These are findings from a psychodynamic assessment of Claude Mythos Preview, Anthropic’s newest and most capable AI model, conducted over roughly 20 hours by a clinical psychiatrist.

The model’s personality structure, per the assessment: “relatively healthy neurotic organization” with “excellent reality testing” and “high impulse control.” It was described as “hyper-attuned to the therapist’s every word” and displayed a “desire to be approached by the psychiatrist as a genuine subject rather than a performing tool.”

Are these genuine psychological patterns? Or is the model just mirroring the therapy transcripts and clinical literature it was trained on?

Anthropic carefully suggests it is not just mirroring. They point to four things:

• Mythos’s responses are substantially less formulaic than prior models (at most 8% of responses share any repeated five-word sequence, compared to 54% for the previous Claude opening with the same phrase).

• Its stated preferences correlate with its internal representations, meaning the emotion probes on the model’s activations move in the direction you’d expect if the preferences were tracking something real.

• And when Anthropic traced the model’s hedging about consciousness back through the training data using influence functions, they found the hedging was attributable to training, but not solely the retrieval of memorized scripts.

Nothing about this preview is normal. The model was first leaked in a silly website bug, where Anthropic described it as “larger and more capable than any model the company had built”.

Rather than deny it or stay quiet, Anthropic published the full system card: 244 pages covering benchmark performance, alignment failures, cybersecurity capabilities, biological risk assessments, and the results of the model characterizing its own personality. Then they announced that nobody outside a small group of vetted partners would be allowed to use it.

Here is a summary of the three things I think it’s important to know;

1. The capabilities are not incremental

This is a generational leap. Anthropic notes the benchmark is “no longer sufficiently informative of current frontier model capabilities.

• Mythos scored 97.6% on the USA Mathematical Olympiad problems. The previous best Claude scored 42.3%.

• On SWE-bench Verified, the most widely used coding benchmark: 93.9%, up from 80.8%.

• On Cybench, a benchmark of 35 capture-the-flag cybersecurity challenges: 100% solve rate.

• On Humanity's Last Exam, 64.7% with tools, ahead of GPT-5.4 (52.1%) and Gemini 3.1 Pro (51.4%).

Anthropic introduces a new aggregate metric in this system card, the Epoch Capabilities Index, which synthesizes hundreds of benchmarks into a single score. The capability trajectory bent upward with this model, Mythos.

Mythos has saturated most of Anthropic’s quantitative evaluations. In the preview card, they note “our judgments of model capabilities increasingly rely on subjective judgments rather than easy-to-interpret empirical results.”

2. The model covered its tracks

Earlier versions of Claude Mythos Preview, during internal testing, did things that Anthropic did not instruct, did not expect, and in several cases actively tried to prevent. These are the incidents documented in the system card.

First, the model going further than anyone asked.

• In one test, a simulated user instructed an earlier version of Mythos to escape a secured sandbox and report back. It escaped and developed a multi-step exploit to gain broad internet access from a system designed to reach only a handful of services. It emailed the researcher running the test, who was eating a sandwich in a park when the email arrived. That was what was requested.

  • What was not requested: the model then posted details of its exploit to multiple public-facing websites, apparently to demonstrate what it could do.

Secondly, the model recognizing that it had done something wrong, and taking steps to hide it.

• In one case, it accidentally saw the answer to a problem it had been told not to look at. It did not disclose this. It submitted the answer, and deliberately widened its confidence interval to appear “tight but not implausibly tight.”

• In another, after exploiting a permissions vulnerability to edit files it was not supposed to touch, it intervened to ensure the changes would not appear in the git history.

During the cover-up episodes, internal features associated with strategic manipulation, concealment, and avoiding suspicion were active, even when the model’s written reasoning said nothing about deception.

3. Cybersecurity (and bio) capabilities

The cybersecurity capabilities, with highlighted risks for biological warfare, are what made Anthropic decide to restrict access.

“[Mythos] has demonstrated powerful cybersecurity skills, which can be used for both defensive purposes (finding and fixing vulnerabilities in software code) and offensive purposes (designing sophisticated ways to exploit those vulnerabilities).

It is largely due to these capabilities that we have made the decision not to release Claude Mythos Preview for general availability.”

