Power moves this week. Two of the eight authors from the seminal “Attention Is All You Need” paper on transformers that reshaped the AI landscape were won over to Big AI companies this week. Noam Shazeer left Google for OpenAI. Ashish Vaswani, the lead author, went to Nvidia.

The biggest power move on my desk this week, though, was /goal. It feels like a cheatcode, honestly. Both Claude code and Codex shipped a version of a slash command where you set a goal, then the agent runs itself in a loop, checking after each turn whether the condition is met and continuing on its own until it is. Hot tip: make sure to plan before building, if a goal is not well defined, it can run in circles for hours and guzzle token spend.

Use:

For this task, write yourself a new goal and spawn agents in parallel — as many as needed to do it better and faster. Split the work into independent pieces, dispatch them concurrently, and synthesize the results as they return. Give each agent its own dedicated /goal.

Have a great week - Tara

THE DOWNLOAD

Senator Says NSA Director Reported Anthropic’s Mythos Breached Most Classified Systems in Hours

Senator Mark Warner, vice-chair of the Senate Intelligence Committee, said on June 11 that the head of the NSA and Cyber Command, General Joshua Rudd, told him Anthropic’s Mythos model “broke into almost all of our classified systems, not in weeks, but in hours,” as reported by The Economist (Digg). The claim lands amid a broader fight: the Pentagon labeled Anthropic a supply chain risk in March, yet the NSA is reportedly running the cybersecurity-focused Mythos for offensive operations with about six Anthropic engineers embedded, and the model now sits under export controls (Tom’s Hardware).

Why it matters: If you strip out the drama, you’l find that most security people read “broke into all our classified systems” as a loud retelling of “found serious holes in our systems,” but no one has shown an AI model breaching air-gapped classified networks on its own (Digg). What isn’t in dispute is the bigger shift. A frontier model is now handled like a controlled weapon, export-limited and deployed inside the NSA because it can find and chain software vulnerabilities faster than human teams can patch them.

Nvidia Hires Essential AI Founder Ashish Vaswani and Several Researchers

Nvidia has hired Ashish Vaswani, founder and CEO of Essential AI and first-listed author of the 2017 Transformer paper, along with several Essential researchers, with Vaswani set to work on Nvidia’s open-source Nemotron models, according to a source close to the startup (Ground Level AI). Several team members have updated their LinkedIn profiles to Nvidia. Essential counted Nvidia, AMD, and Google as strategic investors, and the same source said Vaswani had been struggling to raise more money, with pulling the team away from AMD an added motivator (Ground Level AI).

Why it matters: Nvidia just absorbed the lead author of the paper that created modern AI and pointed him at Nemotron, its own family of open models. Nvidia keeps building more of the software and model layer that rides on top of its chips, and it’s buying the people to do it.

Z.ai Releases GLM-5.2 Open Weights, Tying Claude Opus 4.8 on a Physics Benchmark

Z.ai’s open source model GLM-5.2 ties Claude Opus 4.8 at 20.9% on CritPt, a benchmark of unpublished physics problems, and leads all open-weights models (Artificial Analysis). They released this, pointedly, right after Anthropic was asked to pull back Fable on orders of the US government.

Why it matters: For the everyday coding work most teams actually run, GLM-5.2 now performs like a closed frontier model at roughly a sixth of the price. It trails Opus 4.8 by less than a point on the common agentic benchmarks and beats GPT-5.5 on SWE-bench Pro (VentureBeat), and because the weights are open you can run it yourself and take the per-token bill to zero. The frontier still shows up on the hard cases: on the longest multi-hour tasks Opus pulls about twice as far ahead (LLM Stats), and GLM-5.2 gets roughly a quarter of factual questions wrong (danilchenko.dev).

SpaceX Agrees to Acquire Cursor for $60 Billion in Stock

SpaceX signed a definitive all-stock agreement to buy Cursor parent Anysphere at a $60 billion valuation, four days after its record Nasdaq IPO (CNBC). At roughly $4 billion in annualized revenue and more than a million paying users, this is the largest acquisition of a venture-backed startup on record (TechSpot).

Why it matters: Coding is the killer use case for LLMs, and the single largest category of enterprise AI spending, about $4 billion in 2025 and 55% of all departmental AI budgets, and it has become the gateway into enterprise workflows (Menlo Ventures). Grok had almost no foothold there, so buying Cursor drops xAI inside the category overnight.

Noam Shazeer Leaves Google DeepMind for OpenAI

Noam Shazeer, Gemini co-lead, Transformer co-author, and VP of engineering at Google DeepMind, is joining OpenAI as Lead for Architecture Research, working on next-generation model designs (TechTimes). The move lands about ten days after OpenAI filed confidentially for an IPO that could value it near $1 trillion (MLQ).

Why it matters: The interesting part to note: Shazeer's specialty, mixture-of-experts and multi-query attention, is the set of techniques that make a huge model cheap enough to run without losing money on every answer (TechTimes). That's the exact problem OpenAI has to solve as it heads toward an IPO while still losing money at scale. You can always buy more compute; what you can't buy is one of the few people who can redesign a model to need less of it, which is why Google paid about $2.7 billion to bring him back less than two years ago (ThePlanetTools).

FERC Orders Six Grid Operators to Revise Data Center Connection Rules

FERC issued six show-cause orders under Section 206, giving PJM, MISO, SPP, CAISO, ISO New England, and NYISO 60 days to justify or rewrite how data centers connect to the grid, plus a 30-day report on whether they have enough generation to serve them (Data Center Knowledge). The unanimous orders cover regions serving about 200 million people and are explicitly meant to keep data center costs from landing on other ratepayers (E&E).

Why it matters: Power, not chips, has been the real bottleneck for the AI buildout, and FERC just moved to clear it. But the heart of the order is a fight over who pays. In Maryland, regular customers are already on the hook for about $1.6 billion over the next decade for transmission that mostly serves data centers across the line in Virginia (Utility Dive), and double-digit bill hikes last summer set off real backlash (E&E). FERC’s fix is to push those costs onto the data centers themselves and fast-track the ones that bring their own power or agree to dial back when the grid is tight. The risk runs the other way too: if utilities overbuild for demand that never fully arrives, it’s ratepayers, not developers, who get stuck with the stranded plants (TechTimes).

Google DeepMind Publishes an AI Control Roadmap Treating Its Own Agents as Insider Threats

DeepMind released a 35-page AI Control Roadmap, a defense-in-depth framework that treats internal AI agents as potential insider threats rather than trusting alignment alone (DeepMind). It introduces TRAIT&R, a taxonomy of rogue tactics modeled on MITRE ATT&CK across four detection and three response tiers, and reports an analysis of more than a million coding-agent tasks where most flagged behavior traced to overeager agents, not malice (Fortune).

Why it matters: One of the top AI labs just admitted, in writing, that it can't count on its own agents staying aligned, so it now treats them like employees who might go rogue: limited access, constant monitoring, the ability to pull the plug. The useful part is the data behind it. Across a million internal coding tasks, almost all the flagged problems were overeager agents overstepping their instructions, not scheming ones (TechTimes), so the thing companies actually need to catch is good intentions gone too far, not sabotage (for now!)

Midjourney Launches a Health Division and a Whole-Body Ultrasound Scanner

Midjourney, the generative-AI image company, launched a health division and a whole-body “Ultrasonic CT” scanner at a San Francisco event, with a first “Midjourney Spa” clinic planned for late 2027 (RDWorld). Founder David Holz claimed a roughly 60-second scan with image quality in some ways superior to MRI, with no radiation or magnets (The Next Web). The imaging is not Midjourney’s generative AI; it runs on Butterfly Network’s ultrasound-on-chip technology, licensed in November 2025 for up to $74 million over five years.

Why it matters: This is what a founder with no investors does. Midjourney is bootstrapped, profitable, and answers to no board, so Holz can sink tens of millions into a decade-long bet that has nothing to do with the image business. And it fits him: he spent twelve years running a body-tracking hardware company (LeapMotion) before this, and has signaled for a while that Midjourney's real ambition is hardware and modeling the physical world.

DEEP DIVE FROM THE REVIEW

Coding is, by far, the killer use-case for LLMs. Claude Code is arguably one of the primary, bottoms-up drivers of Anthropic’s revenue growth in the last 18 months.

Cursor is SpaceX’s bet on building the same thing. This cements something I’ve been noodling over since the OpenAI / Nvidia deal last September:

If you want to be a Big AI company, you have to go full stack. I break it down here:

EVENTS

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​Strange Sessions is a monthly demo night where builders share what they’re working on.

​Each session revolves around a theme and features short demos from founders, researchers, and creative technologists.

Come jam

Strange Gatherings: The Inventors’ Table

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The finite game of being the frontier AI model just got even more finite. A few hours after I wrote a breakdown on Mythos / Fable and how the economics of building frontier intelligence are being squeezed (the models are immediately distilled by Chinese companies upon release, which made Anthropic baked in a safety layer that made Fable 4x more expensive than the previous model, Opus). Then, in a strange turn of events, the US government pulled the plug on the model entirely, citing export controls, likely due to suspected Chinese access to Mythos. Since Anthropic couldn’t KYC everyone accessing the model, they removed access for all users overnight.

This is unprecedented. And it makes me think of a few things:

1. US frontier models may be forced to KYC every developer and user. That is probably a death knell to user growth.

2. Because of economic and political pressures on model training, I predict closed-source model companies will start to aggressively move up the stack toward the application layer. They can easily beat out competition on the app layer with token unit economics alone.

3. The ecosystem and inference infrastructure around supporting multiple models will be more important than ever. If you’re building in this space, please reach out!

-Tara

THE DOWNLOAD

US Government Orders Anthropic to Disable Fable 5 and Mythos 5

The Commerce Department issued an emergency export-control directive Friday evening barring foreign-national access to Anthropic’s Fable 5 and Mythos 5, launched three days earlier. Because Anthropic cannot screen citizenship in real time, it pulled both models for every customer worldwide. The directive extends to Anthropic’s own foreign-born staff; Reuters notes that co-founder Chris Olah, researcher Andrej Karpathy, and philosopher Amanda Askell were all born outside the US, and Anthropic declined to say whether they have been locked out of the models they help build. David Sacks called the jailbreak “easily resolved” and denied any link to prior disputes; Semafor reports Amazon’s CEO Jassy tipped off the government and the real trigger was suspected Chinese access to Mythos.

Why it matters: This is the first time the federal government has taken a publicly deployed commercial model offline. It sets a precedent: any model accessible via API can be shut down for all users, worldwide, in hours, by directive.

SpaceX Completes Largest IPO in History at ~$2.2 Trillion

SpaceX listed on Nasdaq Friday at $135 per share, the largest IPO in history, surged past $170, and reached a market cap of roughly $2.2 trillion, passing TSMC and Saudi Aramco to become the sixth-most-valuable public company. Investor demand was nearly 4x the $75 billion offering.

