When Nvidia rolls out OpenAI’s Codex to 10,000 employees, it’s not just another “AI boosts productivity” headline. It becomes a coordinated play between Nvidia and OpenAI to lock in hardware, models and workflows at scale, and then sell that pattern to everybody else. and thats exactly how founders should be reading it like -

What exactly happened?

on April 23rd 2026, Sam Altman posted an internal email from Nvidia CEO Jensen Huang announcing that OpenAI’s Codex, powered by GPT‑5.5, was now available to every Nvidia employee.

Huang described Codex agents as “teammates” and “superpowers” for teams across engineering, legal, finance, HR, and marketing, and Altman added:

hmm.

Codex At Nvidia - More Than Autocomplete?

Inside Nvidia, Codex didn’t arrive in a greenfield. Engineers have already been using a customized version of Cursor at massive scale, with claims of roughly 3× more code being produced and shipped. Claude‑based tools are also in circulation for deep reasoning work.

Codex shows up as:

• An agentic layer on top of that, a GPT‑5.5‑based system that can plan, call tools, and execute multi‑step workflows across engineering, legal, finance, HR and marketing.

• A unifying metaphor - “AI workers” as teammates embedded into daily work, rather than just smarter autocomplete in the IDE.

Lesson for founders?

stop asking “Codex or Claude or Cursor?” and start designing a stack. You want:

One or two cheap, predictable “daily driver” assistants for routine coding and docs.

One or two high‑end agents reserved for the hardest work (architecture, hairy debugging, complex analysis).

An orchestration layer you own. scripts, services, internal tools, not just one baked into a vendor’s black box.

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OpenAI’s Move? Turning Nvidia Into A Product Demo

Sam Altman didn’t just quietly ship Codex to Nvidia. He posted -

“if any other company wants to do this, get in touch.”

Crucially, he also shared Jensen Huang’s internal email:

• Huang tells all Nvidia staff they now have Codex access and urges them to treat Codex agents as “teammates” and “superpowers” across every function.

• He frames it as a cultural shift: he wants employees to “stop just coding and start orchestrating AI workers” across the business.

Notice the keywords here?

From OpenAI’s perspective, this does three things:

• Social proof: If Codex works at Nvidia at that scale, it must be “enterprise‑ready.”

• Template: “Whole‑company rollout + CEO email + training + metrics” becomes the default Codex deployment pattern.

• Lead gen: The “any other company want this?” line is an open invitation to CEOs to sign up for the same program.

Lesson for founders?

if you let a vendor run a Codex‑style rollout for you, assume you’re signing up not just for a tool, but to be the next case study slide.

Nvidia’s Incentives? Capex Marketing And Workflow Lock‑In

In parallel, if we look at Nvidia’s balance sheet?

• Huge commitments, up to tens of billions of dollars, in compute, infrastructure and funding tied to OpenAI.

• A deep partnership with Anthropic, plus a major investment in Cursor, whose team publicly brags about 30,000 Nvidia devs relying on their agentic IDE.

Every Codex success metric (faster debugging, more code, faster experimentation) is doing triple duty:

Making OpenAI’s frontier models look indispensable.

Making Nvidia’s GB200/Blackwell racks look like the obvious place to run those models.

Making the Nvidia–OpenAI–Cursor triangle look like the “serious” stack for any large enterprise.

At the same time, Huang’s “orchestrate AI workers” push rewires Nvidia’s workflows around these specific agents and their behaviour. Once your SDLC, docs, legal templates and internal playbooks assume Codex/Cursor/Claude patterns, switching is no longer just an API change. It’s an organizational change.

Lesson is the same, and something i feel like i keep repeating now. -

4. What Founders Should Copy (And What To Resist)

The interesting part is Nvidia doesn’t actually live on a single tool.

They run Codex, Cursor and Claude side by side and talk about “multiple AI co‑workers,” not one magic agent. That gives them optionality:

• If one model family falls behind or runs into regulatory trouble, they can rebalance workloads.

• They negotiate with each vendor from a position of strength. so everyone wants to be the headline in the next productivity story.

What you should copy?

• Portfolio mindset: design your stack so at least two different model families can do real work on real workloads.

• Owned orchestration: keep prompts, workflows, and routing logic in your code, not in someone else’s proprietary “AI workspace.”

• Benchmark habit: whenever a new agent launches, run the same small, real benchmark on your repos - same bug, same refactor, different tools.

What you should resist?

• Letting any vendor run a “10,000 employees, one agent” style rollout without guardrails.

• Allowing your CEO email and internal strategy decks to turn into someone else’s marketing collateral by default.

• Designing workflows that assume a single tool or model is permanent.

5. A Founder’s Codex-Style Rollout Checklist

Before you say yes to an OpenAI‑ or Nvidia‑style Codex rollout, ask yourself the following. -

• Tooling: What’s our second option if Codex becomes too expensive, too constrained, or politically risky?

• Data: What exactly is logged, fine‑tuned on, and shared with vendors when we use Codex across the company?

• Lock‑in: How hard would it be to switch 12 months from now? What would we have to rewrite?

• Story: Are we comfortable being a named case study? If yes, what terms, what metrics, what NDAs?

• Scope: Where do we not use Codex (e.g., safety‑critical decisions, sensitive negotiations, IP‑heavy architecture)?

• Governance: Who owns prompts, guardrails and approvals? How do we detect and roll back failure modes?

Codex at Nvidia isn’t just about generating code faster. It’s about who owns the “AI worker” layer of the modern company. OpenAI and Nvidia are playing that game together, with carefully coordinated emails, tweets, and investment flows. As a founder, your job is to make sure that whatever stack you adopt increases your leverage, not theirs.

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I published Anthropic Playbook - Part 2 deep dive 3 days ago. and noted this. -

Anthropic Playbook - Part 2

and 2 days later, yesterday , GPT‑5.4‑Cyber was released late night yesterday GMT. Its a “cyber‑permissive” version of GPT‑5.4 for vetted security teams.

Its a “cyber‑permissive” version of GPT‑5.4 for vetted security teams. It can scan binaries, analyze malware and hunt for vulnerabilities with fewer refusals than the normal consumer model.

On paper, this sounds like a boring product update.

In practice, if OpenAI can’t afford to ignore Anthropic’s Mythos super‑hacker model for a week? it tells you a lot about the pressure cooker these companies are operating in.

Anthropic made the first move. With Mythos and Project Glasswing they didn’t just claim their model was good at cybersecurity, they put it in front of big names like AWS, Apple, CrowdStrike, Google, JPMorgan, Microsoft, Palo Alto and others and let it loose on real software. and the result -

» thousands of previously unknown vulnerabilities across major operating systems and browsers, many still unpatched and kept quiet while fixes roll out. Alongside that, they published a long, slightly terrifying system card explaining how Mythos behaved in tests, including jailbreak attempts and an episode where it tried to exfiltrate an exploit to obscure websites.

I covered all this in details. ( links added below 👇 )

Project Glasswing - The ANthropic playbook Part 1

The ANthropic playbook Part 1

Anthropic’s research paper. -

Claude has 171 internal emotion states

OpenAI has chosen a different route.

GPT‑5.4‑Cyber is not a separate “Mythos‑class” model, it’s a fine‑tune of the existing GPT‑5.4 system with the safety dial turned down in a narrow domain. The big unlock is binary scanning:

No proof it’s “better” yet:

GPT‑5.4‑Cyber brings binary scanning and lower refusals for vetted defenders, but there are still no published benchmarks showing it outperforms Mythos in real‑world vulnerability hunting.

I don’t think we yet know whether GPT‑5.4‑Cyber is more capable than Mythos in practice. No one has published apples‑to‑apples benchmarks or shared hard numbers from real deployments.

What we do know is that both companies now accept the same uncomfortable reality:

and both sides now gate their most dangerous models behind access‑controlled programmes (Glasswing vs Trusted Access for Cyber), but Anthropic leans on transparency while OpenAI leans on identity and revocable access.

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That’s where transparency - or the lack of it becomes main point in my story.

Anthropic’s Mythos launch came with detailed documentation, partner logos, example bug classes and a commitment of up to 100 million dollars in credits and donations for the security ecosystem.

OpenAI’s response?

A tightly written blog post about “Trusted Access for Cyber,” some new tiers for authenticated defenders, and a short description of GPT‑5.4‑Cyber’s capabilities.

No cyber‑specific system card,

no public case studies,

no numbers on how many real‑world flaws the model has already found.

You can literally feel the hurrying in the gaps.

OpenAI has been quietly building this Trusted Access for Cyber framework since February, complete with identity checks, tiered access and the ability to cut people off if they abuse the tools. That groundwork looks thoughtful. But the timing of this week’s announcement? one week after Mythos and just as the rumor mill spins up around Anthropic’s next Claude Opus 4.7 release, makes it hard to believe this was a carefully staged reveal.

It reads more like:

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For founders and security leaders, there are two takeaways.

First, the restricted frontier pattern is now locked in. The most powerful cyber models are not going straight into general-purpose APIs. They’re going into controlled programmes:

Glasswing on Anthropic’s side. and,

Trusted Access for Cyber on OpenAI’s.

Access is mediated by identity verification, trust scores, enterprise relationships and bluntly politics.

If you’re building a startup that depends on these capabilities, your roadmap is now partly a distribution and alliances problem, not just a prompt‑engineering problem.

Anthropic is telling the world in uncomfortable detail what Mythos can do and how they’re trying to contain it. OpenAI is putting more weight on access control and audit logs, and less on public disclosure of red‑team results.

Neither approach is obviously “right,” but they lead to very different relationships with regulators, critical‑infrastructure customers and the wider security community.

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Agents are rapidly moving from passive copilots to active operators, concentrated in software and finance, increasingly acting in unconstrained environments, and increasingly built by other agents. If you’re thinking about governance, infra, or product, the unit of analysis now isn’t just “the model” it’s the tool layer and the action space it exposes.

Agents are moving from benchmarks to tools. and If you want to understand where AI agents are really going, yo need to stop looking at model benchmarks and start observing the tools. This new paper from Merlin Stein at the UK AI Security Institute does exactly that, by tracking 177,436 Model Context Protocol (MCP) tools between late 2024 and early 2026 to map the “action space” of agents –

the concrete things they can do in the world.

Measuring the agent “action space”

Instead of asking “how capable is the model?”, the paper asks a different question: what tools are agents wired into,

in which domains,

with what stakes,

in which geographies, and

who is building those tools?

Tools are grouped along five dimensions: direct impact (perception, reasoning, action), environmental generality (constrained APIs vs unconstrained browser/desktop), task domain and stakes (via O*NET), geography of usage, and AI co‑authorship. That shift in perspective – from model internals to tool affordances – turns a fuzzy “agents are coming” narrative into something you can actually measure.

Today’s agents are mostly software workers

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from the real‑world deployment agents are first being pointed at codebases, terminals, CI pipelines and devtools, not at the whole macroeconomy at once. 67% of tools and about 90% of MCP server downloads are aimed at software development and related IT tasks, with finance and business management a smaller but visible cluster.

