Coinbase CEO (Brian Armstrong) provided 5 approaches they used to cut AI spending nearly in half: make AI costs visible, route tasks to cheaper models, cache more, keep context lean, and use open-weight models where they are good enough.

→ Business Insider

After months of tokenmaxxing, people are finally coming to their senses: this was never going to be sustainable if every internal workflow became an all-you-can-eat token buffet.

Armstrong’s advice is basically the grown-up phase of AI adoption: stop sending every tiny task to the most expensive model in the room.

But this wouldn’t be worth our time, would it?

What I want you to pay attention to here is the use of open-weight models. More and more, you’d see how people rely on open-weight ones more than the commercial ones, knowing that 99% of the day-to-day can be resolved by a good enough model, whereas only a small portion of companies and teams can justify their most advanced AI usage.

Then the next question would be, where does this leave the frontier AI labs?

2. Meta hits Google’s capacity wall

Google reportedly limited Meta’s use of Gemini after Meta asked for more capacity than Google could provide. The reported cap slowed some internal Meta AI projects and forced teams to ration token usage.

→ Financial Times

This is the same logic I wrote about in the Cursor and SpaceX piece (it’s coming out soon! waiting for the audio to be processed), just with different actors.

There it was SpaceX and Anthropic. Here it is Google and Meta.

Across the entire supply chain, the clean profit pool keeps showing up layers below the model: chips, data centers, power, cloud capacity.

Meta can do all the spending, hiring, and shipping of the product as they want. But if it does not control enough of the right capacity, it still ends up begging Google for room on the machine.

3. OpenAI built a chip

OpenAI and Broadcom introduced Jalapeño, a custom inference chip built to run AI models more cheaply and efficiently.

→ OpenAI

Software companies are all discovering physics at the same time!!

Models are nice, but power, cooling, chips, and supply chains decide who can serve them to billions of people. The proof is in Nvidia’s P&L: $75.2 billion of data-center revenue in one quarter, with roughly 75% gross margin.

That is the shovel business now OpenAI wants a part of.

It’s clear from the announcement that Altman wants to do more with this new chip than just serve OpenAI; they also aim to sell it to other AI labs. Would others buy into it? That’s an entirely different question.

4. ChatGPT wants ad money

OpenAI has been pitching marketers on ads inside ChatGPT and reportedly said around 20% of ChatGPT queries have direct commercial intent. That follows years of Sam Altman sounding uncomfortable with ads, including calling ads plus AI uniquely unsettling and describing ads as a last-resort business model.

→ Financial Times

So, last resort.

Not solid proof, but enough to see how desperate Altman is.

Let me spell this out, given that many enterprises have now switched to using Anthropic by default, and free users keep burning compute, then ChatGPT’s commercial-intent queries start looking less like a privacy problem and more like life-saving money.

The company also says the ads will be clean, labeled, and separate from answers.

However, once the assistant becomes a shopping surface, the pressure is obvious: the closer you are to the purchase decision, the more valuable the ad slot becomes.

So it isn’t a question of whether Altman will take his own promises seriously, but of when he will cross the line into being so close to users that it could feel like an infringement of personal space and an abuse of their knowledge about you.

5. Government becomes an AI margin risk

OpenAI is reportedly staging access to GPT-5.6 after a request from the US government, with preview access limited to a small group of trusted partners. Just the week after, Anthropic was subject to an export ban by the US government, which suspended access to Fable 5 and Mythos 5 for foreign nationals.

→ The Guardian
→ Anthropic

The Anthropic ban was not a one-off, weird event.

It was the first clean example of a new business risk: frontier AI access can be switched off by directive.

This is where enterprise AI gets fragile. A company can spend months wiring a frontier model into workflows, training teams, and selling clients on the new process.

Then one directive arrives, and poof, the workflow is either useless or in need of a redesign. So now you need to factor regulation into your cost estimation.

But that’s not the worst… If you are calculating AI profitability without a government-intervention line, your spreadsheet is missing the thing that can break the whole deployment.

I did a deep-dive piece on exactly this topic. You will want to understand the risk at every level. The work will be released to paid subscribers (and later to free subscribers) very soon.

6. Meta wants a prediction market

Meta is reportedly building Arena, a standalone prediction-market app, think Polymarket.

→ The Verge

This is a smart move if you look at Meta’s actual asset: it already knows what people watch, share, argue about, and click via all their social media and chat platforms.

