AI Update Q2/2026

Earlier this week, I gave my regular Quarterly AI Briefing for clients. This time, we covered 8 topics in ~45 minutes – the things that happened that I found newsworthy enough to matter for your business.

As usual, there was a lot of noise. But this quarter, there was also (unusually) a lot of signal. We saw the first major frontier model get banned. The first real budget bursts. And clear signs that “agentic AI” is moving from conference slides into core business systems.

Today, I’d like to pull a few highlights from that briefing – the three things I’d pay closest attention to if you run AI in a business.

Let’s dive in!

Here are the eight topics that made it into the briefing

Let’s zoom in on three of these:

Back in April, Anthropic announced Project Glasswing. Their unreleased Mythos model scanned critical software for security flaws, available only to a handful of partners. It found over 10,000 high or critical vulnerabilities in the world's most important software.

So Anthropic decided to hold the model back (“Too dangerous to release”). Keep it private, work with trusted partners, and ship a safer version later.

That safer version eventually became Fable 5, released in June.

And this is where things started to get kafkaesque.

On June 10, Dario Amodei published an essay “Policy on the AI Exponential” where he literally wrote:

Two days later, on a Friday afternoon, the US Commerce Department issued export controls on Mythos and Fable 5. Anthropic’s best AI model effectively became unavailable worldwide across all platforms within a few hours.

Be careful what you wish for.

The interesting nuance here is that the US government didn’t ban Anthropic’s model per se. It just restricted access to non-US citizens (which effectively led to Anthropic taking the model down because how would you ever control who is a US citizen or not?)

To be clear, this was a precedent situation which many have kind of predicted, but no one saw it coming that fast.

A few days ago, the export controls were lifted and Fable 5 came back globally on July 1.

Except it didn't.

The developer platform BridgeMind re-ran their coding benchmark on the restored model. Debugging performance dropped by almost 70%. Refactoring by nearly 50%. The new safety classifiers reroute anything that smells like security work to the older Opus model, and they trigger on routine tasks.

If this quarter has shown one thing then it’s that Sovereign AI stopped being a buzzword and became a hard requirement for enterprises running critical workflows with AI.

I believe the consequence is not that every company should now go out and buy their own GPU racks. But you should be able to answer the following question:

For chat and productivity tools, switching is annoying but doable. For the workflows embedded deep in your operations, swappable models or version-fixed open source models are now table stakes. I've written about the how in Build AI Fast, Then Own It Smart.

Q2 was also conference season. We saw Anthropic’s Code with Claude, Google I/O, Microsoft Build, and NVIDIA GTC Taiwan. Hundreds of announcements that condense into one sentence, and I think Satya Nadella said it most clearly:

The receipts:

What this gave us is consumer, enterprise, and infrastructure all betting on the same thesis: we’re moving full-steam into the era of agentic AI.

Now here's the slide none of the keynotes showed: what happens when AI agents actually run the real world for a longer period of time.

Two interesting experiments from this quarter:

What I wanted to show with this post is AI benchmarks typically evaluate one-off tasks: “Do this job, I'll check the result." But production is different. Deployed agents make repeated decisions, react to feedback, face manipulation, and pursue a goal over a long horizon. That's where small mistakes compound into drift nobody designed for.

So yes, the direction is set, whether we like it or not. But there's a whole staircase between an assistant and an autonomous agent, and I still prefer the middle step: when the workflow is known, an Autopilot built in n8n beats a fancy AI agent. And where agents do go live, governance belongs in the design from day one, with performance watched all the time. Not just the first week after go-live.

One story made the rounds this quarter: a company that reportedly spent $500 million in a single month on Claude tokens. Because nobody set usage limits.

I couldn't verify it because there’s no named company, and no source. But it fits what I see in consulting work: companies spend first and measure second.

The verified stories are telling enough:

And what’s actually worse than spending is that none of them can actually draw a clear line from spend to gains. More tokens does not mean more productivity. (Picture employees using AI to check the weather every day.)

This is nothing new: Automating a task is not the same as creating value. What typically gets automated is the work people dislike, not the work that matters. The agent layer is cheap to buy and expensive to run, and the vendors above are making the buying part easier every quarter. The value part stays your job.

Which brings back the two questions I ask before any engineered AI use case:

If a use case can't answer both, it's a use case without a case. Kill it, no bonus points for running the most agents.

Surging AI costs are a control problem more than a cost problem. You can switch a workflow off. If you designed it so you can. (See my Cost Cap Model)

OpenAI just announced GPT-5.6 a few days ago – and guess who they asked first before they shipped it? Right. We’re still waiting on general approval. Until then, only a “selected group of partners” have access.

I guess the old times where frontier AI labs can’t ship new models fast enough are over. We’ll see.

I’ll report back in Q3.

Until then…

See you next Saturday!
Tobias