” “intend,” “continue,” “potential,” “ongoing,” or the negative of these terms or other comparable terminology, although not all forward-looking statements contain these words. Forward-looking statements include statements regarding the Company’s ability to capitalize on accelerating demand for AI infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; the Company’s plans to continue expanding its digital infrastructure platform through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships; the Company’s ability to maximize utilization of its power resources while creating multiple long-term revenue opportunities; the ability to continue deploying modular digital infrastructure at the Company’s South Texas site; the anticipated deployment and scaling of NVIDIA B200 and B300 GPU systems; the ability to advance and execute against the Company’s commercial infrastructure pipeline; the anticipated development of the Company’s footprint; the ability to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations; customer demand for AI infrastructure, enterprise compute, and digital infrastructure; the Company’s ability to build a scalable platform designed to serve that demand and create long-term shareholder value; and the Company’s broader business strategy and long-term growth objectives.

These statements are based on current expectations and assumptions that involve risks and uncertainties that could cause actual results to differ materially. Most of these factors are outside the Company’s control and are difficult to predict. Factors that may affect actual results include, but are not limited to, the Company’s limited operating history within AI infrastructure and compute operations, project scope, engineering challenges, supply chain constraints, installation timelines, energy availability, finalization of site usage rights, regulatory considerations, equipment performance, ability to raise capital required for expansion activities, changes in digital asset markets, evolving compute demand, market conditions, the Company’s ability to successfully integrate the combined business following the completion of the merger, the risk that the anticipated benefits and synergies of the merger are not realized, the risk of unexpected costs, charges, or expenses resulting from or relating to the merger, potential adverse reactions or changes to business relationships resulting from the completion of the merger, risks related to the diversion of management’s attention from ongoing business operations during the post-closing integration period, the risk that required stockholder approval for the conversion of preferred stock issued in the merger as required by rules of The Nasdaq Stock Market LLC (the “Conversion Proposal”) is not obtained, and additional risks and uncertainties described in the Company’s most recent Annual Report on Form 10-K and subsequent Quarterly Reports on Form 10-Q filed with the SEC, which are available at www.sec.gov. The Company undertakes no obligation to update forward-looking statements except as required by law.

Important Information About the Merger and Where to Find it

The Company expects to file a proxy statement with the SEC relating to the Conversion Proposal. The definitive proxy statement will be sent to all Company stockholders. Before making any voting decision, investors and security-holders of the Company are urged to read the proxy statement and all other relevant documents filed or that will be filed with the SEC in connection with the Conversion Proposal as they become available because they will contain important information about the amended and restated merger agreement between the parties and the related transactions and the Conversion Proposal to be voted upon by the Company’s stockholders. Investors and security-holders will be able to obtain free copies of the proxy statement and all other relevant documents filed or that will be filed with the SEC by the Company through the website maintained by the SEC at www.sec.gov.

Participants in the Solicitation

The Company and its directors and executive officers may be considered participants in the solicitation of proxies from EVTV’s stockholders with respect to the Conversion Proposal under the rules of the SEC. Information about the directors and executive officers of EVTV is set forth in its Annual Report on Form 10-K for the year ended December 31, 2025, which was filed with the SEC on April 13, 2026, and in subsequent Quarterly Reports on Form 10-Q and other documents filed by the Company from time to time with the SEC. Additional information regarding the persons who may be deemed participants in the proxy solicitation and a description of their direct and indirect interests, by security holdings or otherwise, will also be included in the proxy statement, and other relevant materials to be filed with the SEC when they become available. You may obtain free copies of these documents as described above.

¹ Source: International Data Corporation (IDC), “AI Infrastructure Spending Caps Historic Year at ~$90 Billion in Q4 2025; 2029 Spending to Eclipse $1 Trillion,” April 16, 2026. The Company has not independently verified the data or projections contained in this report, and there can be no assurance that the projections will be realized.

Contact

Phoenix MGMT & Consulting

Press@PhoenixMGMTConsulting.com

Revised transaction structure enables immediate closing, accelerating the Company’s strategic pivot toward AI data centers, enterprise GPU compute, and digital power infrastructure.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) (“EVTV” or the “Company”) today announced the successful completion of its merger with Azio AI Corporation (“Azio AI”) on July 2, 2026, paving the way for the Company to transform to an AI Datacenter Provider and meeting the growing market demand for artificial intelligence (“AI”) infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; a market that the International Data Corporation (IDC) projects will reach $487 billion in global spending in 2026 and exceed $1 trillion by 2029.[1] The transaction marks a defining milestone in the Company’s strategic transformation and establishes the foundation for its next phase of commercial execution and long-term growth.

The parties amended the proposed transaction structure to expedite the closing timeline, allowing the combined company to begin operating as a fully integrated public company significantly sooner than originally anticipated. The accelerated closing enables management to immediately focus on commercialization across its expanding AI Datacenter strategy.

With the merger complete and the combined company operating as one organization, management is now fully focused on commercial execution, infrastructure deployment, strategic growth initiatives, and creating long-term shareholder value.

Over the past several months, the Company advanced development activities at its South Texas site and deployed six megawatts of off-grid power for its modular data centers. The Company further secured rights to a 548-acre site with the capacity to scale up to 500 MW, supporting the future development of AI hyperscale data centers.

Management believes these achievements demonstrate that the combined company is entering its next phase with meaningful operational momentum already in place rather than beginning from a standing start. Infrastructure deployment is underway, customer commitments have already been established, commercial execution is actively progressing, and the Company’s corporate structure is now aligned with an operating platform built to support long-term expansion.

The completion of the merger comes at a time when investment in AI infrastructure continues to accelerate globally as enterprises increasingly require access to high-performance computing resources, GPU infrastructure, and scalable digital power solutions. Management believes the combined company is well positioned to capitalize on these long-term industry trends through a diversified infrastructure strategy designed to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations.

Following the closing of the transaction, the Company intends to continue expanding its AI Infrastructure strategy through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships designed to maximize utilization of its power resources while creating multiple long-term revenue opportunities.

In connection with the closing of the merger, Phillip Oldridge has stepped down as Chief Executive Officer. Jason Maddox vacates the President position and is now the Chief Financial Officer. The Company’s Board of Directors appointed Simon Yu as President and Chris Young as Chief Executive Officer, effective immediately.

Mr. Yu is a serial entrepreneur and public markets operator with almost a decade of experience taking companies public, executing capital raises, and scaling businesses. He has previously served in founder, C-suite, and board roles at three publicly traded companies, two of which reached market capitalizations in excess of $1 billion. Mr. Yu has led legal, accounting, and advisory teams through Regulation A+ Tier 2 offerings, PCAOB audits, and public company reporting, alongside leading M&A transactions. As an active early-stage venture investor, he has evaluated investment opportunities across artificial intelligence, SaaS, and B2B technology.

Mr. Young brings extensive experience in launching and leading public companies and investing in and advising emerging technology companies, with a particular focus on artificial intelligence, software innovation, and strategic growth initiatives. Prior to joining EVTV, he served as Chief Executive Officer of Clubhouse Media Group, a publicly traded social media company and an Entrepreneur in Residence at Amplify, where he worked alongside founders and venture-backed technology companies to accelerate commercialization and support the development of high-growth technology businesses.

“Today’s announcement represents far more than the completion of a merger—it marks the beginning of our next chapter,” said Chris Young, Chief Executive Officer of EVTV. “Over the past several months, our teams have been building the operational foundation of this business while simultaneously working toward completing this transaction. With the merger now finalized, we move forward as one company with one leadership team and one strategy, focused on executing against the opportunities in front of us. We believe demand for AI infrastructure, enterprise compute, and digital infrastructure will continue expanding for years to come. Our objective is to build a scalable platform capable of serving that demand while creating long-term value for our shareholders.”

Jason Maddox, Chief Financial Officer of EVTV, added, “Completing this transaction under the amended merger structure allows us to immediately focus on execution. We have already established meaningful operational momentum, and we believe operating as a unified public company enhances our ability to deploy infrastructure, serve customers, pursue strategic growth opportunities, and continue building long-term shareholder value.”

The transaction establishes a unified operating platform designed to support the Company’s long-term growth strategy through continued investment in AI infrastructure, enterprise computing, digital power assets, and digital infrastructure development. Management believes the completion of the merger provides the operational and organizational foundation necessary to pursue the next phase of commercialization while expanding its presence across some of the fastest-growing sectors of the global technology market.

Transaction and Operational Highlights

• Successfully completed the merger with Azio AI pursuant to an amended and restated merger agreement.
• Approximately six megawatts of off-grid digital infrastructure deployed at the Company’s South Texas development site.
• Development footprint exceeding 548 acres with the potential to support up to 500 MW of AI infrastructure capacity.
• Combined company positioned to accelerate commercialization across AI infrastructure, enterprise GPU compute, digital power solutions, and digital asset mining operations.
• Merger consideration consisted of 2,655,157 shares of common stock and 973,450 shares of non-voting convertible preferred stock in exchange for 100% of outstanding capital stock of Azio AI, of which 194,807 shares of common stock were reserved for convertible notes of Azio AI assumed by the Company upon closing.
• Each share of preferred stock convertible into 100 shares of Company common stock subject to stockholder approval.
• Chris Young appointed Chief Executive Officer and Chairman of the Board.
• Simon Yu appointed President.
• Jason Maddox appointed Chief Financial Officer.
• Phillip Oldridge stepped down as Chief Executive Officer.