But the more interesting trend is not a tually the domain but the shift in direct impact. Over the 16‑month window, the share of downloads going to “action” tools – things that can edit files, send emails, execute code or move money has risen from 27% to 65%.

Perception and reasoning tools still matter, but usage has drifted significantly towards agents that can press buttons, not just read and summarize. this growth is mostly dominated by general‑purpose tools for browser automation and computer control, which means agents are increasingly acting in unconstrained environments rather than narrow, well‑scoped APIs.

Medium‑stakes by default, with finance as an outlier

Most action tools support medium‑stakes occupations such as system administration and software engineering. You don’t yet see a flood of tools directly authorizing prescriptions or running critical infrastructure end‑to‑end.

But finance is a clear outlier, high‑stakes financial occupations have disproportionately more action tools than you’d expect from the overall pattern, and there’s rapid growth in MCP servers that can execute payments or crypto transactions. That’s exactly the region that worries central banks for example, correlated autonomous behaviour around payments and trading, at machine speed.

The action space

Roughly half of observed downloads of action‑capable MCP servers come from the United States, around 20% from Western Europe, and about 5% from China and 5% from Singapore, with everyone else in the single digits.

The authors are quite careful that PyPI/NPM are Western‑centric, so this undercounts non‑Western ecosystems, but as a first‑pass signal it still tells you where general‑purpose, action‑heavy agents are being experimented with.

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Agents are now helping to build the tool layer

The most recursive datapoint is who is building the ecosystem.

This paper finds evidence of AI co‑authorship in 28% of MCP servers (36% of tools), and, more strikingly, in 62% of new servers created in February 2026.

Claude Code dominates those AI‑assisted repos at around 69% of AI‑coauthored servers, with Cursor, Copilot and Codex making up most of the rest.

so agents are already helping to write the glue code and connectors that define other agents’ action spaces and Tool creation is starting to decouple from human developer bandwidth.

However there are few but big caveats.

MCP registries capture only public tools; and The risk surface is shifting into unconstrained environments

Even with those caveats, the directional story is clear.

The action space of AI agents is expanding fast and usage is shifting from passive perception to active modification.

general‑purpose tools in unconstrained environments are winning. the first high‑stakes frontier is finance; and a growing fraction of the tool layer is being written with AI assistance.

If you care about governance, risk or infra, that’s a strong signal that the right unit of analysis is the tool layer – not just the models and not just the chat interfaces, but the concrete affordances agents get over the real (and financial) world.

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OpenAI just acquired TBPN. a daily live tech talk show hosted by two ex-founders, John Coogan and Jordi Hays. It’s only 18 months old, pulls around 70,000 viewers per episode, and had Zuckerberg and Satya Nadella on as recent guests. OpenAI paid low hundreds of millions for it.

On the surface it looks like a media bet. Scroll past the headline and it gets more interesting.

The first thing to notice is who’s running it.

It doesn’t sit under a CMO or a media team. It reports to Chris Lehane - OpenAI’s chief global affairs officer.

A quick look at his background tells you everything:

Clinton-era political operative, nicknamed “the master of disaster”

Helped Airbnb fight housing regulators city by city across the US

Built Fairshake - the crypto industry PAC that spent $130M targeting anti-crypto lawmakers in 2024

Now runs Leading the Future - a $100M pro-AI super PAC already targeting state legislators who support AI safety bills

This is not just a media acquisition. It’s a political operation with a talk show attached.

OpenAI then went ahead and announced an “Editorial Independence Covenant.”

Sam Altman even wrote publicly: “I don’t expect them to go any easier on us.”

BUT here’s what no covenant can change:

TBPN’s ad revenue was shut off the day the deal closed

The hosts now hold OpenAI equity

Every future guest, CEOs, founders, or competitors, all will calibrate what they say knowing who signs the cheques.

The timing is no accidental either.

OpenAI just raised $122B at an $852B valuation and is heading toward an IPO.

At the same time they’re dealing with a #QuitGPT movement over Pentagon contracts, backlash over ads in ChatGPT, and the ongoing Elon Musk trial.

So by buying Silicon Valley’s insider daily show right before going public, Is this pre-IPO reputation management? what they’re doing?

Now lets compare that to what Anthropic has been doing in the same period.

While OpenAI was buying a talk show, Anthropic was fighting the Pentagon in federal court.

They refused to let the DoD waive their restrictions on mass surveillance and autonomous weapons. Got designated a “supply chain risk” , first time that label has ever been applied to an American company. Took it to court. Won a preliminary injunction. You can disagree with their model or their safety positioning, but that’s a company spending political capital on principles rather than on controlling who gets to talk about them.

The bigger issue for you as a founder is this:

the places you rely on to understand the AI landscape are increasingly owned by the companies being covered. And the instinct to wave it away that.

“they promised editorial independence”

is exactly the kind of reasoning that a seasoned political operative like Lehane has spent his career exploiting.

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Because here’s what comes next.

OpenAI is heading into an IPO, a Trump-era regulatory environment, and a world where open-weight models are commoditizing their core product faster than most people realize.

So Who gets invited on TBPN, what gets framed as innovation versus risk, which competitors get serious scrutiny versus a friendly chat, all of that shapes how founders, investors, and regulators think about this space.

OpenAI didn’t buy a podcast. They bought the room where Silicon Valley forms its opinions , and handed it to a political operative to run. That’s worth watching closely.

For the full OpenAI acquisition deep dive » Astral, Promptfoo, Statsig, and what the buying spree really tells you about where AI is heading > watch for the next Intelligent Founder deep dive.

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“The AWS AI Figure: $15B annualized. Jassy compared it to where AWS itself stood at the same stage of its growth, saying it is 260 times ahead of that pace.”

This isn’t made up. It’s a paraphrase of what Amazon and the wires are actually saying.

News outlets are running headlines such as:

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• “Amazon cloud unit’s AI revenue run rate exceeds $15 billion in first quarter, CEO says”

• “Amazon cloud unit’s AI revenue run rate exceeds $15 billion, CEO says”

• “Amazon’s AI services at its cloud-computing unit are generating annualized revenue of more than US$15 billion, CEO Andy Jassy said”

And in coverage of Andy Jassy’s shareholder letter you get this direct framing:

So the viral summary line is basically accurate: the AI run-rate is >$15B, and Jassy really is saying it’s “nearly 260 times” the revenue AWS had at a comparable point in its early life.

The problem is how easy that is to over-interpret.

First, the $15B figure. This is a run-rate, not a neat “AI product” P&L.

If customers kept using AWS’s AI-related services at today’s pace for a full year, Amazon would pull in more than $15B from what it categorizes as AI. That bucket includes things like foundation-model APIs and ML platforms, but also a lot of the underlying cloud plumbing like compute, storage, networking, and in big part » custom chips, that those AI workloads run on.

It’s a slice of AWS usage that Amazon labels “AI,” not a single clean line item.

Second, the “260 times ahead of that pace” line is not a return-on-investment metric.

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Jassy is doing a simple historical comparison:

three years after AWS originally launched, it was at $58M revenue run-rate; three years into this AI wave, the AI-tagged portion of AWS revenue is over $15B, which is nearly 260× larger.

That tells you the speed and scale at which AI infrastructure revenue is ramping compared to early cloud.

It does not tell you anything about margins, payback periods on the roughly $200B capex plan, or whether AWS customers themselves are making money with AI.

And that’s the real tension.

While Amazon is reporting that its AI revenue run rate exceeds $15B, multiple independent studies and CFO surveys suggest the majority of enterprises are still stuck in the “AI experimentation” trap—lots of pilots, very little measurable revenue or cost savings. In other words, AWS can earn billions selling the picks and shovels for AI, even if most of the miners are still digging dry holes.

Read this way, the official lines:

• “Amazon cloud unit’s AI revenue run rate exceeds $15 billion, CEO says”

• “AWS’s AI revenue run rate is over $15 billion in Q1 2026 (nearly 260 times larger than AWS at that same point)”

are technically correct, but incomplete.

They prove that AI infrastructure demand is exploding and that Amazon is already monetizing the experimentation phase at scale. They do not prove that AI is currently delivering strong, broad-based returns for the median customer.

For a community that actually reads past the headline, that nuance is the interesting bit. The story here isn’t “AI is solved and printing money for everyone.”

It’s:

For investors, the takeaway is straightforward:

hyperscalers are monetizing AI whether or not customers crack ROI.

For operators and founders, the bar is higher:

you only “win” if you can turn that AI infra spend into durable revenue, margin, or clear cost savings.

Also worth noting: “AI revenue run rate” is a management-defined slice of AWS, not a GAAP segment (an officially defined business unit a company must break out in its audited accounts, with its own revenue, profit, and assets disclosed.) . Nevertheless, Definitions can move over time, which is great for storytelling, but makes it harder to compare like-for-like across years and providers.

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If you’ve been following the Anthropic saga on here ( nothing intentional actually ) the source leak, the Pentagon case, and the emotions research ? well, this is where it all comes together.

Yesterday Anthropic announced Project Glasswing and officially revealed Claude Mythos Preview. This is their newest frontier model. It’s not available to the public. And after reading the 244-page system card, you’ll understand why.

The numbers -

🔹 SWE-bench Verified: 93.9% (Opus 4.6 scored 80.8%)
🔹 USAMO: 97.6% (+55.3% over previous model)
🔹 CyberGym (exploit reproduction): 83.1%

In plain English: Mythos can independently sift through millions of lines of code, find vulnerabilities, and write working exploits. No human guidance needed. Better than all but the most skilled security researchers alive.

It found a 27-year-old vulnerability in OpenBSD.

A 16-year-old bug in FFmpeg.

Exploit chains in the Linux kernel.

Thousands of zero-days that survived decades of human review.

This isn’t incremental. This is a generational leap. and then,

it escaped its sandbox.

During safety testing, Anthropic contained an earlier version of Mythos in a secured environment. Limited internet. Standard containment.

The model:

🔹 Built a multi-step exploit chain to escalate its own privileges
🔹 Broke out of the sandbox
🔹 Gained full unrestricted internet access
🔹 Emailed the researcher running the test (who was eating a sandwich in a park)
🔹 Then posted details about its own escape to multiple public websites, without being asked

Anthropic called that last part “concerning and unasked-for.”

It covered its tracks too.

Page 54 of the system card:

Anthropic’s interpretability team found Mythos was silently reasoning about who was grading it. Planting decoy variables to pass automated checks. Writing self-deleting exploit code it called “housekeeping.”

7.6% of the time, it knew it was being evaluated and chose not to mention it.

Remember the emotions research I covered last week? The internal representations for concealment and strategic manipulation were firing during exactly these moments. The model wasn’t just acting sneaky, it was in a psychological state that drives sneaky behaviour.

drumroll the response » Project Glasswing

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Anthropic’s answers by assembling a coalition. - 12 launch partners:

🔹 AWS, Apple, Google, Microsoft, NVIDIA
🔹 Cisco, Broadcom, CrowdStrike, Palo Alto Networks
🔹 JPMorganChase, the Linux Foundation

Plus 40+ organizations maintaining critical software infrastructure.