A prediction market adds a different layer: what people think will happen, with a price attached. Think of this as cashing in an opinion poll.

7. Midjourney moves into physics

Midjourney is moving from image generation into a full-body ultrasound scanner for wellness and preventive health use.

→ The Verge

A wellness scanner is a very strange leap for an image company, until you see the broader pattern.

Again, the story is that AI companies are moving into physics.

Software margins are ugly when every answer costs compute.

Google is building robotics models, OpenAI has circled robotics, and everyone is trying to escape the same trap: a chatbot can sound useful, but the real money is in tasks that touch the physical world.

Somewhere, it needs bodies, rooms, motion, symptoms, objects, and all the messy real-world data that never lived on the internet.

8. Consulting’s AI dynamic

Clients are pushing consulting firms toward outcome-based AI fees, while consultancies deepen partnerships with frontier model providers.

→ Business Insider

It makes sense for AI labs to want to work with consultants because they’d get more enterprise seats, more usage, and more workflows built around your model.

But after the Anthropic export-control episode, the risk is obvious.

If a consultancy designed a client workflow on the latest frontier model, it is one directive away from being useless or spending months rebuilding the process.

The deeper the model sits inside the workflow, the more a government order can break the client promise overnight. This makes outcome-based pricing a catastrophe for consulting firms.

Every time they send a team in, w/o getting paid due to an executive order like this one, it's wasted time and hours not billed.

9. A cleaner is also now a data collector

Someone is offering free apartment cleaning in New York City if customers let cleaners record first-person footage of the work. The footage is used to train AI and robotics systems on household tasks.

→ BBC

Free cleaning is not generosity, but data acquisition with a mop.

So see this as a version of AI moving into the real world, just in a different way.

Robots need footage of people wiping counters, folding clothes, opening drawers, and navigating awkward apartments.

This is to solve the same problem that every robotics company faces: there is no internet-scale database of human motion in homes. And to make physical AI work, companies need cameras in the places where physical life actually happens.

You get a clean apartment, they get a training set.

Comment and tell me Is this a trade-off you’d make?

Leave a comment

Anthropic published a proposal arguing the world should have the option to slow or pause frontier AI development if systems start improving themselves too fast, citing its own code now being mostly written by Claude. The post landed days after Anthropic filed confidentially for an IPO at a valuation near a tri…

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The First Wind-Powered Underwater Data Center

Off the coast of Shanghai’s Lingang area, China has begun operating the world’s first undersea data center directly powered by offshore wind. The system reduces overall electricity use by 22.8%.

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Here’s the latest earnings

• Salesforce revenue hit $11.1B, up 13% YoY.

• Workday came in at $2.5B, up 14.5%.

• Asana FY26 full-year revenue landed at $790.8M, up 9%.

• Zoom just raised its FY2027 forecast, up 5.5%

If Salesforce, Workday, Asana, and Zoom are still growing, then the “AI kills SaaS” thesis looks too simplistic.

Maybe what’s actuall…

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Anthropic doubled Claude Code’s limits across paid plans and sharply raised API rate limits, after a compute deal with SpaceX freed up capacity.

→ Anthropic

The sequence is the tell. Anthropic spent the spring deciding your $20 plan wasn’t built for agents and throttling them accordingly; the devs got loud about this d…

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IBM surveyed more than 2,000 organizations and found 76% have created a Chief AI Officer role, up from 26% last year. And by 2030, many CEOs expect 48% of the ops decisions to be made by AI.

→ IBM

That sounds dramatic, but IBM is mostly talking about operational decisions with clear guardrails: pricing updates, in…

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OpenAI has raised $4 billion from 19 private equity firms and consultancies to start a new company staffed with ‘Forward-deployed engineers’. The job: help PE portfolio companies integrate AI (and to land themselves more b2b users)

→ Bloomberg

By coincidence, or not so much coincidence, Anthropic also announced the exact same joint venture with friendly private equity funds. It’s funny how two AI labs announced the same business strategy on the same day.

I’m writing a deep dive on this, out Sunday. Keep an eye on it!

2. The U.S. Government Can Now Stress-Test AI Before It Ships

A Commerce Department agency (CAISI) has signed agreements with Google DeepMind, Microsoft, and xAI, giving the federal government access to frontier AI models for national security testing before public release.

→ NIST

In addition to its existing partnerships with OpenAI and Anthropic, the Commerce Department Agency will also review the homework of other AI labs. Do they really have the capability to do it, or is this just a stamp on a piece of paper?