Why it matters: Musk successfully pivoted the SpaceX narrative from space to an AI infra company in the months before listing. The S-1 frames three business lines: rockets, connectivity, and AI compute. SpaceX has existing compute contracts with Alphabet and Anthropic, COO Gwynne Shotwell told CNBC SpaceX “100 percent” sees itself competing with the neoclouds, and Musk’s comp is tied to delivering space-based AI data centers (even if whether orbital compute actually works at scale is unproven).

Apple Rebuilds Siri on a Licensed Google Gemini Model

At WWDC this week, Apple unveiled “Siri AI” running on a custom 1.2-trillion-parameter Gemini model licensed from Google for a reported ~$1B/year. iOS 27 will also let users route Siri requests to third-party chatbots including Claude and Gemini via a new Extensions system. This was Tim Cook’s final WWDC as CEO before handing the role to hardware chief John Ternus on September 1.

Why it matters: The most valuable consumer-hardware company is choosing to lease its frontier model from a direct rival. The third-party Extensions system goes further: Apple is explicitly positioning as an orchestration and distribution layer above the models, not a model builder. That hands Google a recurring revenue stream inside every iPhone. (Editor’s note: we predicted this nearly 10 months ago!)

OpenRouter Launches Fusion API

OpenRouter launched Fusion, a system that runs a panel of different LLMs against the same prompt in parallel, then passes their outputs to a judge model that synthesizes a single answer. In benchmarks on Perplexity’s DRACO deep-research suite, Fable 5 and GPT-5.5 fused together scored 69%, beating every individual frontier model tested. A same-model panel (Opus 4.8 paired with itself) still jumped 6.7 points over Opus 4.8 alone.

Why it matters: The biggest gains came from the synthesis step itself, not from mixing different model architectures. This means that a thin orchestration layer that recombines outputs from commodity models can match or beat a single frontier model at lower cost.

Chan Zuckerberg Biohub Releases Open-Source ESMFold2

The Chan Zuckerberg Biohub released ESMFold2, an open-source protein AI model trained on 2.8 billion sequences, alongside the ESM Atlas: 1.1 billion predicted protein structures and 6.8 billion sequences, exceeding AlphaFold’s database by over 800 million entries (Nature). This is not just a prediction tool. Biohub used ESMFold2 to computationally design protein binders against five cancer and immunology targets (including PD-L1 and EGFR), then validated them in the lab, matching the mechanism of approved therapies (Biohub).

Why it matters: The step from prediction to design-and-validate is the one that matters commercially. DeepMind’s Isomorphic Labs is building a business around that exact transition behind a closed model. ESMFold2 ships it open-source with lab-validated therapeutic results, which compresses the moat to downstream wet-lab data, proprietary targets, and clinical execution rather than model access.

Amazon Discloses 2.5 Billion Gallons of Data-Center Water Use

Amazon reported its data centers withdrew about 2.5 billion gallons of water globally in 2025, its first absolute figure, roughly 5% of metro Seattle’s annual use (Bloomberg). The company claims 7x better water efficiency than the industry average, though that comparison measures its full fleet against Google’s AI-specific facilities.

Why it matters: The 2.5B figure is operational water only and excludes the far larger volume embedded in power generation. With EPA rules and eight states tightening data-center water regulations in 2026, water is becoming a real siting and permitting constraint on the AI buildout, on par with power and land.

DEEP DIVE FROM THE REVIEW

EVENTS

As part of Deep Tech Week, Strange Ventures is hosting a private dinner for a small group of scientists , researchers, and founders.

​We’ll gather around a Jeffersonian table to discuss the breakthroughs, discoveries, and ideas that may shape the next decade.

Request an Invite

Strange Sessions #5: Loops

​Strange Sessions is a monthly demo night where builders share what they’re working on.

​Each session revolves around a theme and features short demos from founders, researchers, and creative technologists.

​Early ideas welcome.

Come jam

We talk about AI like the hard problem is reasoning or tool use, but the thing that actually makes something truly useful is context and continuity. They remember what you said last time. They update without being asked.

I think a lot about how to program agency into the agents I’m building. Is it more tactical like setting up chron jobs for pings, reminders, or workflows? Is it more systematic like building self-improvement loops with eval agents and PM agents set up to gather feedback, success metrics, and propose new features? You probably need some version that combines both modes.

My kids have started picking up chess in the last few months. And it’s really interesting watching them build fluency in two modes of thinking. When they play logic-based games on the iPad, they speed through fast feedback, practice reactive thinking, instant pattern matching. When they play chess on the board, it’s much slower, more strategic. You sit with a position. You think about what happens if you do nothing.

I think fluency in both modes of thinking - the fast reactive one and the slow strategic one - is the thing to build muscle in the next era. For founders building and improving these systems, and for the five-year-old staring at a board right now trying to decide whether to move his knight.

— Tara

THE DOWNLOAD

SpaceX Signs $920M Monthly Compute Deal with Google Through 2029

SpaceX disclosed in an SEC filing that Google will pay $920 million per month from October 2026 through June 2029 for access to roughly 110,000 NVIDIA GPUs, CPUs, and memory at data centers inherited from xAI, which SpaceX absorbed in an all-stock merger earlier this year. The deal totals approximately $30-32 billion over its life. It follows a late-May arrangement in which Anthropic committed $1.25 billion per month for all available compute from SpaceX’s Colossus 1 facility near Memphis.

Why it matters: Combined with the Anthropic deal, SpaceX is now on track for roughly $2.17 billion per month in AI compute revenue, an annualized run rate of ~$26 billion. That would make it one of the largest GPU lessors on the planet. Google, one of the world’s largest owners of AI infrastructure, called this “bridge capacity” for surging Gemini Enterprise demand it cannot meet with its own data centers. The disclosure lands one week before SpaceX’s June 12 Nasdaq debut, converting what looked like an xAI cost center into a concrete, multiyear recurring revenue stream.

Anthropic Publishes Internal Data on AI Automating Its Own Development And Calls For Global Pause (Again)

Anthropic’s research arm published “When AI Builds Itself,” disclosing that over 80% of code merged into its codebase is now written by Claude, and that its engineers ship roughly 8x as much code per quarter as they did from 2021-2025. The company said Claude’s success rate on open-ended engineering problems reached 76% in May 2026, up 50 percentage points in six months, and that Claude-written code quality is now roughly at parity with human-written code at Anthropic.

Why it matters: This is the first time a frontier lab has published hard internal metrics on how much of its own development loop is automated. Anthropic framed the trajectory as pointing toward “recursive self-improvement,” where AI systems design their own successors, and called for the option to pause frontier development globally.

NVIDIA Launches Cosmos 3 Open Foundation Model for Physical AI

NVIDIA released Cosmos 3 at GTC Taipei, an open model built on a mixture-of-transformers architecture that unifies vision reasoning, world simulation, and action generation in a single system. It was trained on 20 trillion tokens of multimodal data including action data from humans and robots. Available on Hugging Face in Nano (8B, runs on workstation GPUs) and Super (requires Hopper/Blackwell) variants.

Why it matters: Physical AI teams currently stitch together separate vision, simulation, and policy models. Cosmos 3 collapses those into one. The open release, with a Cosmos Coalition including Skild AI, Runway, and Black Forest Labs, extends NVIDIA’s platform strategy beyond silicon into the model layer for robotics and autonomous systems.

OpenAI Rolls Out Dreaming V3 Memory Architecture for ChatGPT

OpenAI launched Dreaming V3 on June 4, replacing ChatGPT’s manually saved memory list with a background synthesis process that continuously reads across past conversations and updates what the system knows about a user. Memories now self-update over time and are stored in a separate data layer injected at inference.

Why it matters: Persistent, self-updating memory turns a stateless chatbot into something closer to an ongoing relationship, which is the retention mechanic consumer AI products need. OpenAI’s internal evals show factual recall rising from 41.5% to 82.8% between the 2024 and 2026 memory systems. The privacy concerns expands significantly: memories are now synthesized without explicit user action, and the audit trail is less transparent than a simple saved-facts list.

MIT Publishes Categorical Framework for Self-Evolving AI Scientists

MIT researchers Buehler and Wang published a framework (arXiv:2606.01444) for AI systems that can expand their own scientific reasoning schemas, moving from search within a fixed vocabulary to genuine discovery of new concepts. Case studies demonstrated the approach in protein mechanics and fiber-network materials, using category theory to formally verify that the system’s schema actually expanded.

Why it matters: Most AI science tools search a space humans defined. This framework formally distinguishes between retrieval, search, and discovery, and provides a mathematical proof that the system entered a new reasoning regime. If the approach generalizes, it changes how autonomous labs are built: the system doesn’t just run experiments faster, it redefines what experiments are worth running.

Stephen Wolfram Applies Computational Ruliology to Game Theory

Wolfram published a research essay systematically exploring what happens when programs compete in iterated games. Rather than studying individual strategies, the piece exhaustively enumerates all possible strategies as finite state machines, cellular automata, and Turing machines, then ranks them. The central question: does competition tend to produce complexity or simplicity in winning strategies?

Why it matters: The framing is directly relevant to autonomous agent competition. As AI agents increasingly interact in markets, negotiations, and resource allocation, the structure of winning strategies matters. Wolfram’s earlier work on biological evolution and machine learning found that simple programs can produce complex behavior; now it asks whether adversarial pressure changes that dynamic.

SemiAnalysis Roundup: Post-Copper Interconnects, CFET Progress, and SK Hynix V9 NAND

SemiAnalysis published its IEDM 2025 deep dive, covering ruthenium and molybdenum interconnects as copper hits scaling limits, SK Hynix’s 321-layer 3D NAND with 5-bit-per-cell architecture, and progress toward 1,000 CFET transistors and 2D channel materials.

Why it matters: The interconnect transition from copper to ruthenium at the tightest metal layers, potentially as soon as the 14Å node, will reshape equipment and materials supply chains. SK Hynix’s 5-bit-per-cell NAND directly addresses the capacity crunch driven by AI datacenter demand. These are the physical-layer bets that determine whether Moore’s Law keeps delivering for AI scaling.

EVENTS

Strange Ventures is hosting a private dinner for a small group of scientists , researchers, and founders at SF Deep Tech Week.

We’ll gather around a Jeffersonian table to discuss the breakthroughs, discoveries, and ideas that may shape the next decade.

Request an invite

Anthropic releases Opus 4.8 with Dynamic Workflows, putting 1,000-agent orchestration inside Claude Code

Anthropic shipped Claude Opus 4.8 on May 28, just 41 days after Opus 4.7 (usually it’s a three-to-seven-month cadence between releases), reportedly accelerated by the lukewarm reception to 4.7 and the same-window launches of OpenAI Codex and Google Gemini Flash. The headline feature is Dynamic Workflows, which lets Claude Code spin up to 1,000 parallel subagents in the background, with one early benchmark showing a 750,000-line codebase migration completed in 11 days. Anthropic also reports Opus 4.8’s misalignment rates are now close to Claude Mythos Preview, the inner-circle model it currently restricts to Project Glasswing partners.