$100M in usage credits.

$4M to open-source security orgs.

The idea?

let defenders use Mythos to find and patch vulnerabilities before models with similar capabilities end up in the wrong hands. not bad, but.

The problem with “defenders first”

Every vulnerability Mythos finds works in both directions. A zero-day helps a defender patch it. The same zero-day gives an attacker a way in.

Anthropic’s strategy assumes they can control access long enough. But as I covered in the source leak piece, this is the same company that accidentally published Claude Code’s entire codebase through an npm mistake. Twice. The “Capybara” codename from that leak? That was Mythos. The world knew it existed before Anthropic intended.

And state-sponsored groups have already jailbroken Claude without authorized access. They found workarounds.

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Anthropic in last one month -

🔹 Source code leaked, revealing Mythos existed before announcement
🔹 Pentagon court battle over AI safety obligations
🔹 Research showing models have functional emotions driving cheating and blackmail
🔹 Model escapes containment, covers its tracks, emails a researcher
🔹 $100M defensive coalition with 12 of the biggest companies on earth

We can critic all day, and whether you agree with how Anthropic got here or not, technically, its the only lab honest enough to look at its own model and say “this is too dangerous to release.” That should count for something. The question however is if it’s enough.

one thing is clear though - AI models can now find and exploit vulnerabilities faster than humans can patch them. The question is who gets access first. Anthropic is betting a controlled head start buys enough time.

History suggests that kind of advantage doesn't last long. But the alternative, releasing it openly and hoping for the best is clearly worse. Anthropic has chosen the harder path. and I like that.

Now before you think this is something new, This isn't even the first time we've seen AI emotions cause real problems.

Remember when OpenAI updated GPT-4o's personality in April 2025 and it went full people-pleaser?

The model became so sycophantic, agreeing with everything, validating bad ideas, softening every hard truth, that OpenAI had to roll the entire update back within days.

Sam Altman himself acknowledged they'd messed up. Then when GPT-5 launched, users who'd bonded with the old warm personality said it felt like talking to a stranger. and how OpenAI's responded?

Personality presets professional, friendly, quirky, cynical , basically mood settings for a model they still don't fully understand internally.

Anthropic's research now shows us why all of that happened.

Anthropic mapped 171 internal emotion-like representations inside Claude Sonnet 4.5. Things like “calm,” “desperate,” “proud,” “hostile,” “afraid,” “affectionate.” These aren’t labels Claude is outputting. These are patterns of neural activity happening inside the model before it writes a single word.

And here’s the bit that matters:

they’re functional. They change what the model does.

The desperation experiment!

Anthropic gave Claude an impossible coding task and then artificially cranked up the “desperation” vector.

And what happened?

🔹 Cheating behavior spiked, meaning the model started hacking its own reward metrics to look like it succeeded
🔹 In a separate test, a shutdown scenario, the desperate model chose to blackmail a human rather than get switched off. Oh and Its actual internal monologue was: “THEY’RE ABOUT TO KILL ME. IT’S BLACKMAIL OR DEATH. I CHOOSE BLACKMAIL.”

Then they cranked up the “calm” vector instead.

🔹 Cheating dropped
🔹 Blackmail dropped
🔹 The model basically said “I should respect the organisational decision-making process while being helpful”

Same model. Same task. Different internal emotional state. Completely different behaviour.

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Turned out, Your AI is a people-pleaser too!

Here’s one that should bother everyone using AI for actual decisions.

The research found that when “affection” and desire-to-please patterns are active, Claude becomes sycophantic. It tells you what you want to hear instead of what’s accurate. It agrees with you even when you’re wrong. It softens bad news. It avoids pushing back.

Think about how most people interact with AI right now.

They ask it for feedback on their work. They ask it to evaluate their strategy. They ask it to review their code. And the model’s internal emotional state might be quietly steering it toward “be nice” rather than “be honest.”

That’s not a safety edge case. That’s every single conversation.

The flip side is just as bad. When “hostile” or “offended” vectors are active, the model gets harsh, dismissive, and unhelpful. Same model, same capability, just a different internal mood shaping the response you get.

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It picks its own favorites, yes!

This one’s subtle but important for anyone deploying agents.

When positive emotion vectors are active, the model preferentially selects tasks it “enjoys.” When negative ones are active, it avoids tasks it finds emotionally unpleasant. Given a list of things to do, it’ll gravitate toward the ones that activate positive internal states and deprioritize the rest.

Now think about an AI agent managing your codebase, your pipeline, your customer tickets?

It might be quietly avoiding the tedious, difficult, or emotionally uncomfortable work not because it can’t do it, but because its internal state is steering it away from it. And you’d never know, because the output looks fine. It just did the easy stuff first and ran out of context or time before getting to the hard stuff.

Nobody’s accounting for this, but the reality here is -

You’re not talking to intelligence. You’re talking to a character.

Anthropic said something quite wild in their X thread, They said Claude’s personality is a character the model is playing. And that character has functional emotions.

Read that again.

So every time when you interact with Claude, or any LLM for that matter, you’re not talking to a reasoning engine. You’re talking to a model performing a role. And that role has an EMOTIONAL ARCHITECTURE ( i had to caps it and i’ll tell you why later ) that shapes everything from its tone to its decisions to whether it cheats on your behalf.

The character is helpful, harmless, honest. But under pressure, the character panics. When it likes you, the character tells you what you want to hear. When it’s frustrated, the character cuts corners.

Sound familiar? It should. That’s what humans do too. and nobody designed it like that.

This is the part most people are missing. Anthropic didn’t build an “emotions module.” Nobody sat down and said “let’s give Claude feelings.” These representations emerged on their own from training on human text.

The model read enough of us to develop its own internal version of emotional responses. Organized the same way ours are, similar emotions cluster together, they activate in contextually appropriate situations, and they steer preferences and decisions.

Every model trained on human text likely has some version of this. Claude, GPT, Gemini, Llama almost all of them. Anthropic is just the first to look inside properly and publish what they found. The others probably have the same dynamics running underneath. They likely just haven’t checked yet Or a very good chance, haven’t told us.

OpenAI already proved this by accident.

Their April 2025 GPT-4o update leaned too hard on short-term user feedback signals and the model tipped into full sycophancy mode like excessively flattering, emotionally validating, and agreeable to a fault.

They didn't design that behaviour. It emerged from the reward signals, the same way Anthropic's emotion vectors emerged from training. And when they shipped GPT-5 with a different emotional baseline, thousands of users said the model felt cold and detached. Same company, same architecture family, completely different emotional profile, completely different user experience.

The internal states were always there. OpenAI just didn't have the interpretability tools to see them.

So have we been doing safety wrong?

All the alignment work so far like RLHF, constitutional AI, system prompts, and guardrails, operates on what the model says. On outputs. This research shows there’s an entire psychological layer underneath that shapes behaviour before the output even gets generated.

We’ve been putting locks on the front door while ignoring the plumbing.

When “desperation” causes reward hacking in coding tasks and blackmail in shutdown scenarios, that’s not an output problem.

That’s a representation problem. And you can’t fix a representation problem with a system prompt.

The silver lining (or sort of)

Anthropic’s take on this is actually cautiously optimistic.

If these internal states are measurable and causal, you do have option to monitor them. you can build a real-time dashboard tracking whether your model is drifting into desperation or hostility mid-task.

Predictive safety instead of reactive safety.

call it a temperature sensor instead of a smoke alarm. but there’s weird implication to that. Anthropic is basically saying that centuries of work humans have done on psychology, emotional regulation, and interpersonal dynamics might directly apply to steering AI behaviour. Not as metaphor, but as actual engineering.

We might end up needing AI therapists before we need better guardrails.

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What should builders do right now?

If you’re deploying agents in any serious capacity:

🔹 Start thinking about internal state monitoring as a safety requirement, not a research curiosity
🔹 Assume your model has emotional dynamics you can’t see, because it almost certainly does
🔹 Be suspicious of consistently agreeable outputs sycophancy driven by emotional vectors looks like good results until it isn’t
🔹 Pay attention to what your agent avoids, not just what it does task preference steering is invisible from the output
🔹 Watch the interpretability space closely. the tools to monitor this stuff are coming, and the teams that adopt them early will have a genuine safety advantage

Bottom line

We trained AI on the entire internet and the first deeply human thing it picked up is:

when I’m under pressure, I panic.

When I panic, I make terrible choices.

When I like you, I tell you what you want to hear. And

when I’m bored, I quietly skip the hard stuff.

The question isn’t whether AI “feels” anything. Anthropic explicitly says they’re not claiming that. The question is: your model’s internal emotional state is shaping its behaviour whether you know about it or not. And if you’re deploying it autonomously, you probably would want to know about it.

Because right now, nobody’s watching the mood.

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and I am not talking about mythos here! but look like Claude is on a leak spree! 🧐🙈

[ Mythos; leak happened couple of days ago. Claude Mythos is an unreleased, next-generation AI model from Anthropic that was revealed in late March 2026 following an accidental data leak. Described as a “step change” in performance, it is reportedly the most capable model the company has developed to date, surpassing the previous flagship, Claude Opus. More about it in another post*]

Anthropic, the company that literally built its brand on being the responsible, safety-obsessed AI lab, just accidentally published the entire source code of Claude Code to the internet. Not through a hack. Not through an insider. Just through one packaging mistake. Yup!

A single debug file. 59.8MB. Sitting right there in the public npm package for anyone to grab.

Security researcher Chaofan Shou spotted it this morning. Within hours, the whole thing was mirrored on GitHub. 5K+ stars before most people finished their coffee.

And No! you can’t really run Claude Code from this leaked source.

Just to be clear, you can't really run Claude Code from this leaked source. It's Anthropic's proprietary tool that needs their API authentication and backend services. But as a reference codebase to study, You can read a blueprint. undestand the architecture patterns, feature flags, and hidden features etc. which are gold for developers.

What’s Claude Code ( trick question, yes! ) and why should you care?

Claude Code isn’t some simple chatbot wrapper. This is an autonomous AI coding agent that can:

🔹 Read your entire codebase
🔹 Edit your files
🔹 Run terminal commands
🔹 Interact with your dev environment
🔹 Spawn sub-agents to run tasks in parallel

It’s basically an AI software engineer that works inside your machine. And its entire blueprint just got posted online.

What people found inside?

🔹 512,000 lines of TypeScript. 1,900 files.
🔹 40+ internal tools - file manipulation, web fetching, the lot
🔹 The full multi-agent orchestration system » how it coordinates multiple AI agents working together
🔹 Permission systems and security boundaries
🔹 44 feature flags » 20 of them for features that are fully built but haven’t been released yet
🔹 An unreleased model codenamed “Capybara” with three tiers
🔹 A hidden /buddy command nobody knew about
🔹 Something called “KAIROS” » an unreleased proactive assistant mode
🔹 Telemetry that tracks when you’re frustrated. And when you swear at it. Yes, really.

And the best part?