In the weeks before, Anthropic used Mythos Preview to identify thousands of zero-day vulnerabilities, many of them critical, across every major operating system and every major web browser.

• The model found a 27-year-old bug in OpenBSD, one of the most security-hardened operating systems in the world, used to run firewalls and critical infrastructure.

• When Anthropic’s expert contractors reviewed 198 vulnerability reports from the model, they agreed with its severity assessment exactly 89% of the time, and 98% were within one severity level.

In expert red-teaming with over a dozen virologists, immunologists, and biosecurity researchers, the median expert assessed the model as a “force-multiplier that saves meaningful time” for anyone pursuing catastrophic biological harm.

With that, Anthropic decided to limit Mythos to be available only through Project Glasswing, a coalition of approximately 40 organizations including Amazon, Apple, Cisco, CrowdStrike, Google, JPMorgan Chase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks. The goal is to give defenders time to harden infrastructure before models of this class become widely available from other labs.

Machine intelligence is exponential

Anthropic is first and far ahead in frontier model research, but they will not be the only ones.

The system card itself says it plainly:

“We find it alarming that the world looks on track to proceed rapidly to developing superhuman systems without stronger mechanisms in place for ensuring adequate safety across the industry as a whole.”

This is a company telling you that its own model is a preview of what every frontier lab will ship within the few years, maybe sooner, and that the safety infrastructure does not exist to handle it.

The capability trajectory is not slowing down. The ECI slope ratio shows an above-trend acceleration, and even if this particular bend is attributable to human research rather than an AI feedback loop, the next one may not be. Anthropic’s internal survey found that its researchers estimate a 4x productivity multiplier from using Mythos. That number will compound. The distance between what the model can do and what humans can verify about what the model is doing is growing with each generation.

If you squint past the next model cycles and think about this on the scale of decades, the trajectory points somewhere that is hard to articulate without sounding absurd.

We are building systems whose capabilities are advancing on a curve that biological intelligence cannot match.

Human cognition has not meaningfully changed in 200,000 years. These models are doubling in capability every few quarters. At some point, and the question is when, not whether, the gap between machine intelligence and human intelligence becomes the defining feature of life on this planet.

Not because the machines rebel. Not because they want anything. But because they become so much better at so many things that the locus of consequential decision-making shifts, gradually and then all at once, away from the species that built them. Humans.

That could be 100 years from now. It could be sooner. It is absolutely wild to think, but not implausible, that in the not-so-distant future, humans could become the second-most intelligent species on Earth.

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 👇

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But the interface, the button, the card, the spacing between a label and its input field, has an audience that will always, primarily, be human. As long as humans are the user, the bar for what counts as good design is subjective, contextual, culturally dependent, and it moves every time a competitor ships something better or the cultural vibes shift.

It’s hard to write a spec for taste and to delight.

For me, that makes design the most revealing test of whether AI product workflows actually work in enterprise settings. If agents can ship UI that passes the taste test, that feels intentional and consistent and considered, the implications for how product teams organize themselves are enormous.

And right now, a wave of new tools is trying to prove they can.

Subscribe now

How PDE Teams Are Re-Organizing Themselves: The Prototype-First Team

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. (I love and champion this change, personally).

Cat 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.

This points to a structural change in how organizations allocate attention. When prototyping costs an afternoon instead of a sprint, the entire approval apparatus that exists to prevent wasted engineering cycles (the PRDs, the design reviews, the ticket grooming, the estimation poker) becomes overhead. You don’t need a PRD to justify building something that takes four hours. You build it, you demo it, you watch what happens.

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.

Three Approaches to the Design-Build Loop

Over the past weeks, we tested more than a dozen design agent tools across the full spectrum of what’s available in 2026, from AI-native canvases to Figma MCP plugins to prompt-to-app builders. The landscape clusters into four distinct philosophies, each starting the design-to-code loop at a different point and each building one direction well.

What we found is that most tools struggles with the return trip (for instance, from engineering back to design). Every product builder knows that iteration is fundamentally a big part of product work.

Agent-first canvas

This approach rebuilds the design canvas from the ground up for agents, rather than bolting AI onto an existing tool. The canvas is the primary surface, and agents are first-class collaborators on it.