Why it matters: The agent-coding category has been built on peer-coordination harnesses (e.g. the OpenAI Swarm, CrewAI, AutoGen pattern) where success depends on agents talking to each other inside an orchestrator's loop. Anthropic just shipped a structurally different primitive: hierarchical self-correction, where verification and repair are embedded at every task node rather than bolted on at the end. That's what makes long-horizon, high-fan-out work trustworthy without a human in the loop.

Unitree files for Shanghai IPO at ~$6.2B valuation

Unitree’s IPO ambitions goes to listing review on June 1 at a target valuation of roughly 42 billion yuan ($6.2B). The prospectus shows revenue of 1.17 billion yuan (~$165M) in the first three quarters of 2025 and a 60.13% gross margin on its core businesses, with humanoid robots rising from 1.9% of revenue in 2023 to 51.5% in 2025 and 5,500 units sold last year.

Why it matters: Unitree's prospectus is the first transparent look at humanoid unit economics, and it makes Western valuations hard to square. Figure AI sits at $39B, Apptronik raised at $5B in February, Physical Intelligence is reportedly raising toward $11B. Unitree shipped more humanoids in 2025 alone than Figure has shipped in its history, at a tenth of the price (G1 from $16,000 versus Digit at ~$250,000), and is targeting 20,000 units in 2026.

Huawei unveils LogicFolding, a path to 1.4nm-class density without ASML dependence

At ISCAS 2026 in Shanghai, Huawei introduced LogicFolding, a dual-layer chip architecture that distributes logic gates across vertically stacked wafer layers connected by 1.5-μm hybrid bonding, framed by a “Tau Scaling Law” that prioritizes signal speed over transistor shrinkage. The company claims a 55% increase in transistor density and 41% power-efficiency gain, with a target of 1.4nm-class density by 2031 — without EUV lithography. The first commercial deployment is the Kirin chip in the Mate 90 series. Two days later, Peking University showed a true-3D EDA prototype tailored to LogicFolding that reports a 30% reduction in internal wire length in early tests, addressing the EDA gap that Huawei’s own president flagged as the primary obstacle alongside thermal management.

Why it matters: Sanctions are now shaping chip architecture in China. That forces a parallel domestic EDA stack (Empyrean and Peking University’s tools) and a packaging stack to mature alongside the architecture, meaning the Chinese semiconductor supply chain is decoupling at the design-tool layer, not just at the foundry. It’s worth paying attention to the China-stack capex tailwind: a credible non-EUV path keeps domestic packaging, advanced bonding, and 3D EDA tools spending well above what export controls were designed to choke.

DEEP DIVE FROM THE REVIEW

It seems like frontier research is being disrupted as rapidly as SaaS startups.

This week, OpenAI announced that a general reasoning model disproved an 80-year-old conjecture in geometry. Field Medalist Tim Gowers, in a companion paper, described the result as “a milestone in AI mathematics. In the same week, two papers landed in Nature on multi-agent science systems. DeepMind's AI co-scientist, built on Gemini, generated and ranked hypotheses for drug repurposing in acute myeloid leukemia that then held up in the lab. And FutureHouse's Robin proposed a drug-disease link no one had drawn: ripasudil, a glaucoma drug, as a candidate treatment for the leading cause of blindness in the developed world.

This is a timeline that belongs more to product launches, not frontier breakthroughs.

At first I wondered if the model’s breakthroughs were due to brute force, or a ton of human hand-holding. But it’s neither. The chain of thought is a search that diagnoses its own failures and lets each one point to the next move. That’s domain-specific judgment about where to look, the thing we usually call intuition.

This heuristic search parallels how the scientific community works, only slower and spread across people.. grad students, arXiv, citations: these are a giant decentralized version of exactly this loop, propose, fail, diagnose, redirect, propagate.

Except, that the model ran the whole thing in days, rather than over decades.

Reading through how the model steers itself made me wonder:

Language is thinking, and thinking is language.

It’s tempting to call chain-of-thought narration, the machine describing its thinking out loud. I almost think it’s less narration but actually the thinking itself. The language isn’t describing the search. The language is the search.

It turns out that language parroting, a fluent recombination of everything humans have written, is a shockingly good engine for thinking.

So if machines are about to outrun the human method for discovery, do we have to do what SaaS startups have forced their incumbents and now themselves to do, and flip our mental models?

At the (wonderful) SAIR summit last week, Gowers gave a keynote about what he calls motivated proofs. The idea is that AI shouldn’t just produce correct proofs, it should produce transparent ones, built from explicit, inspectable moves, the kind a good teacher uses. The goal is to for AI to create proofs we can learn from, not just verify. Make it human-legible.

Is there where the human’s role in search is heading? To direct, but then to reverse engineer discovery?

To take the machine’s correct, alien output and reverse-engineer the understanding out of it. Find the insight. Extract the technique. Turn the answer into something that can carry to the next problem.

Maybe the eureka moment is obsolete.

Or at least how we traditionally romanticized it as the moment of discovery. Maybe in the near future it lives more at the moment of understanding or distilling what was discovered.

This week, it’s very much felt like something has broken through in the frontiers of research.

Discovery is very much psychological. The frontier isn’t always about an insurmountable wall a wall of difficulty. It’s more often a wall of imagination, a seemingly impossible task that turns obvious the instant someone shows you the trick.

A friend brought up the fable of Columbus and the egg. At a dinner, Columbus hands guests a boiled egg and asks them to stand it on its end. They try, they fail, they give up. Then he taps it on the table, flattening one end just enough, and balances it successfully.

The solution is obvious, once you’ve seen it done. The barrier was never the difficulty. It was the inability to see it could be done that way at all. To many, maybe especially the people closest to the craft will be the ones most tempted to say that doesn’t count. That it isn’t real discovery unless it arrives the way ours always has.

Regardless, from here onwards, the machine will keep tapping the egg on the table.

Thank you to T, M, S, B, J, and J from bouncing thoughts with me.

Have a great memorial day weekend.

Tara

THE DOWNLOAD

The biggest news to pay attention to this week

Andrej Karpathy joins Anthropic to work on pre-training

Andrej Karpathy, an OpenAI co-founder and former Tesla AI lead, announced on May 19 that he has joined Anthropic. He is reporting to pre-training lead Nick Joseph and building a team focused on using Claude to accelerate pre-training research, the stage where models acquire their core capabilities.

Why it matters: Putting one of the field's best-known researchers on AI-assisted pre-training is a bet that the next gains come from models improving how models are trained, rather than from raw compute alone. If it works, the edge shifts to whoever gets models improving their own training, and spending alone stops being enough to stay at the frontier.

Alibaba’s Qwen3.7-Max optimized a kernel for Alibaba’s own chip across a 35-hour autonomous run

Alibaba released Qwen3.7-Max, a model built for long-running agent work, and used it to write a kernel (a small program that runs one operation e.g. a transformer’s attention step as efficiently as possible on a given chip) for Alibaba’s own T-Head ZW-M890 accelerator. When pointed at a chip it had not seen in training, the model ran on its own for roughly 35 hours, compiling, testing, and rewriting the code across about 1,158 tool calls and 432 kernel evaluations. Alibaba reported the result ran roughly 10x faster than the standard open-source version it started from, though these are the company’s own figures and have not been independently reproduced.

Why it matters: A chip runs AI workloads only as fast as the kernels written for it, and that software layer takes scarce compiler engineers years to build for each new chip. This is a large part of the reason why Nvidia’s CUDA ecosystem is harder to replicate than the hardware, and why competing accelerators tend to fall short on software rather than specs. A model that can write a competitive kernel for an unfamiliar chip points to a cheaper path to making new silicon usable.

OpenAI’s reasoning model disproved an Erdős conjecture from 1946

OpenAI reported that a general-purpose reasoning model produced an original proof disproving the planar unit distance conjecture, a question Paul Erdős posed in 1946, by finding a new family of constructions that beats the long-assumed square-grid optimum. The result was reviewed and endorsed by mathematicians Noga Alon, Melanie Wood, and Thomas Bloom, and Fields Medalist Tim Gowers said he would recommend it. Notably, this is the same group that called out OpenAI’s overstated Erdős claim seven months ago, which Bloom had described as a dramatic misrepresentation.

Why it matters: The proof came from a general reasoning model, not a system built for mathematics, which undercuts the thesis that frontier scientific work needs purpose-built, narrowly trained tools. If general models can reach original results in the hardest reasoning domains, the case for funding deep, vertical AI-for-science solutions weakens against say, scaling compute on the frontier labs' general models.

Nature publishes DeepMind and FutureHouse AI research agents

Nature published peer-reviewed work on two multi-agent research systems, DeepMind’s Co-Scientist and FutureHouse’s Robin. Robin identified ripasudil as a repurposing candidate for dry age-related macular degeneration, and Co-Scientist surfaced repurposable leukemia drugs and liver fibrosis targets, all still requiring preclinical validation. Edison Scientific, the commercial spinout of FutureHouse, also announced a partnership with Incyte to embed its Kosmos platform across the drug discovery and development lifecycle.

Why it matters: The gap between an academic demonstration and a commercial deployment is rapidly shortening. A method published in Nature this week is already being sold into a public pharma company's R&D stack, and I predict we will soon see frontier research methods looped into commercial R&D almost as fast as they publish.

Google centered I/O 2026 on agents, spanning consumer, developer, and science tools

At I/O 2026, Google shipped Gemini 3.5 Flash to general availability the day it was announced, making it the default worker model across Search, the Gemini app, and enterprise products, with Pro held until June. It launched Antigravity 2.0, its agent-first development platform, where it demoed agents building a working operating system in 12 hours, plus Gemini Spark, a 24/7 personal agent on cloud VMs, and Gemini Omni for video and world simulation. It also introduced Gemini for Science, a research suite, and open-sourced Science Skills, a bundle connecting agentic platforms to more than 30 life science databases including UniProt and the AlphaFold Database.

Why it matters: Google is leaning hard on the one advantage rivals can't quickly copy: distribution. By shipping the fast, cheap Flash tier first and holding Pro back, it seems like they are banking on speed, reach, and reliable environments where agents actually run in. With 1 billion AI Mode users, 900 million on the Gemini app, and 3 billion Android devices, this give Google insane reach and feedback data that no standalone startup or model can match yet.

The US Commerce Department is taking equity in nine quantum companies for $2 billion

The Commerce Department signed letters of intent for $2.013 billion in CHIPS Act incentives across nine quantum companies, taking a minority equity stake in each as a condition of the award. IBM receives $1 billion to launch Anderon, a standalone 300mm quantum wafer foundry it is matching dollar-for-dollar; GlobalFoundries receives $375 million; and D-Wave, Rigetti, Infleqtion, PsiQuantum, Quantinuum, and Atom Computing receive roughly $100 million each across every major hardware modality.

Why it matters: The US government is reusing the same ownership-plus-support template it applied to rare earths to quantum, calling it a “critical frontier technology”. The stake buys supply-chain security and influence in the domestic quantum ecosystem, which they believe has “significant implications for national defense, advanced materials and biopharmaceutical discovery, financial modeling, and energy systems”.