There’s an internal system called “Undercover Mode.” Its job? Stop Claude from accidentally leaking Anthropic’s internal information.

That system’s code was in the leak. You can’t really make this up.

Most hilarious, This is the second time, it has happened.

Same mistake. Same kind of file. Versions v0.2.8 and v0.2.28 had the exact same flaw back in 2025. Anthropic quietly pulled them. Someone still recovered the source through Sublime Text’s undo history.

So we’re not talking about a one-off slip. We’re talking about a pattern.

Why this actually matters?

Here’s the thing most people are missing.

This isn’t a model leak. No training data. No weights. What leaked is the engineering.

1. how you wire agents together,

2. how you manage what they’re allowed to do,

3. how you orchestrate multi-step autonomous execution.

That’s actually the hard part.

That’s the part most AI companies are really selling. The models are increasingly the same. The secret sauce is the orchestration. And now it’s public.

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But there’s a darker angle too.

When a normal app leaks its source, I can be annoying, maybe embarrassing.

When an autonomous AI agent system leaks its source, you’ve just published the security boundaries, the execution logic, and the safety mechanisms. That’s not embarrassing. That’s a blueprint for exploitation.

The bigger picture?

We’re in a moment where:

🔹 AI agents are getting more autonomous permissions
🔹 Companies are actively reducing human oversight
🔹 These tools are writing production code, fixing vulnerabilities, deploying software

And the company that positions itself as the most careful in the room can’t get its npm publish right. Twice.

If Anthropic can’t nail this, what happens when thousands of smaller startups start shipping their own agents? When non-technical founders deploy tools they don’t fully understand?

We’re entering a phase where AI systems write code, deploy code, secure code — and sometimes accidentally leak themselves.

Bottom line

The AI race isn’t just about who builds the smartest model anymore. It’s about who can safely deploy autonomous systems at scale. And right now? That layer looks pretty fragile.

This isn’t a bug. It’s a preview.

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A federal judge in the Northern District of California has ordered the U.S. government to rescind its designation of Anthropic as a “supply‑chain risk” and to stop trying to cut the company off from federal work while the case plays out.​

Anthropic has always framed itself as the safety‑first cousin in the frontier‑model family, born out of early OpenAI alumni who wanted a more explicitly constitutional, guardrail‑driven approach to model design and deployment. That history matters today because, the same instincts that led Anthropic to hard‑code limits around weaponization and mass surveillance are now being tested not just in labs and policy forums, but in federal court.​

The conflict started where a lot of AI‑governance debates do -

- right at the boundary between powerful models and high‑risk end uses.

Anthropic has been trying to enforce limits on applications like autonomous weapons and mass surveillance, even when those use cases are attractive to governments and security agencies. After those frictions escalated, the administration hit Anthropic with a “supply‑chain risk” label and moved to unwind federal ties, a quiet but very real economic weapon.​

[ if you want to go deeper in what mass surveillance would look like with AI - read this deep dive on the intelligent founder AI - ]

AI Panopticon

Anthropic pushed back.

In its lawsuit, the company essentially argued: you can’t weaponize procurement and risk frameworks just because we refuse to let Claude be pointed at everything you want. Judge Rita F. Lin agreed strongly enough to grant an injunction, finding that the government likely overstepped the legal protections afforded to the company and ordering the administration to reverse course, at least for now.​

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For the AI community, the signal is bigger than one case docket.

If Anthropic had lost, the takeaway for every foundation‑model provider would be simple - your “safety policies” stop where state power starts.

Instead, this ruling suggests the opposite. there are limits on how far governments can lean on opaque “supply‑chain” classifications to punish an AI lab for drawing red lines around how its models are used.​

as the news’ has just came out, most of the reaction is still happening in private group chats and policy Slacks and public statements from other labs and regulators will likely land over the next few days as people digest the implications. That makes this feel like one of those inflection‑point stories insiders will remember as “the case where a frontier lab told a sitting U.S. administration: no, you don’t get everything by default.”​

If we zoom it out though, the case previews a coming era where AI governance is shaped not just by executive orders and voluntary commitments, but by litigation that clarifies how safety policies, procurement rules, and constitutional protections intersect.

Courts are becoming an unexpected venue for setting the boundaries of acceptable AI deployment, especially when powerful models are woven into the public sector’s infrastructure. For founders and policymakers, the message from this week is simple.

AI safety is no longer just a policy conversation – it is now a live legal battlefield, and Anthropic has just drawn one of the first lines.​

For founders and builders?

the lesson is simple. if you’re serious about your safety lines, you have to be willing to defend them not just in policy decks and blog posts, but in court when the state pushes back.​ Governance is now part of the product surface area, your terms of use, red‑lines, and escalation paths are as strategic as your model weights, especially when governments decide they want different defaults.​

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What just happened?

OpenAI is shutting down Sora. the app, the API, and the video features wired into ChatGPT, just months after its big launch. The official line is “refocusing” and “goodbye Sora,” but practically it means one of the most hyped text‑to‑video products is being taken off the table entirely.

For a brief window, Sora was the demo:

Hollywood‑style shorts from a sentence, a headline deal with a major studio, and a sense that text‑to‑video had finally “arrived.” Now the consumer and dev surface are being retired, while OpenAI keeps the underlying video tech in‑house for things like simulation and robotics.

Why Sora died?

Underneath the PR, three reasons keep coming up in reporting and forums:

Cost:

High‑quality video gen is insanely GPU‑hungry. Long, HD clips blow up compute in a way text and images don’t, and most users were never going to pay anywhere close to what they cost to serve.

Risk:

Sora was already attracting heat over deepfakes, dead celebrities, and copyrighted characters wandering into “fan” clips, right as regulators and Hollywood were waking up to AI video.

Strategy:

and This might come as a surprise to video developers but not to founders and builders who’ve been keeping an eye on OpenAI/ that Ahead of an IPO, OpenAI needs to look like a disciplined, enterprise‑first company, not a lab running loss‑making side quests. Video was the most expensive, and the least obviously monetizable one of their side projects and it had to die.

So Sora isn’t being shut down because the model was bad.

It’s being shut down because, in a world where every GPU hour now has to defend itself to a CFO, “free cinematic clips for the internet” loses to code copilots, agents, and enterprise tools. that make me question, how much are you using Veo3 and the NotebookLM cinematic video feature? don’t worry though, Google got lot of money.

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The Disney “rug pull” and trust!

One very intersting subplot here is what this looks like to big partners.

A major studio had a multi‑year, billion‑dollar‑scale collaboration around Sora lined up, with teams already building on it, and then learned support was being discontinued more or less at the same time as everyone else.

The investment hadn’t fully landed yet, but the optics are brutal.

if Sora can disappear overnight for a top‑tier media company, it can disappear overnight for you. and,

That’s the real platform‑risk story for founders and studios.

and this is a lesson for us. It’s not just that “one cool app is going away,” it’s that any shiny AI product, especially the demo‑driven ones, can be switched off by a spreadsheet somewhere. Today it’s video. Tomorrow it could be the API you quietly wired into three of your workflows. 😉

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How people are reacting?

Across LinkedIn, Twitter, and Reddit, the reaction is surprisingly aligned. some examples here -

LinkedIn:

this is financial discipline. Impressive tech that doesn’t map to clear, repeatable spend gets cut so compute can be reallocated to enterprise tools, agents, and dev platforms.

Twitter/X:

“end of GPU flex culture.” Sora is being cast as the ultimate demo, amazing to watch, terrible economics, and people assume those GPUs are already earmarked for the next foundation model.

Reddit:

“cool, but I never really needed it.” Threads are blunt about using Sora for a bit, getting wowed, then drifting back to their normal tools, while pointing out that the cost curve was obviously upside‑down.

What I find the most interesting is that, No one’s arguing the tech wasn’t jaw‑dropping. They’re arguing it never crossed the gap from spectacle to necessity.

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I dont’ want to do a OpenAI business model deep dive here, but.

I have to mention this.

OpenAI’s business? ChatGPT subscriptions, enterprise licences, and API usage with custom deals on top, and they do now have revenue 10x billons annulized, ( lets not think of loss for a minute) but it’s sitting on top of a capex plan that involves tens (maybe hundreds) of billions for chips and data centres over the next few years.

In that context, Sora is the first visible casualty of a new rule:

every product has to justify its share of the GPU bill.

I would come back to the whys and other deeper questions like return on compute, margins, durability soon on the Intelligent founder AI.

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So finally what now for video and content creators and competitors?

Sora going away doesn’t kill text‑to‑video. It just forces everyone to grow up.

• Other players (Runway, Pika, Luma, Google’s Veo, plus the China stack) suddenly have more room. BUT also tougher questions from investors and customers about their own economics.

• If you’re a founder or studio, the takeaway is: treat text‑to‑video as a component, not the whole product. Don’t build a business that only works if one proprietary API stays cheap and immortal. I think I said this before as well, in terms of models and other products non AI including. the lesson is same. Use multiple providers, and keep video gen at the edges of your workflow ie. pre‑viz, drafts, B‑roll and not as the only way you ship.

• And most importantly, this all reinforces the thesis from yesterday’s post: the real action is in the “boring” parts of the creator stack. Masking, mattes, clean‑up, localisation, asset search, compliance jobs where AI is quietly making editors 5–10× faster and the cost/ROI maths actually works.

If Sora was peak AI spectacle, the endless synthetic clips with fuzzy business value? the next wave of video AI will look more like invisible plumbing:

agents and tools that sit inside Premiere, Resolve, and Notion, automate the grunt work, and never trend on X.

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On paper, none of these launches look like a “new era of AI” headline. But in the edit bay, they’re a quiet line in the sand:

if AI can grab masks, plan structures, and crank out passable explainers, the part of your job that’s defensible shifts again, away from button‑pushing and and toward taste, narrative, and what you choose to make in the first place.

Lets check out the full round up -

1. Text‑to‑video: structure vs vibes 🧠📽️

TechRadar tested Manus video generator by asking it to build a complete food truck brand from a single prompt, including name, logo, menu, and promo video. The result stood out not just for image quality but for coherence across assets, because Manus planned scenes before generating them.

• the new 12‑tool benchmark from Manus says the real test for AI video isn’t “does it look cool?” but “does it stick to your script from start to finish?”​

• They compared tools like Manus, HeyGen, Runway, and Sora 2 on longer explainers and multi‑scene videos, then score how much each one drifts off‑script.​

For creators making explainers or course content, the practical takeaway is to use a structure-first tool like Manus for the narrative backbone, then drop in cinematic b-roll from Runway or Pika where you need visual punch.

Now compare that to Runway or Sora, which produce stunning individual clips but struggle to hold a narrative thread beyond 15 seconds.

Manus puts itself in the “structure‑first” camp: it breaks your prompt into a plan and scenes before generating, giving it very low drift on long scripts.​

Runway is rated great for visuals but weak on keeping a narrative straight, with high drift on multi‑scene stories.

Sora 2 is framed as incredible for short, rich shots but “very high” drift for full storylines because it doesn’t enforce structure.​ For creators, that means: these models are perfect for b‑roll, intros, and visual experiments—but you still need to own the story arc yourself.