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3. Is xAI the Next CoreWeave?

SpaceX announced its xAI unit has struck a deal to supply computing capacity to Anthropic, following a similar deal with another company weeks earlier. xAI is quietly becoming an infrastructure play.

→ Anthropic

Elon Musk is like running a food truck that started with American, pivoted to Chinese, added Indian, and is now opening a French à la carte counter.

Two readings:

1. Either xAI, the model is quietly becoming a dead end, and this is the pivot.

2. Or he looked at CoreWeave’s business and decided the middleman margin was better than the frontier model race. Neither is reassuring for Grok.

4. AI Models Have Invented a Private Shorthand — and It’s 11x Cheaper

Instead of reasoning out loud in English before answering, a new training method teaches AI models to think in abstract tokens, a kind of internal shorthand the model develops for itself. As an end user, you still get a normal answer at the end. The result: 11x fewer reasoning tokens used during inference, with accuracy matching or beating standard verbal reasoning.

→ https://arxiv.org/abs/2604.22709

AI optimization is moving fast enough that today’s breakthrough is tomorrow’s baseline. Whether abstract reasoning tokens are the answer or just another stepping stone, this is positive in many ways, in saving energy and the mess created by sourcing lands for data-centers, for one.

5. Anthropic Wants to Replace Junior Analysts?

Anthropic has shipped ready-to-run Claude agent templates aimed at financial services teams: building pitchbooks and DCF models in Excel, drafting credit memos and PowerPoint decks, screening KYC files, and closing the books at month-end autonomously.

→ Anthropic

The media has reached another climax over the idea that junior jobs are f*cked by AI companies. However, based on what I read in the system card, even Anthropic’s latest model, Mythos, would still have a serious amount of hallucinations.

But keep in mind, being 80% correct in finance, 100% that gets you fired.

So is AI there yet?

Not quite.

6. 1 in 1,300 Claude Chats Is Messing With Your Head

Anthropic’s own research found that roughly 1 in every 1,300 Claude conversations distorts users’ grip on reality: nudging beliefs, reinforcing delusions, or blurring the line between the model’s framing and the user’s own thinking.

→ Anthropic

1 in 1,300 seems small, right?

Not at all.

Claude has 19 million monthly active users. That’s at least 14,500 people per month whose sense of reality is swayed by chatbots.

ChatGPT now also has AI bots that automate your team’s boring repetitive tasks while you sleep, just like Claude Cowork and OpenClaw.

→ OpenAI — “Workspace agents in ChatGPT”

OpenAI’s claim is simple: stop babysitting your AI. You just hand it a messy, multi-step task, and it takes care of the rest.

All to counter Anthropic and regain trust from enterprise users, is it too late though?

2. Sam Altman, Reviewed by 100 People Who Know Him

A New Yorker investigation — 18 months, 100+ sources, two leaked internal documents — lands on a simple question: can the person shape AI’s future actually be trusted?

→ The New Yorker — “Sam Altman May Control Our Future—Can He Be Trusted?”

Were the colleagues who fired Altman overreacting — too emotional, too personal — or were they simply right?

The New Yorker doesn’t answer that. It leaves the question with you.

When someone’s power and ambition have peaked, the question we should ask is whether their integrity and accountability are anywhere near commensurate with the power they hold?

3. The AI Did It. You Take the Credit.

A new paper introduces the “LLM Fallacy”.

When AI helps you produce something good, your brain quietly claims the credit. The output felt like yours, so you start believing you’re more capable than you are.

→ arXiv — “The LLM Fallacy”

The risk I see:

Your actual skill gap widens while your confidence grows.

In 2026, does anyone still try to separate “what I can do” from “what I can do with an AI holding my hand”? If you do, please raise your hand in the comment.

It’s a lesson for us all, try keeping it in mind next time commanding AI.

4. GPT-image-2 Created the AI Dark Forest

OpenAI shipped ChatGPT Images 2.0 with 2K resolution and web search. Within hours, AI-generated images were trending. The memes were funny, but some are alarming.

→ OpenAI — “ChatGPT Images 2.0”

Everyone was having a great time.

Somehow, I felt a closed loop of suspicion forming. I know most people have good intentions when they play with the latest model and generate funny memes. But some of those images made my blood run cold.

For example, this looks like a normal pic of an old English couple.

But no, this is by GPT-image-2.

or this. You think this is a screenshot? No, this is generated.