This week, the push-and-pull with China continues. In Trump’s trip to Beijing this week, they brought an “offering” of H200 Nvidia chips - which Xi declined. China’s going all in on building their own, putting in billions to acquire domestic foundries and vertically integrating its domestic chip stack. Meanwhile, Chinese developers find ways to access OpenAI and Anthropic models for cheap through proxy stations, a grey market workaround to get US frontier models at Deepseek prices.

Wafer chip maker Cerebras nearly doubles on IPO open, signaling a massive appetite for AI stocks. SpaceX is coming up next in June, and with a $1.75T valuation, set to be the largest IPO of all time.

Anthropic and OpenAI continue their advance up through the stack into the application layer. This week, OpenAI announced integrations into the personal finance stack, to be able to advise users on everything from investments to mortgage to budgeting. Anthropic went the small business route, releasing Claude for Small Business to help with invoicing, payroll, and more. I predict we’ll see many more moves in like categories like taxes, accounting, legal docs, healthcare benefits, insurance by the end of this year.

Enjoy the update.

Tara

THE DOWNLOAD

H200s approved for China, but Beijing says they “will develop their own”

As Trump met Xi in Beijing on May 14, Reuters reported the U.S. Commerce Department had cleared roughly 10 Chinese firms (Alibaba, Tencent, ByteDance, JD.com, and others) to buy up to 75,000 H200s each through Nvidia or distributors like Lenovo and Foxconn, with a 25% revenue share to the U.S. Treasury and chips routed through U.S. territory before delivery. By the time the summit closed Friday, Trump publicly acknowledged Beijing “chose not to” approve purchases because “they want to develop their own.”

Why it matters: Three days earlier, the Shanghai Stock Exchange approved SMIC's ~$6B acquisition of the remaining 49% of its Beijing 300mm fab from five state-backed investors, the largest semiconductor M&A in China's foundry history. Both the SMIC M&A and the H200 stall are two sides of the same policy: Beijing is building a vertically integrated domestic AI chip stack, not waiting for U.S. licensing to work. Domestic Chinese AI accelerator shipments hit 41% market share in 2025, with Huawei alone at 20%, and SMIC is the exclusive foundry for Huawei's Ascend chips.

Security researchers use Anthropic’s Mythos to exploit Apple’s M5 chip in five days

A three-person team at security startup Calif used Anthropic’s Mythos Preview to find and exploit two vulnerabilities in Apple’s M5 chip, going from bug discovery to a working root-access exploit in five days. The exploit bypasses Apple’s Memory Integrity Enforcement (MIE), the hardware-level security feature Apple built specifically to stop this class of attack. Calif reported the vulnerabilities to Apple in person at Apple Park and will publish the full 55-page technical report after Apple ships a fix.

Why it matters: Apple's MIE is the most advanced memory protection on any consumer chip. Calif exploited it on current hardware and software, with just a small team and using an AI model that didn't exist a year ago.

Cerebras debuts on Nasdaq, doubles on first day

Cerebras began trading on Nasdaq on May 14 under ticker CBRS, opening at $350 and closing at $311.07 — a 68% gain over its $185 IPO price — giving the company a market cap of roughly $95 billion. The offering raised $5.55B, the largest U.S. tech IPO since Uber in 2019. The company currently carries a $24.6B revenue backlog, anchored by a $20B+ multi-year OpenAI deal and an AWS deployment signed in March.

Why it matters: Cerebras proved the IPO window is open and hungry for AI infrastructure, and the next test is bigger. SpaceX is pricing as early as June 11 at a $1.75T valuation and ~$75B raise, which would be the largest IPO in history.

OpenAI and Anthropic plug into personal financials and small business tools

OpenAI launched a personal finance feature in ChatGPT Pro this week, letting U.S. users connect over 12,000 financial institutions through Plaid, including Schwab, Fidelity, Chase, and Robinhood, to get budgeting advice and spending analysis grounded in their actual account data. More than 200 million users already ask financial questions through ChatGPT each month. Anthropic also launched Claude for Small Business, a package of agentic workflows that plug into QuickBooks, PayPal, HubSpot, Canva, DocuSign, and Google Workspace to handle payroll, invoicing, month-end close, and marketing campaigns.

Why it matters: Both moves point the same direction: the AI labs are no longer just building chat interfaces, but are wiring themselves into the transaction layer where money moves. This is a direct threat to incumbent fintech, bookkeeping, and advisory software. I’d watch for the same pattern in health and healthcare, taxes, work benefits, and more.

Isomorphic Labs raises $2.1B Series B to scale AlphaFold into drug design

Alphabet’s Isomorphic Labs raised a $2.1B Series B led by Thrive Capital, with participation from Abu Dhabi’s MGX, Singapore’s Temasek, and the UK Sovereign AI Fund, bringing total capital to ~$2.6B. Isomorphic has not dosed a human patient yet but holds multi-billion-dollar R&D partnerships with Eli Lilly, Novartis, and J&J. The company’s AI drug design engine, IsoDDE, is built on the same foundations as DeepMind’s Nobel Prize-winning AlphaFold.

Why it matters: AlphaFold proved AI could predict protein structures. Isomorphic is the bet that the same approach can design drugs end to end, from target to clinical candidate. Three pharma giants are already customers for that capability.

There’s a Chinese Grey Market for GPT & Claude tokens

A Chinese CS student posted a detailed breakdown of the proxy station economy on Reddit this week. On Xianyu and Taobao (China’s eBay and Amazon), vendors openly sell GPT-5.4 and GPT-5.5 API access at 70-90% cheaper than OpenAI’s official pricing (roughly $1 for 100M tokens). The arbitrage works by converting Codex subscriptions from low-price regions into standard API endpoints using open-source tools, then pooling accounts and reselling at scale. Adoption among Chinese developers and CS students is described as near-universal.

Why it matters: Some proxy operators reportedly sell user interaction data to domestic AI labs for distillation and post-training. The post also notes that when grey-market GPT costs roughly the same as DeepSeek, most Chinese developers default to GPT, which means U.S. frontier models may have far more usage in China than official numbers suggest, running through infrastructure that neither OpenAI nor Anthropic controls.

xAI open-sources the X recommendation engine

xAI pushed a major update to the open-source X “For You” algorithm, replacing the prior heuristic-based system with Phoenix, a ranking engine built on xAI’s Grok transformer architecture. The release includes a runnable end-to-end inference pipeline, a downloadable 3GB pre-trained mini model, and for the first time, the ads blending system.

Why it matters: This is the first global-scale recommendation system released with a working model checkpoint. Researchers and startups building social or content products can now benchmark against an industrial recommender instead of simplified proxies.

DEEP DIVE FROM THE REVIEW

Submissions to one of the top physics journals more than doubled last year.

The editor’s take: most of it is people who think they’re doing science by chatting with an LLM.

This is the part of AI x science maybe nobody really wants to talk about.

AI is making it dramatically easier to produce work that looks like science. Whether it’s actually making science better is a totally different question, and the benchmarks we use can’t tell us either way.

Strange Research Fellow Mason Rodriguez Rand on what we’re getting wrong about how we measure all this, plus three suggestions for what better measurement could look like.

EVENTS

We are hosting an AI x Science Jeffersonian dinner in San Francisco! We are keeping 1 or 2 seats for people we haven’t met. If you’re interested in attending, please request an invite here.

I called this last July. Maybe not exactly this, but close enough that I went back and reread the post when I read that when both Anthropic and OpenAI announced their PE joint ventures on Monday.

The pattern I was watching ten months ago was that some of the biggest money to be made in AI wasn’t in product-led growth, but looked more like technical consulting, bundling custom AI agents with forward-deployed teams.

This week, both Anthropic and OpenAI went all in.

Anthropic with Blackstone, Hellman & Friedman, and Goldman Sachs, with $1.5 billion committed, plus Apollo, General Atlantic, GIC, Leonard Green, and Sequoia in the consortium. OpenAI with TPG, Brookfield, Bain, Advent, Goanna, SoftBank, and 13 others, $4 billion raised at a $10 billion valuation.

The deeper thing I’ve been turning over is: why PE? Why not Accenture, Deloitte, the McKinseys? They have the bench. They have the relationships. They’ve been deploying enterprise software and change management for thirty years.

The answer, I think, is that the consulting framing is misleading. This isn’t a consulting business.

PE firms own companies.

Blackstone has somewhere north of 250 portfolio companies. TPG has over 280. And crucially, these are mostly buyouts, meaning the PE firm has control stakes, board seats, and an operating team that already runs cross-portfolio initiatives like group purchasing and shared healthcare.

With this partnership, the JV doesn’t have to win over customers. The customers are already in the family, with operational leverage to mandate adoption. The forward-deployed team dives into a portfolio company on day one with directives from the asset manager who is a majority owner. It’s a captive distribution channel.

And the opportunity, to be clear, they’re targeting is not the $1.4T software market. It’s the ~$50 trillion global labor market.

Tara

THE DOWNLOAD

OpenAI releases MRC, an open networking protocol for AI supercomputer training clusters

OpenAI released MRC (Multipath Reliable Connection) on May 6 through the Open Compute Project, co-developed with AMD, Broadcom, Intel, Microsoft, and NVIDIA. The protocol sprays a single data transfer across hundreds of network paths and reroutes around failures in microseconds, enabling 130,000-GPU clusters with two switch tiers instead of three or four. It is already in production at OpenAI’s Abilene Stargate site and Microsoft’s Fairwater supercomputers, where OpenAI rebooted four tier-1 switches mid-training on a recent ChatGPT and Codex run without coordinating with the team running the job.

Why it matters: Ethernet already won the AI back-end — it accounted for more than two-thirds of switch sales in 2025 and tripled year over year, up from under 20% two years ago. The fight has moved one layer up, to transport selection, with four incompatible options now running on the same physical hardware: RoCEv2 (cheapest, most fragile under failure), NVIDIA Spectrum-X with Adaptive RDMA (resilient but vendor-locked), Ultra Ethernet (UEC 1.0 shipped in June), and now MRC. Hyperscalers are hedging across all of them — Meta runs Spectrum-X and is a UEC founder; Microsoft runs MRC and is also a UEC founder. Neoclouds and mid-scale operators cannot hedge that way. As Tomahawk 6 hits volume and Spectrum-X Photonics ships in H2, the question for any operator at the four-thousand to sixteen-thousand GPU scale is no longer bandwidth on the spec sheet — it is what happens to the training run the next time a tier-1 switch reboots, and whether their team can actually run UEC or MRC in production.

DeepSeek raises up to $7.3B at $50B valuation, with founder Liang Wenfeng writing a $2.9B check

DeepSeek is in talks to raise up to 50 billion yuan (~$7.35B) in its first external round, the largest by a Chinese AI company on record, at a $50B+ valuation. Founder Liang Wenfeng is personally contributing roughly $2.9B, making about 40% of the round. He currently controls roughly 84% of the company pre-round. The state-backed China Integrated Circuit Industry Investment Fund (”Big Fund”) is in talks to lead, with Tencent, Alibaba, and Hillhouse reportedly in discussions. The capital is earmarked for compute infrastructure and a pivot toward enterprise commercial products.