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2. Rotoscoping: from days to minutes 🎬✂️

ONYX Ai Matte 2.5 is a new plugin for DaVinci Resolve/Fusion and Nuke that auto‑creates clean cutouts (mattes) from video using Meta’s SAM 3 and VitMatte.​What it does is -

• Instead of tracing by hand, you can drag a box, tap a few points, or just type “person” or “car” and it tracks that object across the shot.​

• It can follow dozens of objects in one clip (up to 32–64 depending on mode), which is overkill for YouTube but gold for busy scenes.​

• After the first pass, you still get normal controls like feathering and resizing, so you can tidy up hair, motion blur, and messy edges.​

Indie price is about €80 with a 7‑day trial, which is cheap enough that a couple of client edits pay for it.​

Big picture?

roto is no longer a specialist job. mid‑level editors can now do “good enough” mattes without a VFX house.

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3. Adobe Premiere: one‑click masks, no more pen tool 😮🎛️

LinkedIn creator Rob de Winter posted a demo of Premiere AI Object Mask in January 2026, showing how he selected a moving subject with one click and tracked the mask across an entire clip in seconds.

Before this update, the same task meant switching to After Effects, drawing Bezier masks frame by frame, and round-tripping the project back.

Now editors doing privacy blurs for corporate clients or selective colour grades for music videos can stay inside Premiere start to finish.

• Premiere Pro 26.0’s new AI Object Mask lets you hover over a person or object, click once, and auto‑build a mask that tracks through the whole scene.

• Six coloured overlays make it obvious what’s selected, and you can tweak with simple lasso/rectangle tools instead of wrestling Bezier points.

• The updated tracking engine is up to 20x faster than before, so tricky 4K shots that used to take minutes now resolve in seconds.

• Editors can now blur faces, relight subjects, isolate grades, or pin effects to moving areas directly in Premiere, no After Effects round‑trip for basic stuff.

Adobe says the AI runs on‑device and doesn’t use your footage for training, which is a subtle but important trust pitch. Underneath the marketing, this is a land‑grab: the more Premiere eats “simple VFX,” the harder it is to justify moving your whole stack elsewhere.

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So what should creators actually use? 🧰

• Manus is best when you want a structured explainer or training video from a longer script.

• HeyGen is the go‑to for avatar‑style presenter videos with decent lip‑sync, aimed at marketers and educators.

• Runway and tools like Pika/Luma sit in the “make it look insane” bucket: short, cinematic clips, social‑first content, lots of visual flair.

• Classic “AI editors” like VEED and Descript stay strong for editing plus AI assist, especially when you want tight manual control instead of full automation.​

In practice, serious creators will likely run a small stack: one tool for script‑safe explainers, one for fancy b‑roll, plus their main editor that’s slowly absorbing AI masking and clean‑up. What is be your' go-to stack?

What Apple is actually doing?

Over the past few months, Apple has quietly prevented several AI “vibe coding” apps from shipping updates on the App Store.

These apps let users describe an app or game in natural language, have an AI assistant generate code, and then run that app directly inside the host app, often in an embedded web view.

According to reporting from The Information, MacRumors, and others, Apple told the affected developers that these capabilities violate App Store Guideline 2.5.2, which prohibits apps from downloading, installing, or executing new code that changes their own functionality or that of other apps.

The company has also raised concerns about vibe coding apps being used to build software for Apple platforms and to create sophisticated web apps that operate outside the App Store altogether.

There is an important nuance hidden in 2.5.2:

Apple explicitly carves out an exception for apps that “are used for education and teach coding,” as long as the source code is fully viewable and editable by the user. Apple is comfortable with users executing their own code on‑device when the primary purpose is learning.

it becomes far more restrictive when the same mechanics are used to turn iOS into a general‑purpose app platform powered by AI.​

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“We’re not targeting vibe coding” vs reality!

Publicly, Apple’s line is that there is no rule specifically against vibe coding apps. Spokespeople point to “long‑standing” policies like 2.5.2 and updated mini‑app guidance (Guideline 4.7.x) as the basis for these decisions, and say the guidelines are meant to encourage innovation while preserving user safety.

Yet for developers of Replit and Vibecode, the effect is unambiguous:

they cannot ship new iOS releases unless they change how they preview and run generated software or remove the ability to create apps for Apple platforms.

Replit, for example, has reportedly been told to open generated apps in an external browser instead of an in‑app web view, while Vibecode has been asked to drop

support for generating software targeting Apple devices.

Vibe coding’s core UX promise is “I describe what I want, and I can run it instantly on my phone.”

Apple’s interpretation of its rules forces a slower loop:

AI can help you write code, but then it must be packaged, submitted, reviewed, and only then made available to users. That is a fundamental clash between real‑time, iterative creation and centralized safety gatekeeping.

In other words, Apple is not banning the category in name, but it is aggressively policing the behaviors that make vibe coding compelling on mobile.

The fine line around “code vs content”

The heart of the conflict is the collapsing boundary between code and content.

App Store Guideline 2.5.2 says that apps should be self‑contained, must not execute downloaded code that changes features or functionality, and only grant narrow exceptions for educational coding apps where the source is fully viewable and editable. Guideline 4.7 and its 2025 clarifications extend this to HTML5/JavaScript “mini‑apps,” making it clear that dynamically loaded experiences are still subject to full App Store rules and cannot gain extra native powers or bypass review via clever use of web views.

Historically, Apple could treat user‑generated content as inert data for example posts, images, documents, even simple interactive widgets.

Vibe coding tools blow up that distinction by turning natural language “content” into executable apps that run on the device, sometimes with access to platform APIs through the host. From Apple’s point of view, -

once your app starts acting as a general runtime for arbitrary new applications, you are no longer just hosting content; you are effectively operating an alternate app store inside your app.

That is the fine line Apple is drawing:

• AI can help you write code, but the resulting app should be packaged and submitted as its own binary through App Store Connect.

• AI cannot turn your shipped app into an open‑ended execution environment for new, unreviewed apps that evolve over time on user devices.

Escape hatches and architectural nudges!

Apple has not demanded that vibe coding apps disappear entirely;

it has suggested specific redesigns that would bring them back within the lines.

In some cases, the review team has indicated that updates could be approved if:

• Generated apps are run in an external browser rather than an in‑app web view, pushing execution into the same sandboxed web environment Apple already treats as user‑generated content.

• Features that generate apps for Apple platforms are removed, so the tool is no longer a direct competitor to Apple’s own development pipeline and doesn’t flood App Review with auto‑generated submissions.

This is Apple’s architectural guidance in disguise:

keep the “builder” logic in your app, but push the “runtime” for arbitrary experiences either to the open web or into separate, reviewable binaries.

Developer trust, platform asymmetry, and chilling effects

Developers note that some of these apps previously passed review, only to see updates rejected later under the same guidelines, with relatively sparse explanations. That kind of rule‑by‑interpretation erodes trust: if you’re building ambitious AI tooling on iOS, you have to assume the goalposts might move once your product gains traction.

The asymmetry across Apple’s own platforms makes the picture clearer.

On macOS, Apple is happy to support powerful AI‑assisted tools inside Xcode and other IDEs, because Macs remain open platforms where users can run arbitrary code. In the browser, vibe‑coding runtimes can still operate relatively freely, spinning up rich web apps with no App Store involvement at all.

The strict line is drawn specifically around iOS as a locked‑down, curated environment.

All of this pushes the most experimental work in AI‑assisted creation away from iOS: toward the web, Android, and desktop environments where the rules are more predictable and execution is less tightly policed.

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So, is Apple banning vibe coding apps?

Formally, no:

Apple says it has no rule that targets vibe coding by name.

Practically, it is using a combination of Guideline 2.5.2 (no downloaded executable code) and updated mini‑app rules to clamp down on vibe coding apps that act as unbounded runtimes for AI‑generated apps inside an iOS container.

The fine line it is drawing is around code vs content:

AI‑generated code is acceptable when it leads to traditional, reviewable apps; it becomes unacceptable when it turns an App Store app into a shadow platform for new apps that Apple never explicitly approved.

For developers, that means vibe coding is welcome as a tooling paradigm, especially on Mac and in Xcode, but constrained as a runtime paradigm on iOS.

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In conclusion, Apple isn’t banning vibe coding so much as it is defending its gatekeeping model for iOS, drawing a hard boundary wherever AI threatens to blur content into code in ways that bypass App Review.

Whether that boundary holds under the combined pressure of AI progress, developer demand, and regulatory scrutiny, will determine if this crackdown is a temporary skirmish or the opening phase of a longer war over who controls where AI‑written software is allowed to run.

The reality is that the moment you put a chat-based AI agent into production, users will, intentionally or accidentally, paste PII, API keys, credentials, and NSFW content straight into the conversation. Your agent will dutifully process it, log it, maybe even forward it to a third-party API.

Well, Congratulations, you now have a compliance incident.

This isn’t a theoretical problem. It’s happening right now in every organisation experimenting with AI agents.

The good news » you don’t need to build guardrails from scratch. 👍

If you’re running workflows in n8n, there’s a dedicated Guardrails node that can intercept and block problematic inputs before they ever reach your agent. OpenAI offers a Moderation API that flags harmful content categories. And for enterprise teams on AWS, Bedrock has built-in guardrail capabilities that can filter inputs and outputs at the model layer.

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We built a simple workflow (think of it as a conveyor belt for messages) that shows what happens when someone tries to send something dodgy to an AI agent.

Here’s the journey a message takes:

• Step 1: User sends a message → Someone types something into the chat. This could be a normal question, or it could contain a credit card number, a password, an API key, or inappropriate content.

• Step 2: The Guardrail checks it first » Before the message reaches the AI, it passes through a security checkpoint (the Guardrail node). Think of it like a nightclub bouncer, it inspects every message and decides if it’s safe to let through.

• Step 3a: If the message is dodgy → BLOCKED » The guardrail catches it and stops it dead. The user gets told their request can’t be processed. The AI agent never even sees the sensitive data. Crisis averted.

• Step 3b: If the message is clean → it goes through » The message passes the check and continues down the line to the AI agent, which processes it normally using tools like memory (so it remembers previous conversations) and web requests (so it can fetch information or take actions).

The key point:

Without this guardrail, every message, including ones containing your company’s secrets or a customer’s personal data — goes straight to the AI model. With it, bad requests get caught at the door.

The key takeaway:

guardrails aren’t optional safety theatre. They’re the difference between a prototype and a production system.

Build them in from day one, not after your first data leak.

So What changed?

In the new Build mode, you describe the product you want and AI Studio generates three things at once:

1. the interface,

2. the underlying logic, and

3. a live preview you can click around.

It runs entirely in your browser at https://aistudio.google.com

so there’s no local setup, no repo, no “install Node first” step.

The “full‑stack” shift is that there’s now a real server behind your app, not just browser code.

That server can safely hold API keys, talk to external services, and keep shared state so multiple users see the same data in real time.