About Envirotech Vehicles, Inc.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) is a technology infrastructure company focused on developing, owning, and operating artificial intelligence data centers, enterprise GPU compute infrastructure, digital power solutions, and digital asset mining operations. Following its acquisition of Azio AI, the Company operates an integrated AI infrastructure business encompassing AI data center development, the sale and distribution of enterprise GPU systems and server infrastructure, high-performance computing solutions, power hosting, and strategic technology investments, serving enterprise and institutional customers across domestic and international markets. Through this diversified AI infrastructure strategy, the Company is positioned to capitalize on the rapidly expanding global demand for AI infrastructure, compute capacity, digital power, and next-generation AI technologies.

For more information please visit: www.azioai.ai and for potential partnerships contact: AI@PhoenixMGMTconsulting.com

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. In some cases, you can identify forward-looking statements by words such as “may,” “will,” “could,” “expect,” “anticipate,” “believe,” “estimate,” “project,” “intend,” “continue,” “potential,” “ongoing,” or the negative of these terms or other comparable terminology, although not all forward-looking statements contain these words. Forward-looking statements include statements regarding the Company’s ability to capitalize on accelerating demand for AI infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; the Company’s plans to continue expanding its digital infrastructure platform through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships; the Company’s ability to maximize utilization of its power resources while creating multiple long-term revenue opportunities; the ability to continue deploying modular digital infrastructure at the Company’s South Texas site; the anticipated deployment and scaling of NVIDIA B200 and B300 GPU systems; the ability to advance and execute against the Company’s commercial infrastructure pipeline; the anticipated development of the Company’s footprint; the ability to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations; customer demand for AI infrastructure, enterprise compute, and digital infrastructure; the Company’s ability to build a scalable platform designed to serve that demand and create long-term shareholder value; and the Company’s broader business strategy and long-term growth objectives.

These statements are based on current expectations and assumptions that involve risks and uncertainties that could cause actual results to differ materially. Most of these factors are outside the Company’s control and are difficult to predict. Factors that may affect actual results include, but are not limited to, the Company’s limited operating history within AI infrastructure and compute operations, project scope, engineering challenges, supply chain constraints, installation timelines, energy availability, finalization of site usage rights, regulatory considerations, equipment performance, ability to raise capital required for expansion activities, changes in digital asset markets, evolving compute demand, market conditions, the Company’s ability to successfully integrate the combined business following the completion of the merger, the risk that the anticipated benefits and synergies of the merger are not realized, the risk of unexpected costs, charges, or expenses resulting from or relating to the merger, potential adverse reactions or changes to business relationships resulting from the completion of the merger, risks related to the diversion of management’s attention from ongoing business operations during the post-closing integration period, the risk that required stockholder approval for the conversion of preferred stock issued in the merger as required by rules of The Nasdaq Stock Market LLC (the “Conversion Proposal”) is not obtained, and additional risks and uncertainties described in the Company’s most recent Annual Report on Form 10-K and subsequent Quarterly Reports on Form 10-Q filed with the SEC, which are available at www.sec.gov. The Company undertakes no obligation to update forward-looking statements except as required by law.

Important Information About the Merger and Where to Find it

The Company expects to file a proxy statement with the SEC relating to the Conversion Proposal. The definitive proxy statement will be sent to all Company stockholders. Before making any voting decision, investors and security-holders of the Company are urged to read the proxy statement and all other relevant documents filed or that will be filed with the SEC in connection with the Conversion Proposal as they become available because they will contain important information about the amended and restated merger agreement between the parties and the related transactions and the Conversion Proposal to be voted upon by the Company’s stockholders. Investors and security-holders will be able to obtain free copies of the proxy statement and all other relevant documents filed or that will be filed with the SEC by the Company through the website maintained by the SEC at www.sec.gov.

Participants in the Solicitation

The Company and its directors and executive officers may be considered participants in the solicitation of proxies from EVTV’s stockholders with respect to the Conversion Proposal under the rules of the SEC. Information about the directors and executive officers of EVTV is set forth in its Annual Report on Form 10-K for the year ended December 31, 2025, which was filed with the SEC on April 13, 2026, and in subsequent Quarterly Reports on Form 10-Q and other documents filed by the Company from time to time with the SEC. Additional information regarding the persons who may be deemed participants in the proxy solicitation and a description of their direct and indirect interests, by security holdings or otherwise, will also be included in the proxy statement, and other relevant materials to be filed with the SEC when they become available. You may obtain free copies of these documents as described above.

¹ Source: International Data Corporation (IDC), “AI Infrastructure Spending Caps Historic Year at ~$90 Billion in Q4 2025; 2029 Spending to Eclipse $1 Trillion,” April 16, 2026. The Company has not independently verified the data or projections contained in this report, and there can be no assurance that the projections will be realized.

Contact

Phoenix MGMT & Consulting

Press@PhoenixMGMTConsulting.com

Revised transaction structure enables immediate closing, accelerating the Company’s strategic pivot toward AI data centers, enterprise GPU compute, and digital power infrastructure.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) (“EVTV” or the “Company”) today announced the successful completion of its merger with Azio AI Corporation (“Azio AI”) on July 2, 2026, paving the way for the Company to transform to an AI Datacenter Provider and meeting the growing market demand for artificial intelligence (“AI”) infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; a market that the International Data Corporation (IDC) projects will reach $487 billion in global spending in 2026 and exceed $1 trillion by 2029.[1] The transaction marks a defining milestone in the Company’s strategic transformation and establishes the foundation for its next phase of commercial execution and long-term growth.

The parties amended the proposed transaction structure to expedite the closing timeline, allowing the combined company to begin operating as a fully integrated public company significantly sooner than originally anticipated. The accelerated closing enables management to immediately focus on commercialization across its expanding AI Datacenter strategy.

With the merger complete and the combined company operating as one organization, management is now fully focused on commercial execution, infrastructure deployment, strategic growth initiatives, and creating long-term shareholder value.

Over the past several months, the Company advanced development activities at its South Texas site and deployed six megawatts of off-grid power for its modular data centers. The Company further secured rights to a 548-acre site with the capacity to scale up to 500 MW, supporting the future development of AI hyperscale data centers.

Management believes these achievements demonstrate that the combined company is entering its next phase with meaningful operational momentum already in place rather than beginning from a standing start. Infrastructure deployment is underway, customer commitments have already been established, commercial execution is actively progressing, and the Company’s corporate structure is now aligned with an operating platform built to support long-term expansion.

The completion of the merger comes at a time when investment in AI infrastructure continues to accelerate globally as enterprises increasingly require access to high-performance computing resources, GPU infrastructure, and scalable digital power solutions. Management believes the combined company is well positioned to capitalize on these long-term industry trends through a diversified infrastructure strategy designed to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations.

Following the closing of the transaction, the Company intends to continue expanding its AI Infrastructure strategy through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships designed to maximize utilization of its power resources while creating multiple long-term revenue opportunities.

In connection with the closing of the merger, Phillip Oldridge has stepped down as Chief Executive Officer. Jason Maddox vacates the President position and is now the Chief Financial Officer. The Company’s Board of Directors appointed Simon Yu as President and Chris Young as Chief Executive Officer, effective immediately.

Mr. Yu is a serial entrepreneur and public markets operator with almost a decade of experience taking companies public, executing capital raises, and scaling businesses. He has previously served in founder, C-suite, and board roles at three publicly traded companies, two of which reached market capitalizations in excess of $1 billion. Mr. Yu has led legal, accounting, and advisory teams through Regulation A+ Tier 2 offerings, PCAOB audits, and public company reporting, alongside leading M&A transactions. As an active early-stage venture investor, he has evaluated investment opportunities across artificial intelligence, SaaS, and B2B technology.

Mr. Young brings extensive experience in launching and leading public companies and investing in and advising emerging technology companies, with a particular focus on artificial intelligence, software innovation, and strategic growth initiatives. Prior to joining EVTV, he served as Chief Executive Officer of Clubhouse Media Group, a publicly traded social media company and an Entrepreneur in Residence at Amplify, where he worked alongside founders and venture-backed technology companies to accelerate commercialization and support the development of high-growth technology businesses.

“Today’s announcement represents far more than the completion of a merger—it marks the beginning of our next chapter,” said Chris Young, Chief Executive Officer of EVTV. “Over the past several months, our teams have been building the operational foundation of this business while simultaneously working toward completing this transaction. With the merger now finalized, we move forward as one company with one leadership team and one strategy, focused on executing against the opportunities in front of us. We believe demand for AI infrastructure, enterprise compute, and digital infrastructure will continue expanding for years to come. Our objective is to build a scalable platform capable of serving that demand while creating long-term value for our shareholders.”

Jason Maddox, Chief Financial Officer of EVTV, added, “Completing this transaction under the amended merger structure allows us to immediately focus on execution. We have already established meaningful operational momentum, and we believe operating as a unified public company enhances our ability to deploy infrastructure, serve customers, pursue strategic growth opportunities, and continue building long-term shareholder value.”