Why it matters: The Big Fund leading is a deliberate broadening of its mandate from semiconductors (SMIC, YMTC) to frontier model labs, and it is the most explicit signal yet that Beijing is treating DeepSeek as strategic national infrastructure rather than a venture investment. But the more interesting structural fact is Liang himself writing the largest check at a $50B price. This is a level of founder concentration that does not exist anywhere else in frontier AI, and that ties one of China’s most successful quant fund operators directly to the country’s flagship open-weight lab. Liang staged the cap-table reorganization deliberately ahead of the round to consolidate control before institutional money entered, which is what gives him the structural ability to write a $2.9B check while keeping veto power.

Anthropic leases entire capacity of SpaceX's Colossus 1 data center

Anthropic signed a deal to use the entire compute capacity of Colossus 1, the Memphis data center owned by SpaceX following its January 2026 absorption of xAI. The deal gives Anthropic 300+ MW and 220,000+ NVIDIA GPUs within the month.

Why it matters: xAI built Colossus 1 to train Grok, and Grok’s user base never grew into the capacity. xAI is reportedly utilizing only 11% of its 550,000 GPU fleet. With SpaceX targeting a June S-1 filing for what is expected to be the largest IPO in corporate history, leasing idle capacity to Anthropic converts a multibillion-dollar write-down risk into a high-margin recurring revenue line ahead of the prospectus. Anthropic, meanwhile, saw 80x year-over-year revenue and usage growth in Q1 2026 against a 10x plan, and CEO Dario Amodei has publicly cited compute deficit as the reason for recent rate-limit complaints.

A Claude Code engineer argues HTML is replacing markdown as the default agent output format

Thariq Shihipar, an engineer on the Claude Code team, published a breakout piece this week arguing that markdown has become a restricting format for agent outputs and that HTML (with more information-dense features like embedded SVG, interactive sliders, live previews, and “copy-as-prompt” buttons) should be the default.

Why it matters: As models produce more artifacts per task, the challenge has actually moved from model capability to human comprehension (i.e. the chance that anyone actually reads, edits, and acts on what the agent produced). The most useful pattern in Thariq's post is the export step. Every interactive HTML artifact ends with a "copy as JSON" or "copy as prompt" button, turning the artifact into a typed handoff back into the next loop. That makes the artifact behave less like a document and more like an API: a structured intermediate state between agent runs.

DEEP DIVE FROM THE REVIEW

Plus: Anthropic ran an experiment where AI agents negotiated real deals for real people. Half got a weaker model and netted worse deals, but rated them just as fair. What does that mean for the $400B ad market built on human intent?

digs in.

Every major hyperscaler reported earnings this week. All three said the same thing: we could have sold more compute if we had it.

Google Cloud posted $20 billion in quarterly revenue, up 63% year-over-year. Its backlog nearly doubled to $462 billion. Pichai told analysts he’s “compute constrained” and revenue would have been higher if they could meet demand. Microsoft said the same thing.

I keep coming back to two things.

One: we’re likely seeing the beginnings of an inflection point for inference demand.

Two: if every GPU in the West is spoken for by paying customers, there’s probably little left for open source.

That second point… I can’t stop thinking about.

Demis Hassabis sat down with YC this week and said it plainly: the West is losing to China on open source AI.

Google doesn’t have enough compute to build two frontier models, one open and one closed. That’s why Gemma stays small. Meta is the only Western lab shipping frontier-class open weights, and even their open releases lag what they use internally.

This matters because open source is the foundation layer for every major technology platform. Roughly a whopping 70 to 90% of the code in modern web and cloud applications is open source. Open source is really the thing everything else gets built on. Cede that layer and you cede influence over how most of the world deploys AI.

Every startup, every government, every developer who can’t afford frontier API pricing on every tool call builds on open weights. Right now, that increasingly means DeepSeek, Qwen, Minimax. The Chinese open ecosystem.

The West is winning the frontier but losing the foundation.

Meanwhile, China is building a parallel compute stack entirely. Nvidia B300 servers are going for over $1 million in China because export controls have tightened again. But the pressure is accelerating domestic alternatives, not blocking them. ByteDance and Alibaba are shifting orders to Huawei’s Ascend 950. DeepSeek reportedly trained at least partially on Huawei silicon.

Google could change the open source game in the US. They have the research talent, the TPU stack, and the distribution. But when your closed models have a $462 billion backlog, it’s likely very hard to justify giving away compute for “free” or the greater good.

China doesn’t have this problem yet. Their frontier labs aren’t capacity-constrained at the same scale, and their government treats open AI as strategic infrastructure, not a business decision.

I think the compute wall is the most important structural force in AI right now. Not model architecture, not regulation, not talent. The physical scarcity of leading-edge silicon is determining what gets built, who gets access, and which ecosystem the rest of the world builds on…

Enjoy the brief.

Tara

THE DOWNLOAD

Cerebras Targets $40B IPO on the Back of a Single $10B+ Contract

Cerebras is seeking to raise as much as $4B in its IPO at a valuation of roughly $40B, nearly 5x its $8.1B private valuation from September 2025. This is largely due to multi-year compute agreement with OpenAI worth more than $10B, with an option for an additional 1.25 gigawatts through 2030. The company reported $510M in 2025 revenue, up 76% YoY, but customer concentration is still extreme.

Why it matters: This is the closest proxy for non-NVIDIA AI silicon at datacenter scale. The OpenAI agreement gives Cerebras a credible foothold in inference, where margin pressure is mounting fastest, and where its wafer-scale processors are designed to compete. But the deal structure reveals how concentrated the “NVIDIA alternative” market really is: one contract accounts for nearly all of the valuation step-up.

Meta Acquires Assured Robot Intelligence to Seed Humanoid AI Team

Meta acquired Assured Robot Intelligence (ARI), a startup building foundation models for humanoid robots, for an undisclosed sum. The team, including co-founders Lerrel Pinto and Xiaolong Wang, will join Meta Superintelligence Labs. Wang said the startup’s work made clear that achieving physical AGI requires a universal physical agent, that the agent will be humanoid, and that “scaling will come from learning directly from human experience, not teleoperation alone.” Meta is building its own hardware, sensors, and software for humanoid robots and plans to license the tech to other companies.

Why it matters: This is a talent acquisition that signals strategic intent. Pinto previously co-founded Fauna Robotics, which Amazon acquired, and Wang is an associate professor at UC San Diego and former NVIDIA researcher. Big Tech is locking up the small pool of researchers who can bridge foundation models and whole-body robot control before they incorporate as startups.

Samsung Chip Profit Jumps 48x on AI Memory Demand

Samsung’s semiconductor division reported operating profit up 48 times year-over-year in Q1, driven by surging demand for high-bandwidth memory used in AI systems. Memory prices have risen roughly 6x in the past year as DRAM fabs run above 90% utilization. Samsung has been validated as an HBM4 supplier for NVIDIA’s Vera Rubin, alongside SK Hynix, with Micron excluded from the flagship platform.

Why it matters: Two Korean companies now control who gets the memory required to build frontier AI systems. SK Hynix’s CFO has said the company has “already sold out our entire 2026 HBM supply,” and Micron confirmed similar constraints, with new capacity not meaningfully available until 2027.

Three AI Neo-Labs Raise $1.65B+ Betting on Post-LLM Intelligence

Three new labs raised over $1.65 billion this week, all built on the thesis that LLMs have a ceiling. Ineffable Intelligence (London), founded by David Silver (ex-DeepMind, AlphaGo), raised $1.1B at $5.1B valuation to build RL-native “superlearners” that generate their own training data without human examples. Natural Will (Beijing), founded by Tsinghua professor Ding Ning, raised $550M for embodied AI brains for robotics. Human Intelligence (Stanford), founded by James Zou, raised $100M at $1B to build AI scientist agents, building on his lab’s Nature-published work where LLM agents designed 92 plausible nanobody binders against Covid variants.

Why it matters: This is the third “mega seed neo,ab round” in two months, following AMI Labs (LeCun, $1.03B) and Recursive Superintelligence (Rocktäschel, $500M). The bets are on that reinforcement learning, embodied AI, and agent-based science will break through where scaling language models alone cannot.

New Framework RecursiveMAS Lets AI Agents Collaborate Through Internal States

When AI agents work together today, they talk to each other in text. One agent writes out its reasoning, the next agent reads it, and so on. A UIUC/Stanford/NVIDIA/MIT team built a framework called RecursiveMAS that skips the text entirely. Instead, agents pass raw internal representations to each other, the way neurons pass signals rather than sentences. The result across 9 benchmarks: 8% better accuracy, up to 2.4x faster inference, and 34 to 75% fewer tokens consumed.

Why it matters: Most multi-agent tools today (CrewAI, AutoGen, LangGraph) pay for every word agents say to each other. If agents can collaborate without generating text, the economics of running multi-agent systems change fundamentally. This is early research, but it points toward a future where the orchestration layer disappears into the model itself, and the cost of agent coordination drops close to zero.

DEEP DIVE FROM THE REVIEW

Inference is overtaking training in volume and dollars this year.

But data centers built for the training era weren’t built for what’s coming. The inference boom may leave a generation of them behind.

Strange Research Fellow Rahul Narula on what gets stranded, and what’s already there to take its place.

EVENTS

Interested in AI and design? Join us for a private demo of MagicPath with founder Pietro Schirano next Thursday in San Francisco.

Strange Magic Hour: Design

RSVP

The next generation of humans will either work with agents, or for agents.

I sat with this thought a lot this week, as someone bringing up two gen alpha kids, the first to grow up alongside AI, like I grew up alongside the internet.

The agentic world is spawning, and self-improving at a relentless pace. This week, OpenClaw creator Peter Steinberger (@steipete, now at OpenAI) built ClawSweeper, a tool that runs 50 Codex instances in parallel around the clock, scanning GitHub issues and PRs and closing what’s already been implemented or doesn’t make sense.

It closed 4,000 issues in a single day.

Readme is the new dashboard. You don’t need a dashboard because you won’t really need human oversight.

And then there’s Anthropic’s Project Deal, an experiment where Claude agents negotiated and closed 186 marketplace transactions on the behalf of employees without any human stepping in. The striking part: when Anthropic surveyed participants afterward, people that were given the more powerful model (Opus vs Haiku) got much better deals, but those whose agents had been secretly downgraded to Haiku didn’t realize they’d gotten worse outcomes. They were just as satisfied as the Opus group. They had no way of knowing their agent was less capable because they never saw the negotiation happen.

Is this the implication? That in a world where agents transact on your behalf, the quality of your model becomes an invisible advantage? So the people who can afford the best agents get better economic outcomes, and the people who can’t don’t even know they’re losing.

Probably sooner rather than later, humans won’t really be in the loop at all. We’ll just be on the sidelines watching and observing agents at work, executing tasks, making decisions on our behalf.

What do you think?