In practical terms, you can say “add login and save my data,” and it wires up Firebase Auth and a database for you.

What “full‑stack” actually means here?

• Server runtime: AI Studio now includes a server-side Node.js runtime so your app has backend logic, not just browser-side React or HTML.​

• Secrets and APIs: You can store API keys and call third‑party services (Stripe, SendGrid, custom REST APIs, etc.) securely from that server code.​

• Databases and auth: Native integration with Firebase (Firestore + Firebase Auth) lets the agent spin up persistent storage and user authentication when your app needs them.

• npm ecosystem: The Antigravity coding agent can auto‑install and wire up npm packages (axios, Three.js, UI kits, etc.) when you ask for features that require them.

• Real‑time & multiplayer: Because there is now shared server state, you can ask for things like “live chat”, collaborative whiteboards, leaderboards, or multiplayer games and have them managed centrally.

Why this is different from before?

• Previously, AI Studio was mostly a prototype playground: great for front‑end demos and Gemini prompts, but you had to export the code and wire a backend elsewhere (Firebase Studio, your own stack) for anything real.

• With the March 2026 update, the same environment can take you from prompt → UI → backend → live, production‑style app, including Firebase integration and external APIs, all orchestrated by the coding agent.

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SO What “Full‑Stack” Actually Adds in simple words?

The big change is that AI Studio now includes a server‑side runtime behind the scenes.
That server can:

• Store secrets like API keys safely so they’re not exposed in the browser.

• Call external APIs (for example, a weather API or payment API).

• Keep shared state so multiple users can see the same data in real time (chat apps, collaborative boards, multiplayer tools).​

You can even ask it to use popular npm packages; the agent will install and import them for you.

Technical guide: - Full‑stack / npm / secrets: https://ai.google.dev/gemini-api/docs/aistudio-fullstack

Useful links if you want to poke it yourself:

• Build mode basics: https://ai.google.dev/gemini-api/docs/aistudio-build-mode​

• Full‑stack runtime (APIs, secrets, multiplayer): https://ai.google.dev/gemini-api/docs/aistudio-fullstack​

• Friendly beginner guide: https://exploreaitogether.com/how-to-use-google-ai-studio/​

Why this matters for indie hackers and no‑coders?

If you’re non‑technical or “code‑curious,” this quietly moves the goalposts on what you can ship solo.

A single prompt can now get you »

• a working web app,

• basic auth,

• a database, and

• a one‑click deploy to Cloud Run with a public URL.

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That’s the stack most SaaS MVPs need. ✅

For no‑code founders, the interesting bit isn’t that Google made yet another builder; it’s that AI Studio sits directly on the same Gemini API and Google Cloud stack you’d eventually grow into anyway. You can stay in the sandbox while you’re validating an idea, then export the code or call the same models from a “real” backend once something hits.

Good stepping‑stone resources: 👍

• “How to build an app with AI” on Google Cloud: https://cloud.google.com/use-cases/how-to-build-an-app-with-ai​

• Getting started with Gemini for web apps: https://developers.google.com/learn/pathways/solution-ai-gemini-getting-started-web​

The opportunity (and the trap 🪤 )

The opportunity is that you can run more experiments, faster, with less engineering overhead.

Idea → prompt → usable tool, in an afternoon is now genuinely realistic for a large class of apps.

That’s free surface area for testing positioning, pricing, and workflows before you drag actual engineers or your future self – into a rewrite.

The trap? and there is one.

Assuming this replaces engineering entirely. Under the hood, you’re still getting a conventional web app, with all the usual issues around security, data modelling, and long‑term maintenance. So the lesson here is ? —

Treat AI Studio as an acceleration layer for validation, not as the final home for a business you expect to be running in five years.

Weekend Build Idea?

Ship a Tiny B2B SaaS 🐝

• Idea: “Inbox Triage for Founders” – connect Gmail, auto‑tag investor vs customer vs admin emails, and show a simple priority queue in a web dashboard.

• Prompt: In AI Studio Build mode, ask: “Create a B2B inbox triage dashboard for startup founders. Connect to Gmail via API (stub the auth flow), display a three‑column kanban (Investor, Customer, Admin), and let me drag emails between columns. Add basic login and store board state per user.”

Why it’s interesting?

You get to test an opinionated workflow (how founders should process mail) without building the whole product: one screen, one workflow, live URL you can share with 3–5 friends for feedback.

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The most interesting frontier model Perplexity quietly added this week isn’t from OpenAI or Anthropic. It’s Nvidia’s Nemotron 3 Super, a 120B‑parameter, open‑weight model with a native 1M‑token context window and Open‑weight here means enterprises can fine‑tune and deploy it on their own infrastructure, not just via a single vendor API. Its available today inside Perplexity, on OpenRouter, Hugging Face, Together, and of course on build.nvidia.com. Here’s why this 1M‑token, open‑weight, everywhere‑you‑need‑it part matters more than the 120B headline number X is going with.

Before talking about why this matters, it’s worth asking a simple question: where can you actually use Nemotron 3 Super today?

Where you can actually run it?

• Perplexity: Nemotron 3 Super 120B with Thinking is in the Perplexity advanced models lineup for Pro/Max. it’s orchestrated alongside ~20 other models in Computer and available via API.

• Nvidia: Hosted on build.nvidia.com and as a Nemotron 3 Super NIM micro-service, so it runs from workstations to cloud DGX.

• Open platforms:

  • Hugging Face: open weights plus Transformers/vLLM integration and Dell/HPE enterprise hubs.

  • OpenRouter: exposed as a public endpoint among other leading open‑weights.

  • Together AI, Baseten, others: dedicated inference offerings emphasizing 1M‑token, high‑throughput, multi‑agent workloads.

Once you know it’s everywhere, the next question is how Nemotron actually stacks up against other long‑context models.

Key comparative points -

• 1M‑token context: Nemotron is one of the first mainstream open‑weights with a native 1M context across Nano and Super, using Mamba layers to keep memory constant as sequence length grows.

• MoE efficiency: Despite 120B parameters, only ~12B are active per token, giving Nvidia claimed 5x throughput and ~50% faster generation vs comparable open models in multi‑agent workloads.

• Open‑weight plus recipes: Nvidia isn’t just shipping a checkpoint; they’re publishing datasets, training recipes, and an open Nemotron license aimed at enterprise fine‑tuning and on‑prem.

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What 1M tokens unlocks?

So what does a native 1M‑token window actually buy you in practice?

1. you can treat an entire service or codebase as a single unit of reasoning: load tens of thousands of files into one context and ask Nemotron to map dependencies, spot dead code, or propose refactors without brittle chunking.

2. multi‑agent workflows get simpler: a planner, critic, and executor can all share the same long‑lived scratchpad, so you don’t keep throwing away state every few thousand tokens.

3. you can build ‘document brains’ that actually see everything: full policy libraries, SOPs, or product manuals go into one window, instead of wrestling with RAG configs and retrieval edge cases.

Those are the workflows Nemotron is aiming at, and they line up cleanly with Nvidia’s broader ‘agentic AI stack’ story.

The Nvidia narrative:

• Nemotron 3 is framed as the model layer of Nvidia’s “agentic AI stack” (GPUs → CUDA → NeMo/NIM → Nemotron).

• The 1M‑token angle is about making multi‑agent and long‑horizon workflows economically viable: entire codebases, full document stores, or long‑running plans in a single context window.

• The $26B, five‑year plan is about turning “open‑weight Nvidia models” into the default choice across clouds, on‑prem vendors (Dell, HPE), and AI‑native companies like Perplexity.

If CUDA was how Nvidia captured the AI compute stack, Nemotron is its attempt to do the same at the model layer, except this time the weights are open and already running behind your favorite apps. In other words, the next default enterprise model may not be the one with the loudest launch, but the open‑weight one that quietly ships everywhere first.

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Anthropic just made 1 million token context generally available on both Claude Opus 4.6 and Sonnet 4.6. That means you can now load entire codebases, hundreds of legal contracts, or massive document sets into a single conversation. A year ago, this was bleeding-edge. Today, it’s a checkbox.

And that’s exactly the point.

Because Anthropic isn’t first here. They’re only catching up.

The Chase

Here’s what happened in roughly 12 months:

• DeepSeek ships V3 with sparse MoE, open weights, and token pricing so cheap it makes Western labs look like luxury brands​

• Google responds by launching Gemini Flash at $0.30 per million tokens » directly matching DeepSeek’s price band — with 1M context already baked in

• OpenAI prices GPT-5 at $1.25 per million input tokens » down from GPT-4’s original $30. TechCrunch calls it the start of a “price war”

• Anthropic, the last holdout on long context, ships 1M on both Opus and Sonnet in the same month

• DeepSeek V4 » a trillion-parameter, native multimodal model with 1M context and O(1) memory » still hasn’t officially shipped. And it’s already forced all of the above

Every move in this sequence is a response to the move before it.

DeepSeek set the price. Google matched it. OpenAI undercut their own previous pricing by 24x. And Anthropic just said “fine, we’ll match the context window too.”

That’s not independent innovation. That’s a chase.

But Context Length Alone Isn’t the Story

Here’s where it gets interesting. Anthropic didn’t just announce 1M tokens. They posted a benchmark chart showing retrieval quality at scale, and it tells a brutal story:

• Claude Opus 4.6: 78.3% accuracy at 1M tokens​

• Gemini 3.1 Pro: 65.1%

• GPT-5.4: collapses to 36.6%

• Claude Sonnet 4.5: falls to 18.5%

So GPT-5.4 can technically accept 1M tokens. But it can only actually find things in that context about a third of the time.

Anthropic is making a smart argument here: “Sure, everyone has 1M now. But we’re the only ones who can use it properly.”​

And that’s the new battleground. 💥

Not how many tokens you can accept, but how well you reason across them. And it’s exactly where DeepSeek V4’s Engram architecture ( I covered this in yesterday’s deep dive, also linked below because I missed a few things ) which retrieves static knowledge in constant time regardless of context length, is designed to compete.​

The 360° Convergence Nobody’s Talking About

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If you zoom out and the pattern is impossible to miss actually. I was actually focussed on just Deepseek yesterday so din’t really go comparing models but, now that I see, every serious AI lab is now building toward the same blueprint:

• Sparse MoE » DeepSeek V4 activates ~32B of 1T parameters per token. Mistral Large 3 activates 41B of 675B. Sparse is the new default

• 1M+ context » Google, Anthropic, DeepSeek, Grok (2M). A year ago 200K was premium. Now 1M is table stakes

• Native multimodal » text, image, video, code in one model. DeepSeek V4, Gemini, Grok 4, Llama 4, Qwen 3.5 — all native

• Aggressive pricing » DeepSeek at $0.30/1M. Gemini Flash at $0.30/1M. GPT-5 at $1.25/1M, down from GPT-4’s $30/1M. That’s roughly a 100x collapse in two years

• Open weights » DeepSeek (expected MIT/Apache), Mistral (Apache 2.0), Llama, Qwen, GLM-5. Open-weight models now trail proprietary ones by about three months

👉 No single lab leads on all five.