The transaction establishes a unified operating platform designed to support the Company’s long-term growth strategy through continued investment in AI infrastructure, enterprise computing, digital power assets, and digital infrastructure development. Management believes the completion of the merger provides the operational and organizational foundation necessary to pursue the next phase of commercialization while expanding its presence across some of the fastest-growing sectors of the global technology market.

Transaction and Operational Highlights

• Successfully completed the merger with Azio AI pursuant to an amended and restated merger agreement.
• Approximately six megawatts of off-grid digital infrastructure deployed at the Company’s South Texas development site.
• Development footprint exceeding 548 acres with the potential to support up to 500 MW of AI infrastructure capacity.
• Combined company positioned to accelerate commercialization across AI infrastructure, enterprise GPU compute, digital power solutions, and digital asset mining operations.
• Merger consideration consisted of 2,655,157 shares of common stock and 973,450 shares of non-voting convertible preferred stock in exchange for 100% of outstanding capital stock of Azio AI, of which 194,807 shares of common stock were reserved for convertible notes of Azio AI assumed by the Company upon closing.
• Each share of preferred stock convertible into 100 shares of Company common stock subject to stockholder approval.
• Chris Young appointed Chief Executive Officer and Chairman of the Board.
• Simon Yu appointed President.
• Jason Maddox appointed Chief Financial Officer.
• Phillip Oldridge stepped down as Chief Executive Officer.

About Envirotech Vehicles, Inc.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) is a technology infrastructure company focused on developing, owning, and operating artificial intelligence data centers, enterprise GPU compute infrastructure, digital power solutions, and digital asset mining operations. Following its acquisition of Azio AI, the Company operates an integrated AI infrastructure business encompassing AI data center development, the sale and distribution of enterprise GPU systems and server infrastructure, high-performance computing solutions, power hosting, and strategic technology investments, serving enterprise and institutional customers across domestic and international markets. Through this diversified AI infrastructure strategy, the Company is positioned to capitalize on the rapidly expanding global demand for AI infrastructure, compute capacity, digital power, and next-generation AI technologies.

For more information please visit: www.azioai.ai and for potential partnerships contact: AI@PhoenixMGMTconsulting.com

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. In some cases, you can identify forward-looking statements by words such as “may,” “will,” “could,” “expect,” “anticipate,” “believe,” “estimate,” “project,” “intend,” “continue,” “potential,” “ongoing,” or the negative of these terms or other comparable terminology, although not all forward-looking statements contain these words. Forward-looking statements include statements regarding the Company’s ability to capitalize on accelerating demand for AI infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; the Company’s plans to continue expanding its digital infrastructure platform through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships; the Company’s ability to maximize utilization of its power resources while creating multiple long-term revenue opportunities; the ability to continue deploying modular digital infrastructure at the Company’s South Texas site; the anticipated deployment and scaling of NVIDIA B200 and B300 GPU systems; the ability to advance and execute against the Company’s commercial infrastructure pipeline; the anticipated development of the Company’s footprint; the ability to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations; customer demand for AI infrastructure, enterprise compute, and digital infrastructure; the Company’s ability to build a scalable platform designed to serve that demand and create long-term shareholder value; and the Company’s broader business strategy and long-term growth objectives.

These statements are based on current expectations and assumptions that involve risks and uncertainties that could cause actual results to differ materially. Most of these factors are outside the Company’s control and are difficult to predict. Factors that may affect actual results include, but are not limited to, the Company’s limited operating history within AI infrastructure and compute operations, project scope, engineering challenges, supply chain constraints, installation timelines, energy availability, finalization of site usage rights, regulatory considerations, equipment performance, ability to raise capital required for expansion activities, changes in digital asset markets, evolving compute demand, market conditions, the Company’s ability to successfully integrate the combined business following the completion of the merger, the risk that the anticipated benefits and synergies of the merger are not realized, the risk of unexpected costs, charges, or expenses resulting from or relating to the merger, potential adverse reactions or changes to business relationships resulting from the completion of the merger, risks related to the diversion of management’s attention from ongoing business operations during the post-closing integration period, the risk that required stockholder approval for the conversion of preferred stock issued in the merger as required by rules of The Nasdaq Stock Market LLC (the “Conversion Proposal”) is not obtained, and additional risks and uncertainties described in the Company’s most recent Annual Report on Form 10-K and subsequent Quarterly Reports on Form 10-Q filed with the SEC, which are available at www.sec.gov. The Company undertakes no obligation to update forward-looking statements except as required by law.

Important Information About the Merger and Where to Find it

The Company expects to file a proxy statement with the SEC relating to the Conversion Proposal. The definitive proxy statement will be sent to all Company stockholders. Before making any voting decision, investors and security-holders of the Company are urged to read the proxy statement and all other relevant documents filed or that will be filed with the SEC in connection with the Conversion Proposal as they become available because they will contain important information about the amended and restated merger agreement between the parties and the related transactions and the Conversion Proposal to be voted upon by the Company’s stockholders. Investors and security-holders will be able to obtain free copies of the proxy statement and all other relevant documents filed or that will be filed with the SEC by the Company through the website maintained by the SEC at www.sec.gov.

Participants in the Solicitation

The Company and its directors and executive officers may be considered participants in the solicitation of proxies from EVTV’s stockholders with respect to the Conversion Proposal under the rules of the SEC. Information about the directors and executive officers of EVTV is set forth in its Annual Report on Form 10-K for the year ended December 31, 2025, which was filed with the SEC on April 13, 2026, and in subsequent Quarterly Reports on Form 10-Q and other documents filed by the Company from time to time with the SEC. Additional information regarding the persons who may be deemed participants in the proxy solicitation and a description of their direct and indirect interests, by security holdings or otherwise, will also be included in the proxy statement, and other relevant materials to be filed with the SEC when they become available. You may obtain free copies of these documents as described above.

¹ Source: International Data Corporation (IDC), “AI Infrastructure Spending Caps Historic Year at ~$90 Billion in Q4 2025; 2029 Spending to Eclipse $1 Trillion,” April 16, 2026. The Company has not independently verified the data or projections contained in this report, and there can be no assurance that the projections will be realized.

Contact

Phoenix MGMT & Consulting

Press@PhoenixMGMTConsulting.com

You can be forgiven if you think the most important thing AWS ever sold developers was EC2. It’s not. No, AWS’s big gift to developers was permission to stop fretting about servers. That sounds obvious now, but it was close to magical at the time. Before the cloud, getting infrastructure meant waiting on procurement, hardware, and the somewhat arcane process that stood between a developer and a running machine. AWS turned that into a credit card and an API.

It was awesome.

AWS still (over)uses a great phrase for what it removed: “undifferentiated heavy lifting.” That is, all the mess associated with racking servers, patching operating systems, managing storage, planning capacity, etc. Important work, sure, but not the work that makes your application special. Let AWS do that, the company intoned, and developers could focus on the thing their customers actually cared about.

It was a brilliant abstraction. It helped build one of the most important technology companies of the past two decades. It’s also the same logic that increasingly makes the cloud seem superfluous. Not because the cloud is dying. It isn’t. The cloud is bigger and more essential than ever. But developers don’t begin by asking, “Which cloud should I use?” They begin with, “How quickly can I get this thing working?”

That is a different question, and it leads to different tools.

Where to begin?

Let’s get the obvious thing out of the way: AWS remains the biggest developer cloud, and its revenue growth has accelerated in the wake of AI. In the 2024 Stack Overflow Developer Survey⁠, AWS was the top cloud platform, used by 48% of respondents. Microsoft Azure and Google Cloud followed at 27.8% and 25.1%, respectively.

The 2025 Stack Overflow Developer survey⁠ is more interesting, however, because the “cloud development” category no longer reads like a clear cloud-vendor horse race. Docker jumped 17 points to 71% usage, followed by npm and then AWS at 43%. Kubernetes, Azure, Google Cloud, Cloudflare, Terraform, Firebase, Vercel, Netlify, and Supabase all show up in the same mental map.

This is my point: Developers aren’t simply choosing a different cloud first. Often, they’re not choosing a cloud first at all. They’re choosing a workflow, and a workflow is an increasingly separate decision from the underlying cloud.

The first mile moved

For a long time, AWS owned the first mile. If you wanted to build something, you opened an AWS account. Need compute? Launch EC2. Storage? S3. Whatever a developer needed—database, queue, function, CDN, identity layer—AWS, the “everything store,” almost certainly had an answer.

It was magical! Then it became the norm. And, over time, it became a lot. The “everything store” arguably had too much, making it hard for developers to know how to use it effectively. The developer’s question became, “How much AWS do I have to understand before I can ship?” Developers increasingly don’t want to answer that question at the start.

The first mile now often begins in GitHub⁠, which says more than 36 million developers joined in a single year. Or it begins in VS Code, which keeps winning because it’s familiar, extensible, and already open. Or it begins in Cursor, GitHub Copilot, Claude, Codex, and the broader AI coding layer. GitHub’s 2025 Octoverse⁠ says nearly 80% of new developers on GitHub use Copilot within their first week. Stack Overflow’s 2025 AI survey results⁠ point the same way, with 84% of respondents using or planning to use AI tools in their development process, up from 76% in 2024.

