u/InfoTechRG

• Anthropic reportedly pushing toward a $900B valuation
• Microsoft exploring AI acquisitions behind the scenes
• Google confirming the first AI-generated zero day exploit used in the wild
• OpenAI launching Daybreak, its new cybersecurity defense initiative
• AWS building payment systems for autonomous AI agents

This isn’t just “AI tools” anymore.

The battle is shifting toward:
• Infrastructure
• Compute ownership
• Cybersecurity
• Autonomous agents
• Control of the future AI ecosystem

The numbers alone are hard to wrap your head around. Hundreds of billions in cloud commitments, trillion-dollar backlog conversations, and vendors already preparing for a future where current partnerships may not survive long term.

At the same time, cybersecurity teams just got confirmation that AI-assisted exploits are already happening in the real world.

Feels like this was one of the first weeks where AI stopped feeling experimental and started feeling industrial.

Our take:

Two things clearly defined this week.

First, cybersecurity. AI-generated exploits are no longer theoretical and patch windows are about to get much smaller. The vendors building strong defensive AI systems now are going to have a serious advantage over the next few years.

Second, capital. The AI race is quickly becoming less about model benchmarks and more about compute access, infrastructure control, and ecosystem ownership. The partnerships shaping AI today already look very different than they did a year ago and they’ll probably look different again by 2027.

Source: https://www.softwarereviews.com/research/big-5-ai-vendor-roundup-week-of-may-11-2026

Our take: If implemented, age restrictions will depend heavily on how verification is enforced at scale. They may also push more responsibility onto platforms to rethink design and accountability for younger users. - MV

Starting to feel like this whole “AI safety vs go faster” thing isn’t really happening in labs anymore… it’s happening in public feeds.

On one side you’ve got safety people moving beyond papers and policy and actually pushing into media, influencers, trying to shape how normal people think about AI risk. On the other side, the labs are just shipping. Faster models, more capability, constant updates, no real sign of slowing down.

So it’s kind of turning into two parallel tracks: one group trying to steer perception, one group just building as fast as possible. What’s weird is this used to lag. Tech would move first, public opinion would catch up later. Now it’s happening at the same time while the tech is still being figured out.

From an IT perspective, this is where it gets real for companies. It’s not about picking a side, it’s about getting squeezed from both directions. The safety push is going to show up as real pressure internally (boards, legal, governance), while vendors keep accelerating and pushing AI deeper into products.

That gap is where most orgs struggle. What we typically see is either teams stall out because risk conversations take over, or they move too fast and end up with scattered AI use cases and no real oversight. Neither actually delivers much value.

The ones that are getting somewhere are treating this less like a debate and more like an operating model problem. Clear use cases tied to business outcomes, governance that scales with risk, and a roadmap that doesn’t get reset every time a new model drops.

Makes it feel less like “who’s right” and more like who can actually handle the pace of change without creating more risk or more chaos. idk just feels like a different phase than even a year ago.

Everyone was debating the idea… this is the part people usually underestimate:

This kind of move doesn’t fail because Linux “can’t work”, it struggles because of everything around it...

A few things that are almost guaranteed to hit:

1. People, not tech
Switching 2.5M civil servants off something they’ve used for years isn’t trivial
Training, resistance, productivity dips… this is where most orgs quietly stall
We see this a lot in large IT transformations. Change fatigue alone can slow things to a crawl

2. Technical debt shows up fast
All those random internal apps built for Windows? Yeah, those don’t just come along nicely
You either rebuild, replace, virtualize, or keep weird workarounds running in the background
This is usually where costs creep way past what was planned

3. Excel is a bigger blocker than people think
Sounds dumb until you’ve seen it in real orgs
Finance teams live in Excel. Macros, models, reporting pipelines
Open source alternatives exist… but they’re not 1:1
We’ve seen cases where orgs had to make exceptions just to keep Excel

4. You’re not replacing Windows, you’re replacing Microsoft
Identity, endpoint management, security tooling, patching, support models
Microsoft’s ecosystem is tightly integrated on purpose
Once you pull that out, you have to rebuild that cohesion somewhere else

5. Scale is the real boss fight
2.5M devices isn’t a migration, it’s a multi-year transformation program
Phased rollouts, parallel environments, constant support overhead

From an ITRG perspective, this is classic:
Orgs underestimate switching costs and overestimate how clean the end state will be

Not saying it won’t work
But this is the part that decides if it actually sticks or turns into another Munich situation

Source: https://tech-insider.org/france-ditches-windows-linux-2-5-million-devices-digital-sovereignty-2026/

Here’s something that doesn’t get said enough in IT. Bad software doesn’t fail, it just quietly gets renewed.