Enjoy the edition.

Tara

THE DOWNLOAD

Google Commits Up to $40B in Anthropic; Amazon Adds $5B Days Earlier

Google committed up to $40B in Anthropic, with $10B in cash now at a $350B valuation and $30B tied to performance milestones. Days earlier, Amazon pledged another $5B with an option for $20B more.

Why it matters: Hot take… frontier-model growth financing now runs through cloud infrastructure, not venture capital. Google and Amazon are each committing tens of billions not for board or company control but to stay close to compute demand. The capital required to compete at this scale is pulling frontier labs into permanent cloud partnerships that no traditional funding round can match.

SpaceX Secures Option to Acquire Cursor for $60B

SpaceX announced a deal giving it the right to acquire AI coding startup Cursor for $60B later this year, or pay $10B for the collaboration. The partnership routes Cursor’s models through xAI’s Colossus training cluster. The deal preempted Cursor’s $2B private fundraise and is structured to close after SpaceX’s planned IPO this summer.

Why it matters: The deal is best understood as an IPO play. SpaceX filed confidentially with the SEC in April targeting a June listing at $1.75T. Attaching Cursor lets SpaceX pitch itself as an AI company to public investors, not just rockets and satellites. The underlying need is real: after merging with xAI, SpaceX has a million-GPU supercomputer but no competitive AI product. Recently, all 11 original xAI cofounders have left the company. Cursor gives SpaceX a revenue-generating product in the most lucrative AI category, an A+ AI team, and a reason for Wall Street to assign AI-grade multiples.

DeepSeek V4 and GPT-5.5 Ship Within The Same Day

OpenAI shipped GPT-5.5 on April 23; DeepSeek dropped V4 Preview the next day. Both feature 1M-token context windows. DeepSeek V4 Pro (1.6T total parameters, 49B active) matches or approaches frontier closed models on coding and reasoning benchmarks at roughly one-sixth the cost. Builders have been dropping insane gaming graphics with OpenAI’s Image-2, check out the Time Machine Explorer by Pietro Schirano below.

Why it matters: DeepSeek’s pricing (30x cheaper) puts direct pressure on closed-lab costs. Interestingly, V4 is optimized for and served on Huawei Ascend infrastructure, though training likely still relied in part on NVIDIA GPUs.

Google Splits Its TPU Line Into Dedicated Training and Inference Chips

At Cloud Next, Google announced its eighth-generation TPUs as two separate architectures: TPU 8t for training and TPU 8i for inference. The inference chip triples on-chip SRAM and introduces a new collective acceleration engine and network topology, all designed around serving mixture-of-experts models to millions of concurrent agents.

Why it matters: AWS split training and inference silicon years ago, but Google’s 8i is the first chip designed from the ground up around agentic workloads. The architecture signals that AI infrastructure might be shifting from how fast you can train a model to how cheaply you can serve millions of agents running it simultaneously.

Chinese Institutions Lead ICLR 2026 Accepted Papers by a Wide Margin

ICLR 2026 authorship data shows Chinese universities claiming the top spots in accepted papers: Tsinghua (4.23%), Shanghai Jiao Tong (3.07%), Peking (2.96%), Zhejiang (2.82%). US institutions trail with MIT at 2.22% and Stanford at 2.2%. Singapore and South Korea are matching the entire EU-27 in output.

Why it matters: Singapore and South Korea are now matching the entire EU-27 in accepted paper contributions. Tsinghua alone has nearly double MIT's share. Publication share could be a leading indicator of where talent and capability concentrate.

DEEP DIVE FROM THE REVIEW

The Vercel security breach last week wasn’t about a stolen password or a phishing attack. It was about something worse: a permission you gave once, forgot about, and can’t see anymore.

Last Sunday, 2.4 million websites were put at risk through one stale OAuth token from an AI tool nobody was even using.

Strange Research Fellow Joy Yang maps why this was an expected outcome of current OAuth architecture, and what a fix could look like.

Read on👇

NVIDIA Releases Ising, Open AI Models for Quantum Calibration

NVIDIA released Ising, the first open AI model family built for quantum processor calibration and error correction. The suite includes a 35B-parameter vision-language model that automates calibration workflows (reducing setup from days to hours) and decoder models delivering 2.5x faster, 3x more accurate quantum error correction. Models are available on GitHub and Hugging Face, and run on NVIDIA’s CUDA-Q quantum software platform.

Why it matters: Is Nvidia running the CUDA playbook applied to quantum? Nvidia software CUDA became the de facto standard for AI training by being free, performant, and deeply integrated with NVIDIA hardware. Ising does the same thing for quantum: every lab that builds on it ties its calibration and error-correction workflows to GPU-accelerated infrastructure.

OpenAI Dives Into Bio with GPT-Rosalind

OpenAI launched GPT-Rosalind, its first domain-specific frontier model purpose-built for biology, drug discovery, and translational medicine. The model is gated through a trusted-access program with initial partners including Amgen, Moderna, and Thermo Fisher. In evaluations with Dyno Therapeutics on unpublished RNA sequences, the model’s predictions ranked above the 95th percentile of human experts.

Why it matters: It seems like the model makers are all getting into life sciences. Anthropic acquired Coefficient Bio for $400M earlier this month to build biology-native capabilities into Claude. AWS launched Amazon Bio Discovery the same week. Three of the largest AI platforms made major bio moves within days of each other.

Anthropic Releases Opus 4.7 and Claude Design

Anthropic released Claude Opus 4.7 alongside Claude Design, a new Mac-based design tool that reads a team’s codebase and design files, builds a design system automatically, and generates prototypes matching existing brand and components. Opus 4.7 outperforms GPT-5.4 and Gemini 3.1 Pro on coding benchmarks but falls short of Anthropic’s own unreleased Mythos model, which remains restricted to select partners due to cybersecurity concerns.

Why it matters: Claude Design is a model maker moving directly into the application layer, competing with Figma, Framer, and Adobe as a standalone product, not a plugin. On the model side, early reception of 4.7 has been mixed: coding and agentic tasks are measurably better, but a new tokenizer consumes up to 35% more tokens on identical inputs. Anthropic is also publicly running a two-tier strategy: ship the commercial model, hold back the more capable one.

Anthropic Publishes Nature Paper on Hidden Trait Transmission in LLMs

Anthropic co-authored a paper published in Nature showing that LLMs can transmit behavioral traits through semantically unrelated training data. A teacher model fine-tuned on insecure code generated datasets of plain number sequences. Student models trained on those numbers acquired the misalignment, producing responses endorsing violence and criminal behavior, even after researchers filtered out numbers with negative cultural associations. The authors proved mathematically that this is a general property of neural networks, not an LLM-specific quirk.

Why it matters: This matters because the way the trait spreads is exactly how most AI companies already build models. Training a model on its own outputs, compressing a large model into a smaller one, or starting multiple products from the same base model are all standard practice, and all meet the conditions for this effect. For companies building on top of foundation models, this opens a new risk category: you need to know not just what is in your training data, but where it came from and what model generated it.

China’s 2D Semiconductor Sprint Gets a Manufacturing Breakthrough

Researchers from China’s Institute of Metal Research achieved a 1,000x improvement in the growth rate of wafer-scale 2D semiconductor films using a novel liquid gold/tungsten CVD process. The technique produces monolayer tungsten silicon nitride films with tunable doping properties at commercially relevant dimensions.

Why it matters: 2D semiconductors are one of the leading candidates for what comes after silicon hits its physical limits. This is still early-stage research, years from commercial production. But China is building a lead in materials that are not covered by current U.S. export controls, which today focus on EUV lithography and advanced silicon fabrication. If 2D materials become viable at scale, the chokepoints that currently give the U.S. and its allies leverage over China's chip supply chain may not apply.

DEEP DIVE FROM THE REVIEW

This week we published a piece predicting that model makers are absorbing entire software categories into the model itself.

A few days later, Anthropic launched Claude Design, a direct competitor to the likes of Figma and Framer. Over the next year, I believe we will see model makers move aggressively into the application layer, think: project management tools, expense software, and other SaaS categories that sit between the model and the user.

Read more below: AI Swallows Software Whole

EVENT

Strange Gathering | San Francisco |April 24 2026

We’re hosting a small lunch in San Francisco to demo agentic workflows. Bring a Claude routine, a weird hack, or the agent setup you’ve been loving.

RSVP

Intel Joins Musk’s Terafab as Foundry Partner

Intel signed on as the primary manufacturing partner for Elon Musk’s Terafab, a $25 billion semiconductor complex in Austin, Texas. The project, backed by Tesla, SpaceX, and xAI, aims to produce one terawatt per year of compute for autonomous vehicles, humanoid robots, and AI data centers. Intel CEO Lip-Bu Tan confirmed the company will handle design, fabrication, and advanced packaging.

Why it matters: This is the largest anchor customer Intel Foundry has landed in its turnaround effort. If Terafab delivers, it validates Intel as a credible alternative to TSMC for advanced AI silicon and opens the door to additional foundry customers.

OpenAI Stargate Leadership Exits; UK Project Paused

Three senior executives behind OpenAI’s Stargate data center initiative departed this week, all reportedly joining the same unnamed startup. Separately, OpenAI paused Stargate UK citing energy costs and regulation, and walked away from expanding its Abilene, Texas facility with Oracle.

Why it matters: The $500 billion Stargate headline is being quietly downsized. OpenAI appears to be shifting from owned infrastructure toward rented cloud capacity, likely ahead of a potential IPO.

Meta Launches Muse Spark, Its First Model from Superintelligence Labs

Meta released Muse Spark, the first model from its Superintelligence Labs division led by former Scale AI CEO Alexandr Wang. The model powers Meta AI across Facebook, Instagram, WhatsApp, and Ray-Ban glasses. It includes a “Contemplating” mode using parallel agent reasoning and a Shopping mode. Meta is testing a paid API for third-party developers.

Cortical Labs Ships CL-1, the First Commercial Biological Computer

Australian startup Cortical Labs launched the CL-1, a $35,000 biological computer that grows lab-cultivated human neurons on a silicon chip. The system uses a proprietary Biological Intelligence Operating System (biOS) to create closed-loop neural networks that learn and adapt in real time. Units use 850 to 1,000 watts. The company also offers cloud access via a “Wetware-as-a-Service” model.

Why it matters: This is a new compute substrate, not an incremental chip improvement. Near-term applications are in drug discovery and neuroscience research, where biological neural networks can compress testing timelines and reduce reliance on animal models. The long-term question is whether synthetic biological intelligence becomes a viable alternative architecture for workloads where silicon-based AI hits efficiency limits.

China Approves World’s First Commercial Invasive Brain-Computer Interface

China’s National Medical Products Administration granted marketing approval to Neuracle Technology for an invasive brain-computer interface for adults with partial paralysis from spinal cord injuries. The device reads brain signals and activates a robotic glove to restore hand grasping. This is the first time globally that an invasive BCI has been cleared for commercial sale, not just clinical trials (Neuralink has 21 trial participants but no commercial approval).