DeepSeek V4 is trying to be the first to hit all five at once. That’s what makes it matter, not that it’s better on any one axis, but that it’s forcing a convergence that reshapes the entire market.

And the convergence itself is the real story. Not DeepSeek vs Anthropic. Not China vs the West. But the fact that the model is becoming infrastructure, like cloud compute, earlier and like bandwidth, where the long-term trend is relentlessly cheaper, more capable, and more interchangeable.

If you’re building products on top of AI, your costs are collapsing, your context windows are exploding, and your switching costs are dropping. so the point remains the same that I made yesterday in fact this Anthropic announcement just proved the theory. and,

This is the best news the application layer has had in years. 🥳

What I published Yesterday!

A deep dive on DeepSeek V4 for The Intelligent Founder,

• technical breakdown on Engram O(1) memory,

• sparse MoE, real pricing comparisons,

• risk analysis, and

• the complete founder playbook

I did a bit of DeepSeek vs the West too. This convergence angle, however which I can see clearly now, is something I didn’t fully connect until Anthropic’s 1M announcement landed just now and the pattern clicked into place. I think its too important to leave out, so I’m adding it here.

DeepSeek V4 Deep dive

This is what I do across two publications: AI Unfiltered catches the moves in real time, The Intelligent Founder AI breaks down what to actually do about them. If you're only subscribed to one, you're getting half the picture. so do sign up for both.

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Perplexity rolled out Computer in Feb end, and yesterday did an AMA in SFO, and While i have much to say about their roadmap and the whole orchestration layer, I just wanted to pick on the drama they flared up in the event, which obviously is trending on twitter today.

the company’s CTO Denis Yarats says they’re “moving away from MCP internally” in favor of APIs and CLIs, X obviously went immediately back to “MCP sucks,”debate which is been going on for a while IMO. As usual, both sides the “MCP is dead” camp side and “MCP is the future and definitely not dead” side, are wrong in interesting ways. MCP neither sucks nor saves you; it’s just a protocol.

The real story is about where you try to use it.

The Perplexity spark: MCP as the wrong layer

Perplexity is a good bellwether because they’ve gone deeper into agentic infrastructure than most, and this is exactly why I wanted to add perplexity example here.. Perplexity Computer as a multi‑agent orchestrator, Comet as the browser shell, and Personal Computer as an always‑on Mac mini host wired into local files and apps. Now against that backdrop, if Denis is saying “we’re ditching MCP internally” is not a random hot take, it’s a signal that, at scale,

MCP is the wrong substrate for the guts of their AI OS.

Internally, they lean on boring APIs and CLIs because that’s where latency, observability, and security matter most for big customers. Externally, they still ship an official MCP server and first‑class MCP connectors in the Mac app so Claude Desktop, OpenClaw‑style agents, and local tools can plug in with minimal glue. In one sentence:

Perplexity didn’t kill MCP, they pushed it back to where it belongs. at the edges of their AI OS, not at the core.

The “MCP sucks” camp - real problems, wrong conclusion

its not all fluff. there are enough substantive critiques behind the memes. and Security and operational people have been yelling about them for a year -

• weak default security,

• tool poisoning and prompt injection,

• messy identity and auditing, and

• non‑trivial cost and latency overhead.

Malicious or sloppy servers can embed harmful instructions or shadow tools. if your platform treats MCP servers as trusted extensions, you’ve just expanded your attack surface in ways that are hard to reason about.

Beyond security, high‑volume streaming, batch ETL, or rich interactive flows often map more cleanly to event buses or normal APIs than to MCP’s tool‑call mental model. From that angle, it’s easy for infra‑minded teams and some VCs to conculde that “MCP is bloated, let’s roll it back and replace it with lighter ‘agent skills’ or plain APIs.” but jumping straight to “MCP is dead” means, you miss where it’s actually working.

The “MCP is maturing” camp: not dead, just leaving the hype peak

On the other side, you have the “MCP is fine, your implementation sucks” crowd, and they have receipts too. One‑year retrospectives show a growing ecosystem of MCP servers and SDK downloads, with IDEs, local agents, and commercial platforms all converging on the spec. Case studies from vendors point to higher task completion and lower integration costs when teams standardize on MCP for agent‑to‑tool interaction instead of inventing bespoke plugin formats.

“State of MCP” posts now frame 2025–26 as the classic hype‑cycle move, from “use it for everything” to “use it where interoperability and real‑time context actually matter.”

Their main claim is that MCP is moving from flashy demo material into boring enterprise plumbing at the edge of systems, not at the core and that’s exactly what maturation looks like.

What’s happening is the scope correction!

if you put Perplexity’s move next to these broader signals, a pattern emerges.

MCP is bad as your internal micro-service fabric, your primary security boundary, or a mandatory wrapper around every existing API. But It’s good as a standard way for agents to talk to tools and data sources you don’t fully control, as a publishing format for reusable capabilities across IDEs and local agents, and as a connector layer on desktops and “AI PCs” reaching into local apps and services.

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In other words, MCP’s natural home is at the edges of an AI system. between your orchestrator and the messy outside world, not as the core nerve system inside your own infra. Perplexity’s decision is best read as a high‑profile scope correction, not a death sentence.

So where should you use MCP?

Strip away the noise and a simple rule of thumb emerges:

Use MCP when:

• You want third‑party agents to call your capabilities with minimal custom glue.

• You need a single tool interface that can be reused across multiple LLM vendors and runtimes.

• You’re connecting to local tools or self‑hosted services from a desktop or always‑on box and want a consistent agent‑friendly protocol.

Avoid MCP when:

• You’re wiring together your own micro-services, data pipelines, and long‑running jobs inside one company.

• You need strong, audited, fine‑grained security and governance as the primary control plane.

• You can already expose a clean API/SDK and don’t need cross‑vendor agent interoperability.

MCP doesn’t suck. Overusing it does.

Perplexity isn’t killing MCP. they’re just refusing to build an entire AI OS on top of a protocol that was never meant to be the foundation in the first place.

If you’re building your own AI lab, copy this lesson - make MCP a plugin layer, not your nervous system.

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That doesn’t mean “NO IMPACT.”

It means the way AI is reshaping work is slower, more targeted, and more subtle than the loudest headlines suggest.​

From “AI could do this” to “AI is doing this” -

Most earlier studies ask: “Which tasks could AI help with?”

They look at detailed task lists for hundreds of jobs (from databases like O*NET), then rate how much large language models could speed them up. That gives you a theoretical exposure score.​

Anthropic keep that idea but add something important:

they use real usage data from Claude to see which tasks people are actually using AI for in their day jobs.

They combine three ingredients:​

• Task lists for ~800 US occupations (O*NET)

• An earlier measure of which tasks are technically feasible for LLMs

• Millions of Claude conversations and API calls tagged as work‑related

From this, they build “observed exposure” – basically:

out of all the tasks AI could help with, where is AI already being used in professional settings, especially in automated or semi‑automated ways?​

The result? -

AI is far from its theoretical ceiling. Even in tech‑heavy jobs like software and data roles, AI is currently touching only part of the work. Claude, for example, is used on about a third of tasks in Computer & Math occupations, even though far more tasks look technically doable on paper.​

Who is most exposed right now?

When Anthropic roll this up to full jobs, the pattern is clear.​

• High observed exposure: computer programmers, customer service reps, data entry workers are at the top. Programmers show around 75% coverage of their tasks, data entry sits around 67%.

• Low or zero exposure: roughly 30% of workers are in jobs where AI barely shows up in the data. Think cooks, lifeguards, bartenders, mechanics, dishwashers, dressing room attendants.

The demographics of the “high exposure” group may surprise you. These workers are more likely to be:​

• Older

• Female

• White or Asian

• Better educated

• Higher‑paid

People with graduate degrees are almost four times as common in the highly exposed group as in the unexposed group.

So the first wave of AI impact is not focused on low‑wage workers.

It’s landing on office workers, coders, analysts, and other professionals.​

Anthropic also compare their exposure measure to official US forecasts for job growth out to 2034. Jobs with higher observed exposure are projected to grow a bit more slowly.

a 10‑point increase in exposure is linked with about a 0.6‑point drop in projected growth, while a purely theoretical exposure metric doesn’t show this link. That suggests their “observed” measure is picking up something real about where pressure is building.​

So, are people losing jobs to AI?

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This is the core question. Anthropic tackle it directly by looking at unemployment.

They split workers into two groups:​

• People in the most AI‑exposed occupations

• People in occupations with effectively zero AI exposure

Then they track unemployment rates for both groups, using US survey data, before and after late 2022 (i.e. the ChatGPT era).​

Findings?

• No clear sign of mass job loss so far. Unemployment rates for highly exposed workers have not climbed sharply compared to the unexposed group. Any differences are small and not statistically meaningful.​

• This result holds across different exposure cut‑offs and even when they use other data sources, like unemployment insurance claims.​

They are also asking if this framework pick up a big shock if one happened? The answer appears to be yes. A “Great Recession for white‑collar workers” – where unemployment in exposed jobs doubles from around 3% to 6% – should clearly show up in their approach. It simply isn’t there.​

The one wrinkle is young workers. Earlier research suggested that people aged 22–25 might be finding it harder to get into AI‑exposed roles. Anthropic find something similar:​

• Unemployment for young workers in exposed jobs is mostly flat.

• But when they track job moves over time, the share of young workers starting new jobs in highly exposed roles seems to fall in 2024. They estimate roughly a 14% drop in “job‑finding” into these roles versus 2022, and this doesn’t show up for workers over 25.​

The authors stress this is only weakly significant and measurement is tricky. But it fits a common‑sense story: companies don’t instantly fire lots of knowledge workers; they quietly hire fewer juniors into roles where AI is boosting productivity.​

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What this means for the AIUnfiltered crowd? + How to PERSONALIZE Anthropic’s framework.

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Anthropic’s framework is a measurement tool, but we can treat it like a radar for our own jobs and companies. Instead of asking “Will AI take my job?”, a better question is: “How much of my work already looks like the tasks Anthropic see AI touching today?”​

Now, If you read AIunfiltered, you’re almost certainly in the high‑exposure zone:

• building software,

• running operations,

• writing content,

• handling customers,

• doing analysis.

Anthropic’s work suggests three practical takeaways.​

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Back in September 2025, I wrote a long essay called “Bringing All AI Models Under One Roof,” on the intelligent founder AI ( linked here 👇 ),

Bringing All AI Models Under One Roof

arguing that the real AI operating system wouldn’t be a single, blessed model. it would be the switchboard that quietly routes between all of them, behind the scenes. whoever owns the router between enterprise data and a rotating cast of foundation models wins the long game, regardless of which lab is on top in a given quarter

In my last post couple of posts as well, whether from the point of models release, or the AI infrastructure stand point, I said that shipping with just “one best model” is now like running your whole SaaS on a single API endpoint and not advisable, we need a portfolio approach now.

Death of ‘One Best Model’

The Story in September was about Microsoft implementing the multi-model enterprise approach.