The starting point is changing. The default interface to building software is becoming an AI-assisted workflow, not a cloud console. This doesn’t make the cloud less important, but it definitely makes it less visible.

The joy of not caring

The developer platforms with momentum right now are winning because they expose less cloud infrastructure. Call it serverless if you like, but it’s deeper than AWS Lambda ever was. AWS Lambda still made you think in AWS. These new platforms make you think in your app.

Take Vercel⁠. It didn’t win developer mindshare by offering more than 200 services and praying developers would navigate them all. Instead, it attached itself to the way many front-end and full-stack developers already work: GitHub, Next.js, previews, deployments, performance, and a short path from code to live application. It seems to be working: Reuters reported last year that Vercel raised $300 million at a $9.3 billion valuation⁠, after doubling its user base and growing revenue 82%.

Cloudflare offers a similar promise from a different angle, as I’ve written: deploy globally, run close to users, scale automatically, and don’t make infrastructure the developer’s first problem. Supabase⁠ does the same for data, wrapping Postgres with authentication, instant APIs, edge functions, real-time subscriptions, storage, and vectors so the database feels less like a separate system and more like an app platform.

None of this is anti-cloud. But it’s very definitely stripping away the need to even think about cloud. And it’s just as definitely where developers are focused today.

AI accelerated all of this because it changed the first question developers ask. For an AI application, the opening move often isn’t where do you host it, but rather which model, which agent framework, etc., will get you to a live application fastest? For this reason, OpenAI’s developer platform⁠ isn’t framed as renting infrastructure. It’s framed as building with models, APIs, tools, docs, and examples. Similarly, Anthropic’s Claude Code⁠ isn’t positioned as configuring a cloud environment; it’s an agentic coding system that works in your code base and helps you build, test, and ship.

The second mile matters more

I’m not saying the cloud vendors should abandon hope. Indeed, though the first mile has moved away from the hyperscalers, the second mile is still very much theirs to win.

Vercel, Cloudflare, Supabase, OpenAI, Anthropic, and GitHub are brilliant at helping developers make something work, fast. But in the enterprise, “fast” isn’t the key priority: Enterprises eventually need the boring stuff like identity, network controls, observability, cost management, compliance, and the rest of the unglamorous apparatus that keeps customer data from leaking onto Reddit.

Boring is good when boring means the thing works.

This is where AWS, Azure, Google Cloud, Oracle, and other infrastructure companies should have an advantage. But it won’t be enough to say, “We have the grown-up features.” That’s true, but it’s dull, and in this case, “dull” isn’t a winning proposition. No, the strategic challenge for the hyperscalers is to make the jump from prototype to production feel like an upgrade instead of a punishment.

AWS’s new $1 billion investment in forward-deployed AI engineers⁠ is interesting for exactly this reason. AWS says the new organization will embed experts with customers to co-develop and deploy agentic AI solutions in days. That’s not classic bottom-up developer love, but it’s a smart recognition that the bottleneck has moved closer to the application. The hard part is turning capability into a working system, not merely getting access to infrastructure or a model.

Yep. Exactly.

Hiding the cloud

The mistake for cloud infrastructure companies would be to respond to this by trying to become a Vercel, Supabase, GitHub, OpenAI, or Anthropic all at once. That’s the classic incumbent move, and it rarely works. The better move is narrower: become the most natural next step when the prototype starts to matter.

That means meeting developers where they work: GitHub, VS Code, Cursor-style environments, CI/CD systems, and AI agents. It means outcome-native starts: deploy this app, connect this data, expose this API, add auth, evaluate this agent, secure this workflow, move this prototype into production. It means fewer scavenger hunts across product pages and establishing more golden paths (yes, even if that infuriates a service team that gets left out).

The old question was, “Can you run my workload?” For AWS, Azure, Google Cloud, Oracle, and others, the answer is almost always yes. But no one is impressed by this anymore. Therefore, the better question is, “Can you become part of how I build?”

Developers came to the cloud because it was the easiest way to get infrastructure. They’re shifting to developer experience platforms because those are the easiest way to get outcomes. If the cloud providers can become part of the first mile before the Vercels of the world become part of the second mile, they’ll win, and big. If they don’t, well….

A newly disclosed vulnerability in Argo CD is drawing attention to the security risks of GitOps platforms, with researchers warning that the flaw could allow attackers who gain a foothold inside a Kubernetes cluster to execute code and manipulate application deployments.

Security firm Synacktiv said in a report that the flaw affects Argo CD’s repo-server component, which fetches content from Git repositories and generates Kubernetes manifests used to deploy resources in a cluster. Argo CD is one of the most popular Kubernetes tools and is based on the GitOps paradigm.

“Argo CD requires significant privileges within the cluster,” Synacktiv said. “Additionally, it has access to private Git repositories, making it an attractive target for attackers.”

The issue centers on the repo-server’s unauthenticated GenerateManifest gRPC endpoint. Synacktiv said an attacker able to reach that endpoint could supply Kustomize options in a manifest generation request and abuse Kustomize’s Helm-related build options to execute attacker-controlled commands.

Exploitation requires access to both the repo-server gRPC port and the Redis database port, which should not be exposed to users. Argo CD provides Kubernetes network policies designed to prevent that scenario, but those protections are not enabled by default in Helm chart deployments, according to Synacktiv.

In such deployments, compromising a single pod inside the cluster could be enough to give an attacker the internal access needed to exploit the vulnerability.

Synacktiv said it was able to use the flaw to obtain the Redis password from the repo-server environment and access Argo CD’s Redis database. The researchers then manipulated cached deployment data, allowing a malicious manifest to be deployed automatically when Argo CD’s Auto Sync feature was enabled.

If Auto Sync is not enabled, exploitation would require a user to manually sync the application.

Synacktiv publicly disclosed the details on July 1 after first reporting the issue to Argo CD maintainers in January 2025. The vulnerability remains unpatched, and the firm recommended strict Kubernetes network policies to block untrusted pods from reaching the repo-server and Redis services until a fix is available.

Assessing internal cluster exposure

For CISOs, the key question is not only whether Argo CD is exposed to the internet, but whether other workloads inside the Kubernetes cluster can reach its internal services.

“Because the repo-server’s gRPC service does not enforce authentication, any pod that can reach it becomes equivalent to an authenticated attacker,” said Devashri Datta, a cybersecurity researcher. “In a typical cluster, that means any compromised application pod, misconfigured service mesh, or adjacent workload with local code execution can directly query the GenerateManifest endpoint or hit the Redis cache, no internet exposure required.”

Organizations should not equate “not internet-facing” with “low risk,” because modern attacks often begin with the compromise of an internal workload, according to Sakshi Grover, senior research manager for cybersecurity services research at IDC Asia/Pacific.

“CISOs should therefore evaluate which workloads can communicate with the Argo CD control plane, whether east-west traffic is appropriately segmented, and whether unnecessary trust relationships exist between application workloads and GitOps infrastructure,” Grover said. “The assessment should focus on attack paths rather than perimeter exposure.”

Treating GitOps as tier-zero

The flaw also underscores the role GitOps platforms play in controlling software deployment across enterprise infrastructure.

“GitOps engines aren’t utility services; they’re tier-0 control-plane components,” Datta said. “By design, Argo CD holds read access to private repositories, sync/write access to target clusters, and custody of deployment secrets. It sits at the precise intersection of source code, configuration management, and live infrastructure.”

That level of access means an Argo CD compromise may extend beyond a single application. An attacker could turn the platform used to deploy applications into a channel for malicious manifests, while also interfering with auto-sync behavior and extracting credentials cached in supporting systems such as Redis.

A compromise of these platforms could influence software delivery at scale, making them strategic assets that should be subject to stricter governance and privileged access controls similar to those applied to identity platforms and other critical management systems.

The article originally appeared on CSO.

A newly disclosed vulnerability in Argo CD is drawing attention to the security risks of GitOps platforms, with researchers warning that the flaw could allow attackers who gain a foothold inside a Kubernetes cluster to execute code and manipulate application deployments.

Security firm Synacktiv said in a report that the flaw affects Argo CD’s repo-server component, which fetches content from Git repositories and generates Kubernetes manifests used to deploy resources in a cluster. Argo CD is one of the most popular Kubernetes tools and is based on the GitOps paradigm.

“Argo CD requires significant privileges within the cluster,” Synacktiv said. “Additionally, it has access to private Git repositories, making it an attractive target for attackers.”

The issue centers on the repo-server’s unauthenticated GenerateManifest gRPC endpoint. Synacktiv said an attacker able to reach that endpoint could supply Kustomize options in a manifest generation request and abuse Kustomize’s Helm-related build options to execute attacker-controlled commands.

Exploitation requires access to both the repo-server gRPC port and the Redis database port, which should not be exposed to users. Argo CD provides Kubernetes network policies designed to prevent that scenario, but those protections are not enabled by default in Helm chart deployments, according to Synacktiv.

In such deployments, compromising a single pod inside the cluster could be enough to give an attacker the internal access needed to exploit the vulnerability.