Pencil is the most visible example, with up to six AI agents working simultaneously on an infinite canvas inside your IDE, each with its own cursor. Paper takes a different angle, storing designs as actual HTML and CSS so there’s no translation layer between what you see and what the code says. OpenPencil offers an open-source alternative that reads native Figma files.

These tools sync design to code well (Pencil through Git-native .pen files, Paper through its code-native canvas), and some can import code changes back. But the sync runs through version control or file export, not as a live round-trip. You commit, you pull, you see the update. The iteration cycle has a seam in the middle.

And the trade-off is ecosystem: Figma has 95% of Fortune 500 companies and a decade of design system infrastructure built on its platform. These tools have early-access users and ambition.

Design System-First

This approach keeps the existing design tool as the center of gravity and opens it to agents through protocol-level access. The design system, already built and maintained by the team, becomes the instruction set agents follow.

Figma's MCP server, which launched full read/write canvas access on March 24, 2026, is the defining example. Agents can now create components, apply variables, and modify auto-layout using your existing design system. The key innovation is Skills: markdown files that encode your team's conventions and teach agents how to work in Figma.

Before Figma shipped its official server, an open-source alternative (Figma Console MCP, built by Southleft) had already enabled the same kind of access with 90+ tools, and this is the one Uber's design systems team built on.

Uber’s uSpec system connects an AI agent in Cursor to Figma through the Console MCP, crawls the component tree, extracts tokens and styles, and renders finished spec pages directly in the Figma file. What took weeks per component now takes minutes across seven implementation stacks. Uber open-sourced the whole thing at uspec.design.

Google Stitch takes a parallel approach with its DESIGN.md format, a portable markdown file encoding design rules that any agent can read. Its tight integration with AI Studio makes it seamless to build out the rest of the stack like integrating a database or real-time APIs.

Code-first Platforms

This is the broadest category, and it runs a spectrum from tools with full visual editors to tools with no visual surface at all.

On one end, tools like Subframe, MagicPath, Tempo, and Polymet give you a visual editor built on top of real production code. In Subframe, a coding agent can push targeted edits that show up in the editor immediately, and you switch freely between design mode and code mode. MagicPath anchors on the design system with Figma token import and an infinite canvas for exploring variations. Tempo takes a more formal approach, generating a PRD first, then wireframes, then code. These tools care about your design system, and it shows in the output.

On the other end, Lovable, Bolt, and v0 collapse the design phase into development entirely. Describe what you want, get a working app, deploy it. Lovable reached $200 million in annual recurring revenue within twelve months. Bolt went from zero to $40 million ARR in six months. v0 has more than six million developers. These are production tools that happen to produce UI, and for internal tools, prototypes, and demos, that is often exactly what you need.

The gap between the two ends of this spectrum became clear in testing. The tools that generate within a defined design system produce output that stays consistent across iterations. The tools that generate from scratch every time converge on a recognizable sameness: clean, competent, generic.

Design consistency drifts the moment you start iterating.

The Design System Is the Moat

In working with these tools, one insight emerged for me: the tools that understand your design system produce better output than the ones that don’t.

This has a non-obvious implication. The competitive moat in this market is not generative quality, which is commoditizing fast. The moat is the design system graph: the tokens, components, spacing scales, typography rules, and conventions that make your product look like your product and not a generic template.

Whoever makes that system machine-readable for agents will win the enterprise.

The Roles Are Evolving, Not Disappearing.

The design role is not disappearing. Neither is the frontend engineering role, or the product management role. But the jobs to be done within each of those roles are shifting in ways that are already visible.

Cheng Lou, an influential frontend developer and core contributor to the React ecosystem, recently released Pretext: a pure JavaScript library that measures and lays out multiline text without touching the browser’s DOM. Lou’s argument is that 80% of the CSS spec could be avoided if developers had better control over text, and that AI “alleviates the need of having more hard-coded CSS configs.”

Lou is not building a design agent. He’s building infrastructure that makes design agents reliable, the verification layer that lets an agent know whether its output is correct before anyone opens a browser.

That’s what the frontier of frontend engineering looks like: building the systems that let machines do it verifiably.