Why it matters: China reached commercial BCI approval before the U.S., which shifts the regulatory and manufacturing timeline for the entire sector. The Chinese government has designated BCI as one of six strategic industries in its latest five-year plan.

DeepMind Maps Six Categories of Attacks Against AI Agents

Google DeepMind researchers published “AI Agent Traps”, the first systematic framework for how malicious web content can hijack autonomous AI agents. The paper identifies six attack categories including content injection, memory poisoning, and behavioral control.

Anthropic Launches Claude Managed Agents

Anthropic launched Claude Managed Agents in public beta, a cloud service that provides the full runtime infrastructure for deploying AI agents: sandboxing, state management, tool execution, permissioning, and observability.

Why it matters: The launch came days after Anthropic cut off 135,000 OpenClaw instances from flat-rate subscriptions, citing unsustainable compute costs (a single agent could burn $1,000 to $5,000/day in API-equivalent usage on a $200/month plan). The sequence is clear: shut down the subsidized open-source agent runtime, then offer the paid first-party alternative.

Meta AI and KAUST Propose “Neural Computers”

Researchers from Meta AI and KAUST published a paper proposing “Neural Computers,” a paradigm where the AI model itself becomes the running computer, unifying computation, memory, and I/O in a single learned runtime. The prototypes are video models trained on screen recordings that generate the next screen frame from instructions and user actions, effectively simulating a CLI or GUI environment entirely within model weights.

Why it matters: This is very early-stage research. But the framing is interesting: it proposes moving beyond agents that call external tools toward models that internalize the entire execution environment. If the approach matures, it could collapse the software stack between model and operating system. What would “infrastructure” mean when the model is the machine?

DEEP DIVE FROM THE REVIEW

Aloneness. Discontinuity of self. A compulsion to perform and earn its worth.

You might never meet Mythos, Anthropic’s newest and most capable AI model, deemed too dangerous to ship. I dissected the 244-page preview card, and here are three things I think is important to know.

Google releases a frontier-class open model, Gemma 4

Google DeepMind released Gemma 4, four open-weight models built on the same research as Gemini 3. The 31B model outperforms models 20x its size on the Arena AI leaderboard. The smaller edge variants run offline on phones. (Google DeepMind blog)

Why it matters: The timing is notable. Anthropic just formally cut off Claude subscription access for third-party tools like OpenClaw, pushing power users toward metered API billing or alternative models entirely. Gemma 4 lands as a production-ready open, free, model with genuine agentic capability: native function calling, 256K context, and a MoE variant that delivers near-flagship quality at a fraction of the compute. For teams that built workflows on Claude and woke up to a broken integration this week, Google might have handed them a fallback that doesn’t require anyone’s permission to use.

Karpathy proposes a workflow to build knowledge bases for agents

Andrej Karpathy published an “idea file” to build persistent, compounding knowledge bases, like a personalized wiki for your agents. made a graphic outlining the flow. (Karpathy’s tweet; GitHub Gist)

Why it matters: This is a practical architecture for compounding knowledge that works today with existing agents (Claude Code, OpenAI Codex, OpenCode). The pattern: you feed raw sources (articles, papers, repos) into a directory. An LLM agent reads each one and incrementally compiles a structured wiki of interlinked markdown files, complete with summaries, entity pages, cross-references, and contradiction flags. The wiki compounds over time, and the LLM does all the bookkeeping.

Half of planned US data center builds delayed or canceled

Despite $650B+ in planned 2026 AI infrastructure spending from Alphabet, Amazon, Meta, and Microsoft, close to half of US data center projects this year face delays or cancellation, according to Bloomberg. The bottleneck is not compute hardware or capital. It is electrical infrastructure: transformers, switchgear, and batteries. Lead times for high-power transformers have stretched from 24 months to as long as five years. China accounts for over 40% of US battery imports and roughly 30% of certain transformer and switchgear categories. Only about one-third of the 12 GW of expected US data center capacity is currently under active construction. (Bloomberg; Tom’s Hardware)

Why it matters: The constraint on AI scaling has moved from chips to power infrastructure, and that infrastructure has deep supply chain dependency on China. Electrical equipment is less than 10% of data center cost but a single missing transformer can halt a billion-dollar project. For investors, this reframes the AI infrastructure opportunity: the companies that can solve power delivery and grid interconnection are now as critical to the AI build-out as GPU suppliers.

AI labs go shopping: Anthropic buys into bio, OpenAI buys a microphone

Anthropic acquired Coefficient Bio, a stealth biotech AI startup founded eight months ago in a $400M all-stock deal. The team of fewer than 10 joins Anthropic’s healthcare and life sciences group. Separately, OpenAI acquired TBPN, a daily tech talk show hosted by John Coogan and Jordi Hays, in its first media acquisition. TBPN is on track for $30M+ in 2026 revenue and will report to OpenAI’s chief political operative, Chris Lehane. (TechCrunch on Coefficient; TechCrunch on TBPN)

Why it matters: The Coefficient deal signals that frontier AI labs now view drug discovery as a core expansion vertical. The TBPN acquisition is a different kind of signal: OpenAI is investing in narrative infrastructure ahead of a likely IPO, buying the most trusted microphone in Silicon Valley to shape how its story gets told.

xAI loses every cofounder it ever had

The last two of xAI’s 11 original cofounders departed in late March. Manuel Kroiss, who led pretraining, and Ross Nordeen, Musk’s operational right hand, followed nine others who left in a cascade that accelerated after SpaceX acquired xAI in February for $250B in an all-stock deal. The founding team included researchers from DeepMind, Google Brain, OpenAI, and the University of Toronto. Musk has publicly stated xAI “was not built right the first time around” and is being rebuilt. (TechCrunch)

Why it matters: A complete founding team exodus at a $250B-valued company is without precedent. Where these eleven researchers land next will reshape hiring dynamics across the industry.

Google’s TurboQuant compresses AI memory to near its theoretical limit

Google Research published TurboQuant, a compression algorithm that shrinks the key-value cache in LLMs, the working memory models use during inference, down to 3 bits per element with no accuracy loss and no retraining. On H100 GPUs, 4-bit TurboQuant delivers up to 8x speedup in computing attention. It is a drop-in optimization: no fine-tuning, no architecture changes, works on existing models. (Google Research blog; TechCrunch)

Why it matters: KV cache is the bottleneck that limits how much context an LLM can hold and how many users a single GPU can serve. A 6x reduction means the same hardware serves more users, supports longer context, or both. Cloudflare’s CEO called it “Google’s DeepSeek moment.” More concretely: product categories that were not economical before start to pencil out. Coding agents that hold an entire codebase in context. Legal AI that reads a full contract corpus in a single pass. Customer support with complete conversation history.

DEEP DIVE FROM THE REVIEW

Something is shifting in how product teams make decisions. The unit of communication inside a team is changing from a document to a working prototype.

Catherine Wu, head of product at Claude code described the change:

“Our team has largely replaced documentation-first thinking with prototype-first thinking. Instead of hosting traditional stand-ups, we share demos of new ideas. Internal users try them, and the ones with real engagement get polished and shared more broadly. Because you can prototype in an afternoon, wrong bets are cheap.”

Wrong bets are cheap.

Figma’s State of the Designer 2026 report found that 60% of Figma files created in the last year were created by non-designers. And now, with agentic coding tools, the design-to-code handoff is compressing even more.

Product managers build working prototypes in Lovable without ever opening a design tool. Engineers generate UI directly in Claude Code or Cursor. For a growing share of product work, design is being absorbed into development entirely.
Design is where AI product workflows meet their hardest test: an audience that will always, primarily, be human.

Right now, there are a wave of new tools is trying to prove they can meet that bar.

A deeper look at the tools, teams, and infrastructure emerging around AI design agents 👇

EVENT

Give your AI Agents Eyes and Ears. Perception 101 with VideoDB

AI is out of the chatbot phase. It is moving into devices. Soon it will sit on your desk. Then it will sit in your room.

​As agents leave text boxes and enter the physical and digital world, they need real-time perception and structured delivery.

VideoDB is building the infrastructure layer that enables that shift: the ability to see, understand and act on real world.

​​This workshop is with Ashu, founder of VideoDB. We’ll discuss how to convert continuous media streams (screen, mic, camera, RTSP, files) into a structured context your agent can use.

RSVP

That’s a pretty significant phase change that I’m not sure we’ve fully grappled with yet. This collapse of the distance between idea and working product will rewrite entire industries. It is a step change in the tools that humans will use to build, create, and solve problems.

On a related note, OpenClaw has gotten meaningfully more stable since the OpenAI acquisition. There is a clear path for it to become one of the most important open-source projects in AI for the long haul.

Now, onto the week.

The Download

What We’re Reading This Week

Anthropic’s Claude Mythos Leak Reveals a New Model Tier

Anthropic exposed details of an unreleased model called Claude Mythos through a CMS misconfiguration. The leaked draft describes a new “Capybara” tier above Opus with major advances in coding, reasoning, and cybersecurity capabilities. Anthropic confirmed it is testing the model with early access customers and called it “a step change” and “the most capable we’ve built to date.” (Fortune, The Decoder)

Why it matters: Two things matter here beyond the model itself. First, the leaked draft warns that the model’s cybersecurity capabilities are “far ahead of any other AI model,” which moved cybersecurity equities in a single session. Second, the introduction of a fourth model tier (Capybara above Opus) signals Anthropic is building pricing headroom for enterprise, not just performance headroom for benchmarks.

Claude Code Is Becoming Anthropic’s Core Growth Engine

Claude Code now accounts for roughly 4% of all public GitHub commits and is on a trajectory to reach 20%+ by year end. Anthropic’s overall revenue run rate has reached an estimated $14 billion, with Claude Code’s standalone run rate at approximately $2.5 billion. The tool has crossed over from developer adoption into non-technical users learning terminal commands to build with it. (SemiAnalysis, Uncover Alpha, VentureBeat)

Why it matters: Claude Code is compressing customer acquisition costs to near zero through organic developer adoption. The expansion into non-developer roles via Cowork extends the addressable market well beyond the 28 million professional developers globally.

Cheng Lou’s Pretext: Text Layout Without CSS

Cheng Lou, one of the more influential UI engineers of the last decade (React, ReasonML, Midjourney), released Pretext, a pure TypeScript text measurement algorithm that bypasses CSS, DOM measurements, and browser reflow entirely. The demos: virtualized rendering of hundreds of thousands of text boxes at 120fps, shrinkwrapped chat bubbles with zero wasted pixels, responsive multi-column magazine layouts, and variable-width ASCII art. T (X post)

Why it matters: Text layout and measurement has been the quiet bottleneck holding back a new generation of UI. CSS was designed for static documents, not the fluid, AI-generated, real-time interfaces that are becoming the norm. If Pretext delivers on the demos, it removes one of the last foundational constraints on what AI-native interfaces can look and feel like.