Pulled from Software Buyer Insights research, the numbers are pretty telling. Over 90% of software gets renewed, and IT leaders spend around 26 hours a month evaluating tools, a lot of which never get selected. So it’s not a lack of effort.

The issue is what switching actually involves. By the time renewal comes up, teams have already sat through demos, pulled in security and finance, compared vendors that start to blur together, and burned time they don’t really have. Then someone inevitably says, “are we really doing this again right now?” and that’s usually the decision.

What the data shows is that software decisions don’t break at selection, they break at replacement. Replacing means aligning teams that don’t agree, taking on real risk if something goes wrong, retraining users, and explaining to the business why you’re changing something that technically still works. So even if a tool isn’t great, it sticks.

Vendors know this too. Renewal cycles aren’t truly competitive unless you force them to be. The teams that get out of this loop don’t just pick better tools, they handle the process differently. They start earlier, keep a live view of alternatives, and build leverage before renewal even comes up.

Most orgs think they have a software problem. The research points more to a decision friction problem. Bad software isn’t winning because it’s good, it’s winning because doing nothing is easier.

France is trying to move ~2.5M desktops off Windows to Linux as part of a bigger push away from US software. They’ve already swapped out tools like Teams/Zoom for local alternatives, now they’re going straight at the OS layer

sounds bold, but this is where things usually get painful

Munich tried this with LiMux years back. ran into compatibility issues, users hated parts of it, ended up rolling back… then forward again. it dragged on for years

from an ITRG lens, this isn’t really an OS decision. it’s an ecosystem decision

Linux itself isn’t the issue. it’s everything tied to Windows that becomes a problem:
legacy apps, internal tools, vendor dependencies, identity management, endpoint security, support models

this is the stuff that quietly breaks or slows people down

at this scale you’re not just switching OS, you’re basically reworking how people get their jobs done day to day

still feels less like a cost play and more like control over the stack

2.5M devices is no joke

Source: https://thenextweb.com/news/france-linux-windows-migration-digital-sovereignty

There’s a lot of noise around AI right now, but a few patterns this month actually feel different.

The biggest shift
We’re moving from AI that answers questions to AI that takes action.

What’s interesting is all the major players are converging in the same direction, just from different angles:

• OpenAI is pushing deeper into agents that can actually use software. Not just generating text, but navigating interfaces, clicking through workflows, and completing tasks across tools. It’s a clear move toward AI that can operate inside real environments.
• Anthropic is leaning into multi-agent systems, especially around code and security. Think less single assistant, more coordinated systems that review, test, and fix things together. It’s a very different model of how AI gets used in practice.
• Microsoft is wrapping all of this into products people already use. Positioning AI as “coworkers” inside M365, with access to the same systems, permissions, and workflows as employees. The focus here is less on the model and more on distribution and control.
• Google is building this as a layered system. High reasoning models, faster and cheaper execution layers, and specialized generation tools. It feels like they’re treating AI as infrastructure from day one.

Different strategies, but all pointing to the same place. AI that actually executes work.

What’s becoming clear

Security is no longer a side conversation
Prompt injection, data leakage, and unpredictable agent behavior are showing up fast. This is no longer theoretical.

A lot of teams are not blocked by what AI can do. They’re blocked by whether they trust it to do it safely. That’s why you’re seeing more focus on built-in testing, policy layers, and constant validation.

AI is starting to look like real architecture
It’s not about choosing one model anymore. It’s about how different models and systems work together.

You’re starting to see a split between reasoning, execution, and specialization. That’s a big shift from how most teams approached this even a year ago.

Sovereign AI is creeping in fast
More focus on where models run, where data lives, and who controls it.

This feels a lot like early cloud conversations, but with higher stakes. It touches compliance, security, and long-term strategy all at once.

Value is showing up, but only when it scales
The biggest results are not coming from isolated pilots. They’re coming from rolling AI across workflows and teams.

That’s also where things get harder. Integration, governance, and change management start to matter a lot more than the model itself.

Where this is going

AI is becoming persistent, agent driven, and embedded into day to day work.

Not something you open when you need it, but something that operates alongside you and handles parts of the job.

That’s a very different shift than most people expected this quickly.

Source: https://www.infotech.com/research/ai-transformation-brief-march-2026