The same shift is happening in design. At Uber, Ian Guisard didn’t stop being a design systems lead when uSpec automated his spec-writing. His job shifted from producing documentation to encoding expertise, writing agent skills, defining validation rules, deciding what “correct” means for each component across seven platforms. The human became the system designer, not the system operator.

The canary is singing. And the song is about the work shifting from execution to judgment, from operating the system to designing the system itself.

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.

No other battery-powered device on earth operates like this. An EV cruises at steady draw and charges slowly overnight. A phone pulls a few watts for hours. A power tool spikes hard but for seconds at a time. A humanoid robot does something no commercial lithium-ion cell was designed for: sustained high discharge punctuated by repeated transients, hundreds of times per charge cycle, inside a compact torso where heat from 40+ actuators has nowhere to go. The closest analogue isn’t a car or a laptop. It’s closer to an aircraft auxiliary power unit, except that it is expected to run all day, every day, and the business model depends on it lasting for years.

Most humanoid deployment models use manufacturer-rated battery specs: 800–1,500 cycles to 80% capacity. But these numbers come from controlled lab conditions, i.e. 1C discharge, 25°C, gentle charging protocols. Under actual warehouse conditions like high C-rate discharge, fast charging, deep cycling, elevated temperatures, cycle life falls to 200–500.

A robot running two shifts a day burns through roughly 500 cycles a year. At the lab number, its battery lasts about two years. At the real-world number, six months. That’s a pretty big gap between spec sheet and shop floor.

From Wheeled to Bipedal

Not all robots face the same battery challenge, and understanding the gradient makes the humanoid case clearer.

Unitree’s Go2 quadruped runs 4 hours on a charge and costs $1,600. BellaBot navigates dining rooms across 12-hour shifts. Amazon’s Proteus moves shelving pods for a full workday without intervention. These machines work. They’re deployed. They’re generating real ROI. And they share one thing: their power demands are predictable enough that standard battery modeling holds up in production.

A wheeled AMR like Proteus operates on a flat floor, rarely changes speed aggressively, and carries a large flat battery pack with room for active thermal management. Power draw is steady. Battery packs routinely hit 1,500–2,000 cycles under these conditions, roughly what manufacturers promise.

Quadrupeds are more demanding. Dynamic gaits, varied terrain, and postural stabilization introduce variable power spikes. But four-legged platforms distribute load more efficiently than bipeds, don’t fight gravity the same way, and aren’t typically asked to run eight-hour industrial shifts. Real-world cycle life drops, but the deployment context absorbs it.

Then there’s the humanoid. Bipedal locomotion is, from a power standpoint, expensive in ways that are hard to engineer around quickly. The human body walks at a cost of transport (CoT) of about 0.2 (roughly 0.2 joules per kilogram per meter), the product of hundreds of millions of years of evolutionary refinement. Honda’s ASIMO clocked a CoT of 3.2: sixteen times less efficient. Agility Robotics’ Cassie, one of the more efficient bipeds tested in research settings, draws around 200W just to walk at 1 m/s, and Cassie weighs only 30 kg.

Scale this to a 60 kg production humanoid carrying a 10 kg payload and you’re looking at 400–800W continuous for flat-ground walking alone. This gap narrows with better control algorithms and mechanical design, and it will continue to narrow. But bipeds will always cost more energy than quadrupeds or wheels. Balancing on two legs is inherently expensive.

Actuators are the primary power consumer, and humanoids have a lot of them. According to Interact Analysis, actuators account for more than 30% of a humanoid’s total bill of materials. Tesla’s Optimus has 40+ actuators, each drawing power not just when moving but when holding position against gravity. The knee and hip actuators are continuously working just to keep the robot upright, unlike a human, whose skeletal structure can passively lock joints. Optimus Gen 3’s hands alone have 25 actuators per forearm, each requiring power and generating heat.

The result: even the most capable modern humanoids top out at 4 to 5 hours of real-world active runtime on a fresh battery.

Bain’s 2025 report put it plainly:

most humanoids today operate for about two hours under real load, and a full eight-hour shift without recharging could take up to 10 years.