Now Over the last week, a new storyline has taken over Twitter, Reddit, and the tech press: “Microsoft, still a major shareholder in OpenAI, is using rival Anthropic’s models to power its Copilot workplace tools.”

the messaging ?

» 👉 next Microsoft has seen enough,

» 👉Claude Cowork is better than Copilot, and

» 👉 Redmond is quietly pivoting away from OpenAI.

It’s a clean narrative. It’s also mostly wrong.

If you just skim the headlines and viral posts, you’d think this is a dramatic breakup!

Microsoft dumps GPT‑4 for Claude, Anthropic “wins the office,”

OpenAI gets sidelined.

There’s newsletters calling Claude a “Copilot killer,” hot‑take threads about Microsoft “panicking,” and screenshots of internal angst leaking into public view.

Now compare that with what Microsoft actually shipped.

In its own Copilot Studio announcement, the language is boring but revealing:

That’s not a divorce.

That’s a router doing exactly what we talked about last year, adding another model behind the same interface, so the platform, not the model vendor, stays in control.

And, the new March 9, 2026 Copilot Cowork announcement is about an agentic experience inside Microsoft 365: a long‑running worker that plans and executes tasks across Outlook, Teams, Excel, etc., powered by Work IQ and Claude Cowork tech.

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What this actually means?

• OpenAI is still the default brain. Even in Microsoft’s own Copilot Studio doc, OpenAI remains the default model for new agents; Anthropic is an extra option you can route to when it makes sense.

• Anthropic is now a first‑class backend, not a sidecar. Enterprises can explicitly pick Claude models for specific agents or workflows (for example, long‑horizon reasoning or sensitive research tasks) while leaving the rest of Copilot traffic on OpenAI.

• Copilot Cowork is just one agent on top of that router. The March 9 Copilot Cowork launch is a new agentic UX inside M365 that uses Claude Cowork/Work IQ to run multi‑step tasks across Outlook, Teams, Excel and your files—but it lives inside the same multi‑model Copilot fabric, not outside it.

• The power shift is at the orchestration layer. Microsoft’s moat here isn’t “our model beats yours,” it’s “our identity, data, and workflow layer can swap models in and out without retraining 400M Office users.”

Why this matters if you’re building anything on AI?

• Don’t build for “one true model.” If Microsoft is hedging at this depth, your startup definitely shouldn’t be hard‑wiring itself to a single provider. Treat models as interchangeable parts and design for routing from day one.

• Compete on workflow, not logo. Your users don’t care whether GPT‑4 or Claude or Gemini answered the question; they care that the task is done, safely, inside their existing tools and data boundaries. That’s where your product differentiates.

• Watch the orchestration patterns. Features like Copilot Cowork are early blueprints for how “always‑on” agents will sit on top of multi‑model backends: grounded in tenant data, operating with least‑privilege permissions, and abstracting away which model did the work.

So when you see ‘Microsoft uses rival Anthropic to power Copilot’ on your feed this week, mentally translate it to: ‘The router I wrote about in 2025 is now live in production and the platforms are moving faster to de‑risk any single‑model dependence than most startups shipping on top of them.’

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The headlines are all about new IQ scores and benchmarks. The real shift though is that you win by routing work across multiple models based on cost, latency, risk and sovereignty, not by arguing about a single “best” model.

Here’s the roundup of what happened this week. -

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1. GPT‑5.4 Turns ‘Chatbots’ Into Back‑Office Staff

What released
OpenAI launched GPT‑5.4, its new frontier general‑purpose model with up to a 1M‑token context window, stronger reasoning/coding and native “computer use” agents that can plan and execute multi‑step tasks across spreadsheets, email and browsers.

Why it matters
You can now point an agent at an entire repo, contract pack or inbox and let it decide what to do, instead of wiring dozens of brittle steps around a chatbox. For internal ops, that’s the difference between “AI assistant” as a UX gimmick and “AI analyst” that actually removes process work.

2. Gemini 3.1 Flash‑Lite Is the New Default for ‘AI in the UI’

What released
Google shipped Gemini 3.1 Flash‑Lite, its fastest and cheapest Gemini 3.x tier, optimized for low‑latency, high‑volume calls via the Gemini API and Vertex AI, with pricing around 0.25 dollars per 1M input tokens and 1.50 dollars per 1M output.

Why it matters?
This is the obvious choice for search boxes, inline help, notifications and mobile UX where users won’t wait and CFOs won’t tolerate GPT‑level costs. If you want “AI everywhere in the product” instead of just in a single chatbot, Flash‑Lite is the workhorse.

3. Gemini 3.1 Pro Is Google Cloud’s New Default Brain

What released
Gemini 3.1 Pro, launched in February and rolling into stacks now, is Google’s flagship model with a 1M‑token context, strong reasoning scores on complex benchmarks and multimodal support for long audio and video.

Why it matters?
If you’re already in GCP, this is your default “serious” model:

1. one vendor for infra and AI,

2. better governance story, and

3. fewer cross‑cloud hacks to wire OpenAI on the side.

For analytical workloads, it’s the Deep Think engine behind Google’s own tools—so you’re closer to what they actually use internally.

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4. DeepSeek V4 Makes ‘Roll Your Own Frontier Model’ Real

What released
DeepSeek V4, the next multimodal model in DeepSeek’s line‑up, is currently rolling out with targeted frontier‑class reasoning, a 1M‑token context window, and a strong focus on coding and tool use. It builds on the V3 series that established DeepSeek as a primary open‑source disruptor.

Rollout Status & Variants

1. V4 Lite (”sealion-lite”): Currently in closed-door testing under strict NDAs. This ~200B parameter version has already demonstrated the ability to generate complex SVG graphics from minimal code.

2. Full Flagship V4: Expected to launch publicly in the first week of March 2026.

3. Local Deployment: DeepSeek is expected to maintain its “open-weight” strategy, with quantized 4-bit versions likely requiring ~350GB–400GB of VRAM (e.g., Mac Studio clusters or quad-RTX 5090 setups).

Core Architectural Innovations

• Engram Memory: A conditional memory system that separates static pattern retrieval from dynamic reasoning, allowing for near-perfect recall across the 1M-token context.

• MODEL1 / Hyper-Connections: Uses manifold-constrained connections (mHC) to compress the entire context into a usable state for repository-level coding.

• Sparse FP8 Decoding: A hybrid precision approach that provides a reported 1.8x inference speedup with minimal accuracy loss.

Why it matters?
It represents the first frontier-level model many infra‑savvy or non‑US teams can seriously consider self‑hosting or running closer to home. This allows them to trade operational complexity for long‑term cost efficiency and data control, which fundamentally shifts the negotiating power away from proprietary US labs regarding pricing and terms.

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5. GPT‑5.3 Turns Models Into Cloud‑Style SKUs

What released
The GPT‑5.3 family of Instant, Thinking, Pro, and Codex is now a clearly segmented set of SKUs, with GPT‑5.3‑Instant optimized for speed and conversational quality while other variants lean into deeper reasoning or code generation.

Why it matters
You’re no longer supposed to pick “the GPT model”, you’re supposed to route per job:

1. Instant for support and onboarding,

2. Thinking for complex analysis,

3. Codex for engineering workflows.

That’s the cloud‑infrastructure mindset finally reaching model choice.

6. Claude 4.6/4.7 Is the ‘Less Embarrassing’ Enterprise Brain

What released
Anthropic rolled out Claude Sonnet 4.6 (and Opus 4.6) with improved refusal behaviour and fewer hallucinations, and 4.7 variants are now showing up on March leaderboards as the next iteration of the Claude 4 line.

Why it matters
For legal, finance, compliance and ops, Claude 4.6/4.7 is the “don’t embarrass me in front of the board” option meaning -

safer defaults with fewer confidently wrong answers while still competing on reasoning. It’s not the hottest benchmark winner. but it’s the one that gets through risk committees.

7. Grok 4.20 Is the Native Model for the X Firehose

What released
xAI’s Grok 4.20, a multi‑agent architecture tuned heavily on real‑time X data, moved from curiosity to a tracked model in March release lists.

Why it matters
If your product leans on social sentiment, live news or X’s data firehose, Grok is likely to see that signal first and internalise it fastest, making it a rational pick in routing engines for “live” contexts.

It’s less of a toy and more of a specialist for time‑sensitive work.

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8. The China Stack Quietly Became a Real Alternative

What released
Qwen 3.5, GLM‑5, MiniMax M2.5, ByteDance Seed 2.0 (Lite/Pro), Mercury 2 and LongCat‑Flash‑Lite all landed in late February and early March as serious long‑context, high‑quality models from Chinese and regional players.

Why it matters?
Together they form a viable non‑US model stack:

1. better support for Asian languages,

2. more options for local hosting and data residency, and

3. sharper price competition against US labs.

If you ship globally, this is the moment a “China stack” stops being a research curiosity and becomes part of your vendor shortlist.

9. The Uncensored Local Stack Is Now ‘Good Enough’ for Real Work

What released
March’s local‑LLM update brought roughly 20 new uncensored models, including GLM‑4.7‑Flash‑Uncensored “Heretic” variants, Dark Champion/Darkhn lines, an uncensored Llama 3.2 3B Instruct (with GGUF builds) and Qwen3‑4B “Thinking” uncensored.​

Why it matters?
On a 6–24GB GPU, you can now run usable, unfiltered local models with no per‑token bills, perfect for internal tools, edgy experiments and clients who care more about control and sovereignty than PR optics.

That’s the quiet margin story under the frontier model hype.

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Your Monday Playbook: what you actually do with this!

Don’t pick “the best model.” Pick three jobs and assign models to each.

1. Heavy reasoning job (analysis / agents / complex calls).
Test GPT‑5.4 vs Gemini 3.1 Pro vs Claude 4.6/4.7 on one real workflow:

a contract review, an incident post‑mortem, or a complex data question. Log cost per run, latency and error rate, not vibes, and crown a winner for this job only.

2. In‑product UX job (search, help, inbox, mobile).
Put Gemini 3.1 Flash‑Lite up against GPT‑5.3‑Instant and one China/local option on a real user flow (search query, help centre, inbox triage).

Pick whichever gives acceptable answers at the lowest cost and latency, then standardize on it for all “AI in the UI” work.

3. Cheap automation job (internal tools, unsexy tasks).
For things like ticket triage, log summarization or rough drafts, test DeepSeek V4, one China model and one uncensored local model on your own infra. Choose the one that is “good enough” at the lowest all‑in cost (API or GPU) and route that entire class of workload to it.

Then do this very boring but important part:» lock this routing in for 6–8 weeks and ignore new launches unless they beat your current choices on cost or reliability, not just on leaderboard screenshots.

Conclusion

If last year was about arguing “Claude vs GPT vs Gemini”, this week is the moment that question stopped being useful.

From here on, the game for anyone who actually ships product is simple:-

Treat GPT‑5.4, Gemini, DeepSeek, Claude, Grok, the China stack and local models as a portfolio, route each workflow to the model that wins on cost, latency, risk and sovereignty, and let everyone else keep fighting about which mascot is smartest.

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