Synacktiv said it was able to use the flaw to obtain the Redis password from the repo-server environment and access Argo CD’s Redis database. The researchers then manipulated cached deployment data, allowing a malicious manifest to be deployed automatically when Argo CD’s Auto Sync feature was enabled.

If Auto Sync is not enabled, exploitation would require a user to manually sync the application.

Synacktiv publicly disclosed the details on July 1, 2026, after first reporting the issue to Argo CD maintainers in January 2025. The vulnerability remains unpatched, and the firm recommended strict Kubernetes network policies to block untrusted pods from reaching the repo-server and Redis services until a fix is available.

Assessing internal cluster exposure

For CISOs, the key question is not only whether Argo CD is exposed to the internet, but whether other workloads inside the Kubernetes cluster can reach its internal services.

“Because the repo-server’s gRPC service does not enforce authentication, any pod that can reach it becomes equivalent to an authenticated attacker,” said Devashri Datta, a cybersecurity researcher. “In a typical cluster, that means any compromised application pod, misconfigured service mesh, or adjacent workload with local code execution can directly query the GenerateManifest endpoint or hit the Redis cache, no internet exposure required.”

Organizations should not equate “not internet-facing” with “low risk,” because modern attacks often begin with the compromise of an internal workload, according to Sakshi Grover, senior research manager for cybersecurity services research at IDC Asia/Pacific.

“CISOs should therefore evaluate which workloads can communicate with the Argo CD control plane, whether east-west traffic is appropriately segmented, and whether unnecessary trust relationships exist between application workloads and GitOps infrastructure,” Grover said. “The assessment should focus on attack paths rather than perimeter exposure.”

Treating GitOps as tier-zero

The flaw also underscores the role GitOps platforms play in controlling software deployment across enterprise infrastructure.

“GitOps engines aren’t utility services; they’re tier-0 control-plane components,” Datta said. “By design, Argo CD holds read access to private repositories, sync/write access to target clusters, and custody of deployment secrets. It sits at the precise intersection of source code, configuration management, and live infrastructure.”

That level of access means an Argo CD compromise may extend beyond a single application. An attacker could turn the platform used to deploy applications into a channel for malicious manifests, while also interfering with auto-sync behavior and extracting credentials cached in supporting systems such as Redis.

A compromise of these platforms could influence software delivery at scale, making them strategic assets that should be subject to stricter governance and privileged access controls similar to those applied to identity platforms and other critical management systems.

Autonomous AI engineer agents can deliver software at a scale in multiples of what a human engineering team can do, and that productivity is genuinely valuable. But without proper guardrails at the specification level, these agents can industrialise inefficient infrastructure patterns at the same pace, consistently and at a scale that makes post-deploy remediation impractical. When an agent provisions a three-node GKE cluster using n2-standard-16 machines for a workload a single e2-medium node could handle, or generates a Kubernetes pod spec with 4-CPU and 8GB memory requests for a service that peaks at 200 milli-cores and 256MB, or writes a Dockerfile that pulls a full Ubuntu base image where a distro-less container would serve, infrastructure runs that decision continuously, for the lifetime of the service. The agent will reproduce these patterns across every environment it touches, because the specification never instructed it otherwise. When agentic pipelines are generating infrastructure at scale, operational remediation after the fact becomes impractical.

The scale of what is now being generated autonomously is significant. InfoWorld’s reporting on AI-driven development shows the pace of AI-generated output is accelerating sharply, and projections suggest more than a quarter of new production code and configuration is already AI-generated. What those projections do not yet capture is the shift from AI-assisted to fully agentic pipelines, where agents generate Terraform, Kubernetes manifests, Helm charts and Docker configurations end-to-end, commit them and trigger deployment, with no human in the loop or little oversight that concentrates on functional capabilities. When that pipeline runs without sustainability constraints, it systematically reproduces that infrastructure inefficiency across every environment it touches.

Green software has traditionally been an operational problem: Right-size the containers retrospectively, tune the cluster after the fact, schedule workloads in low-carbon windows. That approach was already struggling before agentic pipelines arrived. Gartner projects that by 2027, just 30% of large enterprises will have software sustainability embedded in their non-functional requirements. That statistic carries a consequence most engineering leaders have not yet confronted: If 70% of enterprise code has been written without sustainability intent, then the training data autonomous AI engineer agents learned from is dominated by potentially unsustainable patterns. An agent defaults to the majority pattern in its training distribution, which is the inefficient one. This makes the specification constraint not just a governance need, but a corrective instruction that the agent’s training data never provided.

Sustainable specification as a reliable intervention point

In a fully agentic development pipeline, the specification is not a document an engineer reads before writing code. It is the instruction set the agent executes. It determines which machine types get provisioned, which container base images get selected, how pod resource requests are sized, how storage is allocated and how networking is configured. Every infrastructure decision the agent makes downstream is a function of what the specification permitted or left undefined.

If the specification contains no sustainability constraints, the agent will make infrastructure decisions based on defaults, conventions and training data patterns, none of which are optimised for energy efficiency. An agent prompted to scaffold a GKE-based microservice will, by default, select machine types that ensure availability headroom rather than efficiency. It will size pod resource requests conservatively to avoid out-of-memory conditions from potentially inefficient application code, but not to minimise node utilization. It will pull familiar base images rather than minimal ones. These are not failures of the agent. They are the predictable output of an instruction set that never asked for sustainability.

The fix is to make sustainability a first-class constraint in the specification itself. A constraint such as GS-INFRA-001 (select the smallest GKE machine type that satisfies the workload’s measured resource ceiling, defaulting to e2-medium or smaller) or GS-K8S-001 (set pod CPU requests to measured p95 consumption with a 20% ceiling, not to arbitrary safe values) is a structured policy the agent reads before it generates a single line of Terraform or YAML. The agent does not override it. It executes it. That is the mechanism that makes sustainability structural and automated rather than aspirational.

The infrastructure patterns that matter most

Three infrastructure domains represent the highest-impact targets for sustainability constraints, precisely because autonomous AI engineer agents generate them prolifically and the consequences compound continuously at runtime rather than only when code executes.

The first is IaC and cloud resource provisioning. An agent generating a Terraform configuration for a GKE cluster defaults to instance families and node counts calibrated for resilience, not efficiency. A three-node cluster of n2-standard-16 machines (64 vCPUs, 192GB RAM) provisioned for a service that runs comfortably on a single e2-medium (2 vCPUs, 4GB RAM) represents a 32x over-provisioning of compute. That gap does not show up in staging. It runs in production, is billed continuously, emitting continuously. A sustainability constraint in the Terraform specification that enforces machine type selection against a measured workload profile eliminates this class of error before the agent writes its first resource block.

The second is the Kubernetes pod resource configuration. Pod resource requests are the input the Kubernetes scheduler uses to place workloads on nodes. When an autonomous AI engineer agent generates a pod spec with generous CPU and memory requests, the scheduler reserves that capacity whether the pod uses it or not. Nodes that could host eight efficiently-sized pods instead host two or three over-specified ones, leaving the remaining capacity stranded and the underlying VM running at low utilization. A pod spec with a 4-CPU, 8GB memory request for a service that observably consumes 200 millicores and 256MB at peak is not cautious engineering. It is a scheduler instruction to waste three and a half CPUs and 7.75GB of memory per pod, per node, per hour, across every replica in every environment. A sustainability constraint specifying that pod resource requests must be derived from measured p95 consumption data, not from defaults or intuition, changes this systematically.

The third is the container base image selection. When an agent generates a Dockerfile, it gravitates toward familiar, full-featured base images: Ubuntu, Debian, Python, Node.js. These images are large, carry a significant attack surface and consume more storage, memory and transfer bandwidth than their minimal equivalents. A distroless or Alpine-based image for the same workload can be an order of magnitude smaller. At the scale at which an autonomous AI engineer agent operates, pulling, storing and running bloated base images across hundreds of services is a significant and entirely avoidable infrastructure cost. A constraint specifying distroless or minimal base images as the default, with justification required for exceptions, eliminates the pattern without slowing generation.

4 pipeline stages where constraints are enforced

Embedding constraints in the specification is the intervention. Enforcing them through the pipeline is what makes the intervention reliable. Four stages create the enforcement architecture.

The first stage is generation itself. When sustainability constraints are part of the specification the autonomous AI engineer agent operates from, those constraints shape every artifact the agent produces: Terraform resource blocks, Kubernetes manifests, Helm chart defaults, Dockerfile base image selections. The agent does not reason about sustainability independently. It executes the specification. A well-constrained specification produces sustainable infrastructure by construction, not by review.

The second stage is static analysis. Tools including Checkov, tfsec, KICS and Trivy analyze Terraform, Kubernetes YAML and Dockerfiles against configurable policy rules without modifying the agent or the pipeline architecture. A Checkov policy enforcing the GKE machine type constraint, or a tfsec rule flagging over-provisioned node pools, runs against every artifact the agent generates before it reaches a deployment gate. The violation surfaces as structured CI output the gate acts on. The agent’s output is checked the same way a human engineer’s output would be, consistently, at every commit.