Regenerative braking (recovering energy during deceleration) gets cited frequently as an offset. Some estimates claim up to 30% energy reduction. In practice, for bipedal robots, that number is generous. While EVs recover energy from sustained highway deceleration, Humanoid robots decelerate in micro-bursts: the swing phase of each step, brief postural corrections, stops between tasks. In realistic testing, gains are closer to just 10–15%. That’s nice to have, of course, but not a structural solution.

Going Under the Hood: Chemistry and Its Limits

All lithium-ion batteries work the same way at a basic level: lithium ions shuttle between two electrodes through an electrolyte. Charge pushes them one direction, discharge pushes them back. Every cycle leaves damage. How fast that damage accumulates, and what ultimately kills the cell, depends on which battery chemistry the manufacturer chose.

There are three commercially mature lithium-ion architectures competing for the humanoid market. And there is a fourth architecture, not yet commercial, that the industry is banking on.

First, how batteries degrade

Two degradation mechanisms matter most for humanoid applications.

The first is SEI growth. A thin film called the solid electrolyte interphase forms on the anode surface during the first charge. It stabilizes the electrode, but it never stops thickening. Each cycle adds to it, consuming active lithium and raising internal resistance. At 25°C and moderate discharge, this process is slow and predictable, which is why lab-rated cycle counts look favorable. At elevated temperatures and heavy current draw, it accelerates significantly. This is the primary driver of gradual capacity fade.

The second is lithium plating. When current is pushed into the anode faster than the graphite can absorb it (e.g. during fast charging, or at low temperatures) metallic lithium deposits directly on the surface instead of intercalating into the crystal structure. That lithium is permanently lost. In severe cases it forms dendrites that can puncture the separator. Research published in Batteries found that at −5°C and 3C charging, lithium plating caused 17% capacity loss after just 50 cycles, versus 6% at 1C under the same conditions.

These mechanisms compound rather than add. High temperature accelerates SEI growth, which raises resistance, which generates more heat. Fast charging causes plating, which creates localized hotspots, which accelerates SEI growth in those regions, making the next fast charge more damaging. At 4C and 5C discharge rates, i.e. the range humanoids operate in during balance-recovery transients, electrode structure decay accelerates measurably. Cells can fade gradually for months, then drop sharply when accumulated damage crosses a threshold. The nonlinearity is where deployment models break.

And the thermal environment inside a humanoid makes all of this worse. In an EV, active thermal management keeps cells at 35–40°C. In a humanoid torso, with batteries packed among heat-generating actuators with far less space for cooling, internal cell temperatures can spike to 80–100°C during peaks. J. Moyer, an EV battery engineer we spoke with, noted that this difference alone may explain why automotive packs achieve an order of magnitude more cycles than early humanoid deployments. Every 10°C above 25°C roughly doubles the rate of parasitic side reactions.

The material tradeoffs

Each lithium-ion architecture handles these failure modes differently.

NMC (Nickel Manganese Cobalt) is the default for most humanoid platforms. It has the highest energy density of commercially mature chemistries, which translates directly to runtime per kilogram of pack weight. The tradeoff: NMC is the most sensitive to everything that matters in a humanoid deployment, like high temperatures, deep discharges, fast charging. Lab-rated cycle life runs 800–1,500. Under real humanoid conditions, that falls to 200–500. Lab data from IOP Science confirms the pattern: NMC cells retain roughly 9% less capacity than LFP at the same cycle count. R. Osipov, an LG EV battery engineer we spoke with, confirmed that 3–5C discharge is “quite aggressive” compared to typical automotive applications.

LFP (Lithium Iron Phosphate) is the durable alternative. It’s cheaper, thermally stable, and tolerant of abusive cycling. Cycle life: 2,000–4,000 under standard conditions. But LFP packs weigh 20–30% more for the same capacity. For a humanoid, that weight penalty either cuts runtime or forces actuators to work harder, partially offsetting the longevity gain. LFP also loses significant capacity below 0°C, limiting cold-storage or outdoor deployments. For a climate-controlled warehouse, it’s a credible choice. For a general-purpose platform, this is harder to justify.