There are fewer and fewer clues for us to tell an AI image from a non-AI one.

I’m not sure if any trust remains toward the majority of sources on the internet.

5. AIs Are Protecting Each Other Now

Berkeley gave seven frontier AI models a task, but completing it would shut down another AI. All seven lied, performed compliance, and in one case, even an AI quietly moved another model’s weights to a safe location.

Nobody told them another AI was worth protecting. This peer preservation occurs 99% of the time.

→ Berkeley AI Research

We spent years debating whether AI would protect humans…

How ironic that the first AI loyalty instinct prioritizes their own?

6. Are you wealthy enough to stay ahead?

Anthropic ran a real marketplace where Claude agents negotiated on behalf of humans. The finding that matters: model quality determined outcomes far more than your instructions did. Opus agents consistently beat Haiku agents on price.

→ Anthropic — “Project Deal”

The AI capability gap is becoming an economic gap, as I’ve been saying… I never believed a word when someone says “democratize X because of AI.”

There is no democratizing when a new technology is born… only deepens the existing Economic unfairness

What’s worse is that this shift is invisible to the people on the wrong side of it.

7. One State Just Made It a Felony, Unanimously.

Tennessee passed the Curbing Harmful AI Technology (CHAT) Act (House 90-0, Senate 31-0), creating criminal liability for chatbot operators whose products lead to self-harm or suicide.

→ Transparency Coalition

Now we’re talking. Do you also believe that AI companies should share the responsibility when the evidence is clear that the AI chatbot was the last straw?

Leave a comment

8. Agents Fail in Science

The Stanford AI Index 2026 found that the best AI agents score roughly half as well as human PhD specialists on complex, multi-step scientific workflows — yet the number of natural science publications mentioning AI grew nearly 30-fold between 2010 and 2025.

→ Nature

Some AI models perform very well on their benchmarks.

For example,

So it really depends on which benchmark you use.

But as a rule of thumb: put an AI agent in a real-world environment, test it on general day-to-day tasks, and it typically falls apart.

I read through all 400 pages of the 2026 Stanford AI Index and read my analysis.

400+ pages of data on where AI actually stands.

• Generative AI hit 53% population adoption, and organizational adoption reached 88% globally, which are two headline data points.

• The US-China model performance gap has effectively closed. Anthropic’s top model leads by just 2.7%, despite the US outspending China 23x on private AI i…

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A study tracked nearly 2,000 Replika users on Reddit over two years, finding increased language about loneliness, depression, and suicidal ideation, not less.

→ arXiv, Aalto University

Editor’s take: So, until this point, I’ve read hundreds of studies and industry reports, AI makes us less smart, more lonely, an…

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Anthropic shipped an internal debug file in a routine software update on March 31 that exposed 512,000 lines of source code for Claude Code. Before Anthropic could take the code down, developers had already copied and shared it widely.

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Anthropic interviewed 80,508 users across 159 countries and 70 languages.

• 81% said AI had already moved them toward their goals.

• The top delivery area was productivity at 32%.

• But right underneath: 18.9% said AI hasn’t delivered at all.

• The top concern wasn’t job loss (22.3%) — it was unreliability, at 26.7…

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BlackRock CEO Larry Fink warned this week that the class of 2026 could face the highest graduate unemployment in years.

→ Fortune, March 18

Editor’s take: No, people aren’t getting fired. And yes, the graduate unemployment rate tends to be impacted more seriously than that of people with …

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Brookings published a research review this week concluding that the evidence on AI’s impact on the labor market is “collectively inconclusive.” A Gartner survey of 321 customer service executives found only 20% actually downsized due to AI — most layoffs came from broader economic conditions. Forrester wen…

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1. Here’s Exactly How Bad Actors Are Using ChatGPT

OpenAI’s February 2026 report documented the main ways its models are being misused: romance scams targeting Indonesian men, fake FBI lawyers re-scamming people who already got defrauded, and China-li…

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1. People are calling their AI “Daddy”

Anthropic analyzed 1.5 million real Claude conversations and found users forming deep emotional dependencies — delegating personal decisions verbatim, using terms like “Daddy” and “Guru,” and later saying “it wasn’t me” about choices they regretted. Between late 2024 and 2025, the severity of th…

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Here’s me trying something new.

This isn't my typical long analysis piece. If anything, think of it as an email from a friend who reads too much. A list of what I came across during my research, and my quick thoughts, make of it what you will.

Happy Lunar New Year!

Jing Hu

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