The third stage is the quality gate. Sustainability violations fail the build. They do not generate warnings that an agent pipeline has no mechanism to act on. A gate that blocks deployment on policy violations is the enforcement layer that makes constraints binding rather than advisory. Because the gate operates on artifact output rather than on the agent itself, it is fully autonomous AI engineer agent-agnostic: It does not matter whether the Terraform was generated by Copilot, a custom LLM pipeline, an internal scaffolding agent or a human engineer. The gate evaluates the artifact against the policy. That is the only thing that matters.

The fourth stage is runtime telemetry feeding back into constraint refinement. Actual resource utilization, node efficiency metrics and carbon intensity data from production inform constraint updates at the specification level. A constraint calibrated on design-time estimates tightens over time as empirical data replaces assumptions. The governance model improves continuously rather than stagnating at its initial calibration.

3 steps to start this week

Most engineering organizations already have everything they need to begin. The static analysis toolchain is there: Checkov, tfsec, KICS, Trivy and OPA Conftest all support configurable sustainability policies against Terraform, Kubernetes YAML and Dockerfile artifacts without pipeline replacement. The CI/CD pipeline is there: GitHub Actions, GitLab CI, Jenkins, Tekton and Azure DevOps Pipelines all support blocking quality gates against policy tool outputs. The specification layer is there: Terraform modules, Helm chart value schemas, Kubernetes admission controllers and architectural decision records are already version-controlled in most mature engineering organizations. And critically, this approach is a fully autonomous AI engineer agent-agnostic. The governance layer does not inspect which agent or model generated the infrastructure artifact. It enforces the policy against the output. Whether the Terraform came from a custom agentic pipeline, a Copilot suggestion or a human engineer, the gate applies identically. The only things genuinely missing are the sustainability constraint definitions authored into the specification and the policy rules wired into the CI/CD pipeline to enforce them. Three steps close that gap.

1. Audit your IaC specifications for sustainability constraints. Open an active Terraform module or Helm chart and locate the machine type defaults, pod resource request defaults and base image defaults. For most organizations, these are set to safe, familiar values with no sustainability rationale. Define three constraints: A maximum machine type ceiling for each workload tier, a pod resource request ceiling derived from measured utilization, and a base image policy requiring distro-less or Alpine equivalents. Version control these constraints alongside the specifications they govern.
2. Add one Checkov or tfsec policy to your CI pipeline. A policy flagging GKE node pools configured above the e2-standard-4 threshold without a documented justification is implementable in under an hour using Checkov’s custom check API. Wire it as a blocking gate, not a warning. This single addition creates immediate, agent-agnostic enforcement across every Terraform commit in your repository.
3. Embed sustainability constraints before you scale your agentic pipelines. The highest-leverage moment is now, before autonomous AI engineer agents are generating infrastructure at full organizational scale. Every agentic pipeline that goes into production without sustainability constraints in its specification becomes a systematic source of over-provisioned, carbon-intensive infrastructure that compounds daily. Retrofitting governance after hundreds of agent-generated services are running is an order of magnitude harder than constraining generation at the specification source.

What lies ahead

The sustainability challenge discussed here is not the energy consumed by the AI engineer agent itself, but the long-lived infrastructure decisions encoded into the artifacts it generates. Sustainable infrastructure engineering is no longer an operational discipline. It is an architectural necessity, and the specification layer is where that necessity must be addressed. When autonomous AI engineer agents are generating Terraform, Kubernetes manifests and Docker configurations at scale, the organizations that embed sustainability constraints into the specifications those agents execute will build efficient, cost-controlled, regulation-ready infrastructure by construction. Those that do not will build a remediation programme instead, which at scale will become impractical.

The urgency is not speculative. IEEE Spectrum reports that Microsoft’s emissions have risen 23% since its 2020 baseline and Google’s have climbed 51% since 2019, with AI infrastructure as the primary driver. Global data centres are on track to consume more electricity than Japan by 2030. A significant fraction of that load is over-provisioned infrastructure that an autonomous AI engineer agent generated from a specification that never asked for efficiency. The constraint cost is low. The compounding cost of the alternative is not.

The governance imperative is converging from three directions simultaneously. Cloud cost: Over-provisioned AI-generated infrastructure compounds spend at a rate that makes early specification-layer control orders of magnitude cheaper than post-deployment rightsizing programmes. Technical debt: Every agentic sprint that ships infrastructure without sustainability constraints adds configuration debt that grows faster than any platform team can retrospectively correct. Regulatory pressure: Sustainability reporting requirements, already mandatory in the EU and accelerating in other jurisdictions, will reach infrastructure efficiency metrics. Engineering organizations that have operationalised sustainability governance at the specification layer will meet those requirements as a natural output of their existing pipeline. Those who have not will discover that compliance is a crisis programme when the deadline arrives. These are not abstract architectural concerns. The organizations that govern agentic generation upstream, at the specification, will compound efficiency gains with every agent run, not just sustainability but cost, too. Those who govern only in production will spend a lot of time remediating what they should have prevented before the first line of Terraform was written.

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Key takeaways

• Backstage solved the portal problem, not the platform problem. A portal organizes catalogs, documentation, and templates. A platform owns deployments, environments, policies, and runtime operations. Backstage assumes that the execution layer exists beneath it.
• Point-to-point integrations become a maintenance burden. Many organizations end up with a “messy middle” where Backstage is connected directly to CI/CD, GitOps, Kubernetes, and observability tools through custom wiring that’s fragile and hard to evolve.
• Abstractions are the interface between developers and infrastructure. Developers work with components, endpoints, and dependencies. Platform engineers work with environments, pipelines, and component types. The platform compiles both into Kubernetes resources.
• A control plane bridges the gap. It sits between the portal and runtime, compiling abstractions into infrastructure, enforcing policies consistently, reconciling drift, and aggregating runtime state back to the portal.
• Good abstractions enable advanced capabilities. Unified observability, automated guardrails, and AI agents that can reason about and act on your platform. All becomes possible when you have well-defined concepts and a control plane that understands both sides.

…

Start with Backstage

If you’re building an internal developer platform, Backstage is certainly part of your architecture. It solved the discovery problem and became the default choice for developer portals.

Before Backstage, developers navigated wikis, spreadsheets, and tribal knowledge just to find who owned a service or how to spin up a new one. Backstage brought structure: a unified catalog, a plugin ecosystem, and golden-path templates that actually got adopted.

Backstage is a Cloud Native Computing Foundation (CNCF) project with one of the most active contributor communities in the ecosystem. When organizations evaluate developer portals, Backstage is the starting point.

However, many teams discover something after deployment: Backstage provides a portal, not a platform. A portal organizes information. A platform owns execution: deployments, environments, policies, observability, and runtime operations.

Backstage assumes that the execution layer exists beneath it. That layer is where most of the complexity lives, and it’s what this article is about.

What a developer platform actually is

A developer platform or an internal developer platform is a self-service framework you build to help developers build, deploy, and manage applications independently.

Most organizations already have an organically grown version of this:

• Developer commits code
• CI pipeline builds and pushes images to a registry
• Pipeline updates a GitOps repo containing Helm charts or Kubernetes manifests
• Argo CD or Flux syncs those manifests to clusters

You may have this workflow running today. The question is whether it’s a pipeline stitched together with scripts and tribal knowledge, or a platform with consistent abstractions and self-service capabilities.

What usually happens after adopting Backstage

How do you add Backstage to this setup? The common approach is for developers to maintain Backstage entity files (primarily component and API entities) alongside the source code. Then you configure the built-in entity provider in Backstage to scan source code repositories to populate the catalog. Eventually, you’ll end up with a portal with all your systems, components, APIs, and other resources. So far, so good.

Once developers start using the portal, you’ll be hit with a consistent flow of feature requests:

• “I see my component in the catalog, but is it actually running?” You configure the Kubernetes plugin and link components to their corresponding manifests. Now developers can see pod status, deployment state, and replica counts.
• “I need logs, metrics, and traces related to my component.” You integrate your observability stack or developers context-switch to Grafana, Datadog, or whatever you’re running. Either way, more wiring.
• “Can I create new components from here?” You build Backstage templates that scaffold repos with the right structure, Backstage entities, Helm charts, and CI pipelines, all of which encode your organization’s best practices. Now you’re maintaining golden paths in templates, separately from the runtime configuration that actually enforces them.

Each request is reasonable and achievable, but they add up.

The messy middle

Eventually, you end up with a platform held together by point-to-point connections. Every new capability requires new wiring. Every upgrade risks breaking something. You spend more time maintaining integrations than building features.

You would never design a production system with this many point-to-point dependencies. Why accept it for your platform?

Treat the platform as a product, but also as a system

Organically grown systems get you started, but once you commit to Backstage as your portal, you need a product mindset. Start from developer experience, understand their pain points, then design a system that addresses them coherently.

A platform is also a system. Approach it the way you would approach any production system you’re building. You wouldn’t design a back-end service without thinking about separation of concerns, clear interfaces, and extensibility.

The same principles apply here:

• Separation of concerns: Don’t mix developer-facing abstractions with infrastructure implementation. Keep them separate so you can evolve each independently.
• Clear interfaces: Define explicit abstractions. Developers and platform engineers should interact with well-defined concepts rather than implementation details scattered across Helm charts and CI scripts.
• Extensibility: Requirements keep changing. If every new capability requires custom wiring, you’ll spend more time maintaining than improving. Design for extension from the start.