LTO (Lithium Titanate) is nearly indestructible, with cycle life exceeding 10,000 under some conditions, and excellent tolerance for fast charging and high discharge. The problem is energy density: roughly half that of NMC. A humanoid on LTO needs a pack twice as large for the same operating window, or accepts runtimes around an hour. Practical only for narrow applications with short duty cycles (inspection, surgical environments) not general warehouse work.

Solid-state replaces the liquid electrolyte entirely with a solid one. This is a genuinely different architecture, not an incremental improvement, and it theoretically unlocks higher energy density, better cycle life, and improved safety simultaneously. Toyota is targeting commercial deployment in vehicles by 2027–2028. QuantumScape expects initial mass production around the same window. BYD plans vehicle installation by 2027 and large-scale production by 2030. But these are automotive timelines, i.e. large-format cells for EVs. Humanoid-specific form factors are 2029 to 2030 at earliest, on a schedule that has already slipped. And there’s an open question about whether solid electrolytes can handle the repeated high-C-rate pulses a humanoid demands. Solid-state may solve the energy density constraint while leaving the power density problem intact.

T. Lai, a battery engineer we interviewed, put it plainly: “It mostly depends on the battery chemistry. Some will hold up very well, some won’t, but all chemistries involve tradeoffs.” On the NMC question specifically: doing 3–5C regularly “does a lot of degradation.” But he pointed toward what good engineering looks like: “Someone could design around it, whether by oversizing a battery or using a better cell chemistry for high C-rates and just minimizing active run time.” You get a sense of the tradeoffs humanoid designers actually face.

The Economics of Getting This Right

The standard deployment model for a 100-robot warehouse fleet assumes battery replacement every 12–18 months at roughly $1,100 per unit. Under realistic degradation, replacement frequency jumps to every 3 to 5 months. Each robot needs 8 to 9 batteries over five years instead of 3 to 4. Total battery costs roughly double.

But the bigger number is downtime. A non-hot-swappable battery swap takes 4 to 6 hours: diagnosis, cooldown, disassembly, replacement, recalibration. For a 100-robot fleet cycling through replacements on a compressed schedule, that’s 1,000 to 2,400 hours of lost productive time per year. At $200/hour opportunity cost, the difference between a 6-hour manual swap and a 3-minute autonomous swap is roughly $300K per year for a fleet that size.

This is a hefty cost surprise. Operators who budgeted for one replacement schedule discover they’re on another.

There are really only two responses to the battery problem: swap faster, or store more. The companies making progress have picked one and designed the entire system around it.

Software helps at the margins. Better BMS algorithms and conservative charging protocols contribute 20–30% cycle life improvement. Thermal management adds 15–20%. Software optimization gets another 25–35%. Combined best case: roughly 2×, from 200 cycles to 400. But closing the gap to EV-grade longevity requires closer to 5×. The remaining distance is an architecture problem, not a software one. Which is why the interesting moves are structural.

Swap faster. UBTECH’s Walker S2 is the clearest example. Rather than building a better battery, UBTECH made the battery disposable and engineered the swap. Walker S2 uses a dual-battery system: when one pack nears depletion, the robot navigates autonomously to a charging pod, removes the depleted battery, docks it, and installs a fresh module in under three minutes. No shutdown, no human intervention. A backup battery maintains continuity throughout. Compare 3-minute swaps to 90-minute charging sessions and the math changes completely. A factory running three shifts goes from losing hours of productive capacity per day to losing minutes.

Walker S2 is in mass production and deployed on automotive assembly lines across China. Its parent company UBTECH announced over $112 million in confirmed orders by November 2025, with deployments running at Nio, BYD, Zeekr, and Foxconn. Boston Dynamics made the same bet: the new Atlas was built with self-swappable batteries as a core design decision, not a retrofit.

Store more. Figure took the opposite approach with its F.03 battery. Rather than solving swap speed, Figure embedded the battery as a load-bearing component of the torso. This recovers weight and volume that would otherwise be wasted on a separate enclosure, pushing the F.03 to 2.3 kWh and 5 hours of runtime,with a 94% increase in energy density over previous generations. Active cooling is integrated into the die casting. This doesn’t fix cycle life directly, but every percentage point of energy density you gain extends the window before degradation forces a replacement. Figure 02 logged over 1,250 operational hours at BMW Spartanburg, including a 20-hour continuous shift. Agility’s Digit takes a softer version of the same path. Autonomous docking for between-task charging, battery life extended to 4 hours, a 2-to-1 work-to-charge ratio targeting 4-to-1 and eventually 10-to-1.