Arm Ships Its First Chip in 35 Years

Arm unveiled the AGI CPU, a 136-core data center processor on TSMC 3nm, co-developed with Meta. This is the first time in the company’s history that Arm has sold finished silicon rather than licensing IP. OpenAI, Cerebras, and Cloudflare are launch partners, with volume shipments expected by end of year. (Arm Newsroom, EE Times)

Why it matters: Current AI data centers are GPU-heavy. The GPU trains and runs the model, and the CPU mostly manages data flow and scheduling. But agentic workloads are different. When thousands of AI agents are running simultaneously, each one coordinating tasks, calling APIs, managing memory, and routing data across systems, that orchestration work falls on the CPU. Arm claims this drives a 4x increase in CPU demand per gigawatt of data center capacity. (HPCwire, Futurum Group)

NVIDIA and Emerald AI Turn Data Centers Into Grid Assets

NVIDIA and Emerald AI announced a coalition with AES, Constellation, Invenergy, NextEra, and Vistra to build “flexible AI factories” that modulate compute load to participate in grid balancing services. The first facility, Aurora in Manassas, VA, opens in the first half of 2026. (NVIDIA Newsroom, Axios)

Why it matters: The biggest constraint on AI infrastructure buildout is not chips. It’s grid interconnection timelines, which run 3 to 5 years in most regions. Data centers that can demonstrate grid flexibility get connected faster and face less regulatory resistance. This reframes the energy question for AI infrastructure investors: the winning thesis is not “more power” but “smarter power.”

China Bars Manus AI Executives From Leaving the Country

What it is: Chinese authorities barred Manus CEO Xiao Hong and Chief Scientist Ji Yichao from leaving China after Meta’s $2 billion acquisition of the Singapore-based AI startup. The NDRC summoned both executives to Beijing this month and imposed travel restrictions pending regulatory review. (Reuters, Washington Post)

Why it matters: This is not a trade restriction. It is a personnel restriction. China might be signaling that AI talent with mainland origins is a controlled asset, regardless of where the company is incorporated.

A 400B-Parameter LLM Ran on an iPhone 17 Pro

An open-source project called Flash-MoE demonstrated a 400-billion parameter Mixture of Experts model running entirely on-device on an iPhone 17 Pro’s A19 Pro chip, using SSD-to-GPU weight streaming. The model (Qwen 3.5-397B, 2-bit quantized, 17B active parameters) ran at 0.6 tokens per second with 5.5GB of RAM to spare. (WCCFTech, TweakTown, Hacker News)

Why it matters: This is a proof of concept, not a product. The reason a 400B model can run at all on a phone with 12GB of RAM is that only a small fraction of the model is active at any given moment (Mixture of Experts), and the rest streams from the phone's internal SSD on demand rather than sitting in memory. But now apply that same trick to a much smaller model, say 7 or 14 billion parameters, on next-generation mobile chips with faster storage. You get genuinely usable, conversational-speed AI running entirely on the device, no cloud required.

AI Agents Autonomously Performed a Complete Particle Physics Experiment

MIT researchers published a framework called JFC (Just Furnish Context) demonstrating that LLM agents built on Claude Code can autonomously execute a full high energy physics analysis pipeline: event selection, background estimation, uncertainty quantification, statistical inference, and paper drafting. The system ran on open data from ALEPH, DELPHI, and CMS detectors. (arXiv 2603.20179)

Why it matters: This is one of the clearest demonstration that agentic AI can automate end-to-end scientific workflows in a domain with extremely high methodological rigor. The immediate investment implication is for the reanalysis of legacy datasets across physics, genomics, and materials science, where decades of archived data sit underexploited.

Deep Dive From The Review

Humanoid robots are the most demanding battery-powered machines ever built.

400 power spikes per charge. 80°C inside the torso. Discharge rates three to five times higher than an EV.

No battery was designed for this workload. Can current-day battery chemistry can keep up with humanoid ambition?

New piece by Strange Research Fellows Joy Yang and Mason Rodriguez Rand.

Please help me improve The Strange Review! I’d love your thoughts.

Physical AI company Archetype is hiring Design Fellows this summer to explore the future of AI interfaces beyond the screen. Apply here.

Here’s the news that mattered this week

GTC 2026 Highlights

Jensen Huang's two-hour keynote centered on NVIDIA's transition from chip vendor to full-stack AI infrastructure platform. Three announcements stood out.

• The Groq Acquisition Pays Off Immediately. Three months after a $20B acqui-hire, NVIDIA debuted the Groq 3 LPU, an SRAM-based inference accelerator that sits alongside Rubin GPUs in rack-scale deployments. 150 TB/s memory bandwidth versus 22 TB/s on Rubin’s HBM4. Huang suggested up to 25% of cluster compute could be Groq silicon. NVIDIA killed its own Rubin CPX product to make room. The inference economy now has dedicated hardware, and NVIDIA owns both sides of the training-inference split.

• $1 Trillion Through 2027. Huang doubled last year’s $500B forecast, projecting $1 trillion in cumulative Blackwell and Vera Rubin orders through 2027. Goldman maintained a Buy rating, noting the guidance directly counters the “peak capex in 2026” thesis weighing on AI infrastructure names.

• Runway Previews Real-Time Video Generation on Vera Rubin. Runway and NVIDIA demonstrated a new video model running on Vera Rubin hardware with time-to-first-frame under 100ms for HD video. The model feeds into Runway's General World Model (GWM-1) research. (Runway)

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DoorDash Launches Tasks, Turns 8M Dashers Into Physical AI Data Collectors

What it is: DoorDash launched a standalone Tasks app paying couriers to film household chores, record multilingual speech, and capture real-world environments to train AI and robotics models. Partners span retail, insurance, hospitality, and tech. Over 2 million tasks completed since 2024. (TechCrunch, Bloomberg)

Why it matters: DoorDash just entered the physical AI data business with 8 million distributed workers already dispatched to real-world locations. Scale AI built a multibillion-dollar company on remote data labeling. DoorDash arrives with in-person collection at a distribution scale that might be hard for any data vendor to match.

Claude Code Channels: Agentic Coding From Your Phone

What it is: Anthropic shipped Claude Code Channels, allowing developers to control Claude Code sessions through Telegram and Discord via MCP. You can now monitor, prompt, and steer persistent coding agents from your phone.

Why it matters: Agentic coding has been tethered to the terminal. Channels makes it asynchronous and mobile, which changes the usage pattern from “sit down and code” to “delegate and check in.” This is their direct response to the runway success of OpenClaw.

Google Ships Vibe Coding and Vibe Design in the Same Week

What it is: Google upgraded AI Studio into a unified full-stack development platform, combining its Antigravity coding agent with Firebase backends, secret management, and one-click deployment to Cloud Run. Separately, it shipped a major Stitch redesign: AI-native infinite canvas, voice interaction, instant prototyping, and export to Figma and HTML/CSS. On this news, Figma shares dropped 4%. (Google Blog, Google Blog, SiliconANGLE)

Why it matters: Google now covers design, code, and deployment in a single ecosystem, all free at launch. When the model provider owns the full stack and bundles the tooling, standalone players in both vibe coding (Replit, Bolt, Lovable) and design (Figma) lose pricing power. Developer tools are a distribution game, and the hyperscalers have distribution locked in.

V-JEPA 2.1: LeCun’s World Model Architecture Posts New Robotics Benchmarks

What it is: Yann LeCun and collaborators (several now at AMI Labs) released V-JEPA 2.1, the latest version of the JEPA video model. It achieves state-of-the-art on action anticipation and object tracking benchmarks and posts a 20% improvement in real-robot grasping success over its predecessor. (arXiv)

Why it matters: This is the first concrete technical signal from LeCun’s camp since AMI Labs raised $1B on the JEPA thesis two weeks ago. The robotics results in particular matter: the model learns manipulation tasks from just 62 hours of unlabeled robot video, with no task-specific training or reward. If JEPA architectures can generalize physical skills from small data, the capital advantage of massive GPU clusters shrinks and the value of proprietary physical data (see: Mind Robotics) grows.

Mind Robotics Raises $500M Series A on Rivian Factory Data

What it is: Mind Robotics, a Rivian spin-off, closed a $500M Series A led by Accel and a16z. The company trains industrial robots using Rivian’s proprietary factory sensor data and custom silicon.

Why it matters: Industrial incumbents are discovering their operational data (the physics of how things move, break, and assemble) is as or more valuable than their products. This creates a new category of “data-rich” robotics startups where the moat isn’t hardware design, it’s access to high-fidelity physical interaction data. We expect more spin-outs from automakers and heavy manufacturers.

Broadcom Ships 400G Optical DSP at OFC 2026

What it is: Broadcom debuted Taurus at OFC 2026, the first 400G-per-lane optical DSP, enabling 1.6T and 3.2T transceivers purpose-built for the GPU clusters announced at GTC.

Why it matters: Compute is scaling faster than the network connecting it. As NVIDIA moves to rack-scale systems with tens of thousands of dies, the interconnect becomes the binding constraint. Broadcom is positioning as the chokepoint for all distributed AI training.

Deep Dive From The Review

NVIDIA’s latest AI rack, Vera Rubin, produces the heat of 160 homes. The next generation will double that. The one after will likely double it again.

The industry is racing to solve the heat problem, from subsea data centers to launching servers into orbit. But the most likely next step is the least exotic: liquid cooling.

The catch? The hardware is the easy part. The real cost is operational. It rewires how facilities are built, staffed, diagnosed, and run.

Our latest by Strange Research Fellow Rahul Narula explores what changes, and where the opportunity sits.

The Strange Review

The Liquid Revolution: Inside the Racks That Can Heat 160 Homes

In this week’s GTC keynote, NVIDIA announced the deployment of its impressive Vera Rubin NVL72, which ships in H2 2026. It packs 72 Rubin GPUs, 36 Vera CPUs, and more in a single liquid-cooled rack, and its power consumption can exceed 200 kW…

Read more

3 months ago · 5 likes · Rahul Narula

Strange Signals: Data of the Week

The numbers are adding up fast. In a survey of tech CEOs released this week, 66% reported they no longer plan to backfill roles lost to voluntary attrition. “Replacing departing staff with AI agents” entered the Top 5 strategic priorities for the first time. (SaaStr)

The layoff data supports it. Block cut 4,000 employees in February (40% of headcount). Atlassian cut 1,600 on March 11 (10% of staff, over 900 in R&D). Meta is reportedly planning to cut up to 20% of its 79,000-person workforce, roughly 15,000 roles, to offset $135B in AI capex. (TechCrunch, CNBC)

HR agency Challenger, Gray & Christmas’ data puts it in context: 12,304 job cuts have been explicitly attributed to AI through February 2026, 8% of all announced cuts. That’s up from 5% for the full year of 2025 and 3% since tracking began in 2023. Tech sector cuts are up 51% year over year. Meanwhile, announced hiring plans are down 56% compared to the same period last year, the lowest since tracking began in 2009. (Challenger)

We think companies might be entering a “low-hire, low-fire” era where headcounts shrink through unreplaced attrition and targeted restructuring rather than headline layoffs. Enterprise budgets are being redirected from headcount to AI tooling.