The difference between a pile of integrations and a platform is architecture. Get the system design right, and new capabilities slot in cleanly. Get it wrong, and every feature request becomes a maintenance burden.

The missing layer beneath Backstage

Moving from an organically grown pipeline to an actionable developer platform is a big leap. You probably have CI/CD pipelines that work, a Kubernetes cluster running workloads, and a Backstage catalog describing what exists.

The questions are:

• How do you transform an informational portal into one with a platform under the hood?
• How do you bridge the gap between what the catalog describes and what’s actually running?
• How do you enforce golden paths beyond initial scaffolding?
• How do you design a platform that evolves with your organization’s needs?

What’s missing is a connective layer between Backstage and your runtime, something that makes the portal operational rather than just informational. Let’s look at the key architectural elements to consider when designing that layer and the whole platform.

Start with abstractions

One of the main goals of a developer platform is to reduce cognitive load. The platform should meet developers where they are and speak their language, not Kubernetes’.

Every organization has its own vocabulary, but the Backstage system model is a good starting point. It may not cover everything, but you can extend it with custom entities. The key is that developers work with high-level concepts while the platform compiles them into Kubernetes resources. Developers are abstracted away from the underlying details, but they can still see what’s happening underneath.

Concept	Description	Backstage mapping
Project	A cloud-native application composed of multiple components. It is also a unit of isolation.	System
Component	A deployable unit, such as web services, APIs, workers, or scheduled tasks.	Component
Endpoint	A network-accessible interface exposed by a component.	API
Resource	External infrastructure such as databases, queues, and caches.	Resource
Dependency	A component’s reliance on endpoints or resources.	consumesAPI, dependsOn

These are not just static abstractions; they also have associated runtime semantics. The following diagram illustrates runtime representations of these concepts.

In the workload cluster, a project becomes an isolation boundary for all of its components. The platform translates this into Kubernetes namespaces and network policies that enforce the boundary, not just document it.

Endpoint visibility determines which endpoints can talk to which. A project-scoped endpoint gets network policies that block traffic from outside the project. An organization-scoped endpoint is exposed to internal traffic but remains behind the internal gateway. An external endpoint gets routed through the public gateway with appropriate authentication. Developers declare visibility; the platform generates the policies.

Dependencies work the same way. When a component declares a dependency on an endpoint, the platform injects the URL and other environment variables required to connect to the dependency. It configures the network policies for both directions, egress from the calling endpoint and ingress to the target endpoint. Without the declared dependency, egress is blocked by default. The dependency graph you see above reflects actual permitted traffic flow, not just intended relationships.

You need platform abstractions, too

Developer abstractions help your developers. Platform abstractions help you.

While developers work with components, endpoints, and dependencies, you need a different vocabulary to design and operate the platform itself. These abstractions let you and your team define standards, enforce policies, and create structure without writing low-level configurations for every scenario.

Concept	Description
Namespace	A logical grouping of users and resources, typically aligned to a company, business unit, or team. Defines ownership and access boundaries.
Data plane	A Kubernetes cluster that hosts one or more deployment environments. You can have multiple data planes for isolation, regional distribution, or scaling.
Environment	A runtime context, such as dev, test, staging, or prod, where workloads are deployed and executed. Environments carry their own policies and resource configurations.
Pipeline	A defined process that governs how work, such as builds, deployments, promotions, or any automated workflows, flows through the platform. Encodes your operational processes as a platform primitive.
Component type	Defines a category of workload—Service, Worker, Cron, Job.
Trait	A reusable capability that attaches to any component, such as autoscaling, resilience, observability, and security policies. Compose behaviors without duplicating configuration.

These abstractions separate platform concerns from application concerns. Developers don’t need to know which cluster their code runs on or how environments are wired together. They deploy to “staging” or “prod,” and you define what those terms mean.

The missing layer is a control plane

The control plane is where abstractions become real. It sits between the portal and your workload clusters, translating developer intent into infrastructure configuration.

You can think of it as a compiler that targets Kubernetes clusters, converting higher-level abstractions into what Kubernetes and its underlying frameworks understand. It can also apply platform-wide rules during this compilation. Resource limits, security requirements, etc., can be enforced consistently, not merely documented and hoped for.

But compilation is only half the job. The control plane also reconciles continuously. It monitors drift between the declared and actual states. When they diverge, it corrects. Your abstractions remain the source of truth; the control plane enforces them over time.

Programmability is not optional

One of the key aspects of this control plane is programmability. If you want your platform to evolve, the control plane needs to be extensible. Different teams have different requirements. New capabilities emerge. You can’t anticipate everything up front.

This means allowing customization of how abstractions compile to Kubernetes manifests. But extensibility without guardrails is dangerous. You need programmability that preserves your invariants. The goal is constrained flexibility, open enough to evolve, structured enough to stay coherent.

Observable abstractions make the portal useful

The control plane also aggregates runtime state and associates it with your abstractions. This is what makes the portal useful. Without this, developers piece together information from different tools: Kubernetes dashboard for pod status, Argo CD for the deployment state, Grafana for metrics, Jaeger for traces. Each tool knows part of the story; none shows the full picture.

With the control plane aggregating state, the portal tells a connected story. When a developer opens a component page in Backstage, they see:

• Deployed environments and their status
• Current replicas and resource usage
• Recent deployments and who triggered them
• Logs, metrics, and traces that are scoped to that component, in each environment
• Dependencies and their health

No context-switching. No reconstructing which pod belongs to which service in which cluster. The abstraction is the anchor; everything else attaches to it.

This only works because the control plane understands both sides. It compiled the abstractions to Kubernetes, so it knows how to map runtime data back. Information flows in both directions. Downward: developer intent flows through the control plane and becomes running workloads. Upward: runtime state flows back through the control plane and appears in the portal.

This is what makes the portal actionable. It’s not just displaying information; it’s connected to a system that can act.

Data plane: keep it simple

The data plane is where your workloads actually run. In most cases, this means one or more Kubernetes clusters. The data plane doesn’t know about your abstractions. It understands Kubernetes primitives such as pods, deployments, services, and ingresses. The control plane’s job is to compile your higher-level concepts into these primitives and apply them.

The data plane does one thing: it runs what the control plane tells it to run. The intelligence lives in the control plane; the execution happens in the data plane.

Where AI fits into the platform

AI is now part of every platform conversation, but the architectural question is where it actually belongs.

The abstractions and control plane you’ve built create the foundation. You have well-defined concepts such as components, endpoints, and dependencies. You have a runtime state aggregated and tied to those concepts. You have a connected view of your system. AI agents can definitely leverage this.

Agents as platform users

AI agents should be able to interact with your platform as first-class participants. This requires exposing platform capabilities through interfaces that agents can use, such as Model Context Protocol (MCP) servers, APIs with clear semantics, user-friendly CLIs, and skills that map to platform operations.

These capabilities of the platform enable agents to create components, trigger builds and deployments, query environment status, and reason about dependencies. They help you and your developers become more productive.

Agents as platform capabilities

You can also embed agents inside your platform to help your teams’ day-to-day operations. Here are some examples of agents you can develop:

• SRE agents: Analyze logs, metrics, and traces to surface likely root causes. Instead of developers digging through dashboards, the agent correlates signals and suggests where to look.
• FinOps agents: Help teams understand and optimize resource costs across environments and components.
• Architect agents: Assist with system design decisions, such as dependency analysis, capacity planning, and migration impact assessment.

These agents work because they have access to the control plane’s unified view. They see abstractions, runtime state, and observability data in one place, the same connected story developers see in the portal.

The pattern holds. Good abstractions make everything easier, including AI.

OpenChoreo as a reference implementation

OpenChoreo is an open-source developer platform for Kubernetes. It was recently accepted into the CNCF as a sandbox project. OpenChoreo implements the architecture described in this article: developer abstractions backed by a control plane, a Backstage-powered portal, integrated CI/CD and GitOps, and observability wired to your abstractions.

If you’re building this architecture yourself, OpenChoreo is worth studying as a reference, even if you don’t adopt it directly. The project demonstrates how these pieces fit together: how abstractions compile into Kubernetes resources, how runtime state flows back to the portal, and how guardrails are enforced during compilation.

You can use OpenChoreo as a complete platform, or install its Backstage plugins into your existing portal and use just the control plane layer. Either way, the underlying patterns are what matter. The architecture is the idea. OpenChoreo is one way to implement it.

A useful mental model: multi-plane architecture

OpenChoreo separates concerns across five planes:

1. Experience plane: Where developers, platform engineers, and SREs interact with the platform via the Backstage-powered portal, CLI, GitOps, or AI agents.
2. Control plane: The brain that translates high-level abstractions (components, APIs, environments, pipelines) into Kubernetes manifests. Programmable through component types and traits, so you can extend it without forking or writing low-level controllers. Continuously reconciles the runtime state back into those abstractions.
3. Data plane: Where workloads run. Enforces the semantics of your abstractions, such as project isolation, traffic policies, and security boundaries. These aren’t just configurations; the platform guarantees them.
4. Observability plane: Feeds metrics, logs, and traces back through the same abstractions developers already understand, requiring no translation.
5. Workflow plane (optional): Handles builds using Cloud Native Buildpacks and Argo Workflows by default.

These planes work together but remain separate concerns. You can reason about each independently, evolve them at different rates, and deploy them flexibly: a single cluster with namespace isolation for dev/test, fully separated multi-cluster setups for production, or hybrid topologies that colocate planes like Control and CI for cost efficiency.

AI and OpenChoreo

OpenChoreo is being built to treat AI agents as first-class participants. In OpenChoreo 1.0, external agents can interact with the platform via MCP servers, agent skills, or the CLI to generate and edit component configurations, reason about releases and environments, and more. The built-in SRE Agent is a first example of this. It analyzes logs, metrics, and traces from your deployments and uses LLMs to surface likely root causes and actionable insights.

From portal to platform

Backstage solved the portal problem. It gave you a unified interface for catalogs, documentation, and golden paths. But a portal isn’t a platform. There’s a gap between what developers see and what’s actually running, and that’s where you get stuck. You fill it with point-to-point integrations, custom plugins, and scripts that become their own maintenance burden.

The pattern that works is portal, control plane, data plane:

• A portal that gives developers ready access to catalogs, documentation, and templates.
• A control plane that compiles platform abstractions, reconciles drift, and aggregates runtime state.
• A data plane that runs workloads and enforces guarantees.

Whether you build this yourself or you adopt something like OpenChoreo, the architecture matters more than the tools. Get the layers right, and new capabilities slot in cleanly. Get them wrong, and every feature request becomes a project.

Backstage gives you the front door. The real platform begins behind it.

—

New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

“The quantum era is no longer ahead of us, it has started,” said IBM CEO Arvind Krishna in statement tied to news that the company is committing $10 billion to advancing quantum computing and commercializing the technology.

IBM will spend the money over the course of the next five years on research and development, capital expenses, partnerships, manufacturing, and mergers and acquisitions, according to its June 2 announcement and a related filing with the SEC.

“A $10 billion investment is pretty significant,” said IDC analyst Heather West. “And it’s sending signals out that in order to actually move the technology forward at a significant pace and get to these larger systems, there has to be a bigger investment in the technology itself. If the US wants to be ahead of the game, and keep leadership, there has to be this level of funding, either on the public or private side, or a combination of the two.”

IBM’s $10 billion investment news came on the heels of a $2 billion investment in a new quantum wafer foundry, Anderon — $1 billion of that funding is coming from IBM, and the other $1 billion is from the US government.

When news of the quantum investment was released late last month, IBM’s stock price rallied, and analysts expect it to continue to climb. Barclays analyst Raimo Lenschow predicted that IBM’s stock price would go up to $350, and that quantum computing has the potential to be IBM’s “next chapter,” according to reports. Citi raised its target from $285 to $375, calling IBM “underappreciated” and with potential exposure to an $850 billion federally supported quantum market, according to reports.

The new announcements aren’t changing IBM’s stated quantum timeline, said West. IBM had already said it is targeting 2029 for fault-tolerant quantum computing. (Pictured above is a rendering of IBM Quantum Starling, a large-scale, fault-tolerant quantum computer that IBM is building in its Poughkeepsie, New York, facility for delivery by 2029.)

“What we’re seeing now is the financial aspect, substantiating what their research path has been,” West said .

IBM has an almost unmatched record of delivering on their roadmap, said Gartner analyst Mark Horvath. The $10 billion is a significant investment, he said. “This is, overall, a significant step towards both advancing their technology base and bringing their quantum computers into mainstream status.”

Anderon foundry

IBM already has quantum computing manufacturing facilities. They will become the basis of a new company, a pure-play quantum foundry called Anderon, which will provide 300mm wafers to multiple quantum technology vendors. There is no announcement yet of when the foundry will be officially open, but the website is already up, at anderon.com.

It will initially support superconducting qubits and supporting electronics wafers, but IBM’s goal is to expand to other types of quantum computers in the future, the company announced in May. According to Gartner’s Horvath, superconducting qubits have been the most reliable quantum computing platform so far, but other platforms — like photonics, trapped ions, and neutral atoms — also show strong promise.

It’s not clear whether IBM’s $10 billion plan includes the $1 billion Anderon investment or is in addition to it. As of press time, IBM did not respond to requests for comment.

Meanwhile, the US government is also partnering with other quantum computing firms as well.

The U.S. government will invest a little over $2 billion in nine quantum-related companies, the Department of Commerce announced in late May. The biggest portion of this investment, $1 billion, will go to IBM, for establishing the new quantum foundry. Another $375 million will go to GlobalFoundries to build a quantum foundry for different kinds of quantum computers, including superconducting, trapped ion, photonic, topological, and silicon spin.

Six other quantum companies will receive $100 million each — Atom Computing, D-Wave, Infleqtion, PsiQuantum, Quantinuum and Rigetti. And a seventh, Diraq, will receive up to $38 million. In return, the Commerce Department will receive a minority, non-controlling equity stake in these companies, the announcement said.

IBM can afford to invest billions in quantum computing, said IDC’s West. “But with startups, it’s a little challenging to be able to do so — they’re very much dependent on outside funding.”

When Pope Leo XIV wrote about the effect that AI is having on our world in his encyclical, Magnifica Humanitas, he may not have imagined the document being referenced in an HR environment.

But, according to a report by Business Insider, Erin Maus, a software developer in North Carolina, used the Pope’s message about the need for vigilance in how AI would be deployed to gain a special exemption from her employer about using the technology for coding.

Maus is not even a Catholic but a Unitarian Universalist, according to the report. However, it said, she maintained that the use of AI didn’t align with her religious beliefs.

Business Insider said that to make her case, she consulted an employment lawyer — a move to be expected — and her local chapter’s minister — which probably wasn’t. Her wishes were reportedly granted last month. “I’m writing my code and reviewing my code by hand, which seems crazy to say,” she told the publication.

She’s certainly not alone in wondering whether AI is always the way forward for techies: a journalist at PC World has also been rethinking its use after reading the encyclical.

It remains to be seen whether this will be the spur for a torrent of claims from Catholic workers, asking to be freed from the demands of using AI or whether Business Insider’s report is an outlier.

When Pope Leo XIV wrote about the effect that AI is having on our world in his encyclical, Magnifica Humanitas, he may not have imagined the document being referenced in an HR environment.

But, according to a report by Business Insider, Erin Maus, a software developer in North Carolina, used the Pope’s message about the need for vigilance in how AI would be deployed to gain a special exemption from her employer about using the technology for coding.

Maus is not even a Catholic but a Unitarian Universalist, according to the report. However, it said, she maintained that the use of AI didn’t align with her religious beliefs.

Business Insider said that to make her case, she consulted an employment lawyer — a move to be expected — and her local chapter’s minister — which probably wasn’t. Her wishes were reportedly granted last month. “I’m writing my code and reviewing my code by hand, which seems crazy to say,” she told the publication.

She’s certainly not alone in wondering whether AI is always the way forward for techies: a journalist at PC World has also been rethinking its use after reading the encyclical.

It remains to be seen whether this will be the spur for a torrent of claims from Catholic workers, asking to be freed from the demands of using AI or whether Business Insider’s report is an outlier.

This article first appeared on InfoWorld.

IBM and ServiceNow are teaming up for new services they say will help enterprise customers bring aging legacy environments into an AI-ready infrastructure. 

The collaboration will combine IBM’s AI, data, and automation capabilities and ServiceNow’s AI platform for a variety of offerings that will modernize aging systems, enable autonomous IT operations, and help organizations evolve existing systems rather than replace them, the companies stated. ServiceNow says its AI-Platform offers a workflow layer that sits on top of an enterprise’s existing systems and helps automate work across them.

Decades of deeply interconnected legacy systems are the biggest barrier to moving fast on AI, the companies stated. Their pairings will take advantage of Big Blue’s expertise in working with large systems, such as its mainframe environment, and extensive legacy applications, along with ServiceNow’s workflow and agent management platforms.

“Most enterprises have the ambition to deploy agentic AI, but lack the foundation to run it at scale,” said John Aisien, senior vice president and general manager, central product management, security and risk, at ServiceNow. “IBM brings the tooling to modernize the systems and extend ServiceNow’s data capabilities. ServiceNow provides the platform to put that data to work across every workflow in the business.”

The vendors will focus on three core services that will be available in the second half of 2026:

• Application modernization: Scans and refactors legacy systems using tools like IBM Bob, Enterprise Application runtime (Java) and IBM watsonx.data to help enterprises bring existing applications into the AI era without starting from scratch.
• Autonomous infrastructure operations: Integrates Red Hat Ansible, IBM Bob, Instana, Hashicorp Terraform, and Hashicorp Vault into ServiceNow IT workflows to detect, remediate, and resolve issues before they affect the business.
• Data governance: Extends ServiceNow Workflow Data Fabric with IBM watsonx.data to unlock key capabilities like Data Quality, Observability, Master Data Management – employing the ServiceNow Data Catalog so that mutual customers can keep track of their AI-ready data.

IBM and ServiceNow have a long-standing relationship, having worked together to help large enterprise customers implement everything from cloud computing, automation, and security to IT service management and observability technologies.