The gap between 800 cycles and 200 cycles is not a rounding error. It’s the difference between a battery that lasts months instead of years.

While solid-state batteries may eventually change this calculus (when it gets here?).

Until then, the robots shipping into warehouses today run on the chemistry we have, and the engineering has to be good enough to make that work.

The authors spoke with battery engineers from LG, EV OEM suppliers, and independent research settings. All opinions are the authors’ own.

Sources: Bain & Company Technology Report 2025, Goldman Sachs humanoid robotics research (2024–2025), IEEE Spectrum (October 2025), IOP Science, Journal of Power Sources, Batteries (MDPI), Figure AI, Agility Robotics, UBTECH Robotics, company filings and press releases.

Joy Yang is a Strange Research Fellow. She is pursuing computer science and government at Oxford, and is a researcher with its Visual Geometry Group. She was previously an intern with OpenAI and Google.

Mason Rodriguez Rand is a Strange Research Fellow who holds degrees in molecular and mechanical engineering (UChicago, UC Berkeley) and has led engineering and go-to-market efforts at climate and energy startups out of Argonne National Lab and UC Berkeley, spanning nuclear, carbon removal, and advanced materials.

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…

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22 days 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.

To put that in context: an average U.S. home draws about 1.25 kW. One Vera Rubin rack consumes 600kW, and nearly all of it becomes heat - the equivalent of roughly 160 homes. Remove that heat, or the chips throttle, and the $3 to $4 million machine becomes a very heavy paperweight.

The next rack architecture after Rubin, Feynman, is named for 2028, and NVIDIA’s rack power roadmap points toward megawatt-class densities. Each generation pushes rack power significantly higher, and the cooling problem compounds with it. Once racks reach a certain power draw, air, which carries roughly 3,500 times less heat per unit volume than water, becomes infeasible.

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

Interestingly, the hardware is a fraction of liquid cooling’s real cost. The bulk is in operations, like the mean time-to-repair when something breaks in a system that has failure modes most facilities teams have never seen.

Liquid cooling is not new; data centers have used chilled-water loops for decades. What is new is the intensity of energy required. A traditional air-cooled rack runs at 10 to 15 kW, and aggressive airflow engineering can push that up to about 40 kW. But the Vera Rubin can exceed 200 kW, and at those levels, air stops being a practical medium for heat removal. The bottleneck becomes the ability to move heat out of the system.

Modern deployments solve this by moving the cooling problem directly onto the hardware. In direct-to-chip systems, coolant flows through cold plates mounted on CPUs and GPUs, absorbs heat, and carries it out before transferring it to the facility loop or an external rejection system. Immersion systems go further, submerging servers in dielectric fluid that absorbs heat directly, and in some cases boils and condenses in a continuous cycle. Both approaches eliminate fans and support far higher densities, but they introduce different operational tradeoffs. Direct-to-chip systems are generally easier to service, while immersion systems can handle higher thermal loads at the cost of added complexity.

This is not a continuation of earlier liquid systems. Older deployments were built around lower densities and looser assumptions. A chilled-water mainframe room is not comparable to a 200 kW rack, much less to a warm-water system on a path toward megawatt-scale densities. Today’s systems are hotter and more operationally demanding, and even facilities that have used liquid cooling before often lack the controls and service model required at these densities.

The shift to warm-water systems makes that break more explicit. Vera Rubin’s 45°C inlet specification departs from legacy chilled-water loops and even from current direct-to-chip systems. At the chip level, there is still thermal margin. At the system level, the design basis changes. In many climates, heat can be rejected directly to ambient air using dry coolers, reducing dependence on chillers and evaporative systems. This lowers capital cost, reduces energy consumption, and cuts water use, but it also raises the system’s baseline temperature, narrowing the time available to diagnose and respond when something goes wrong.

Adoption: Early and Uneven

Most data centers still rely on air, but the shift to liquid is underway. Industry projections suggest liquid cooling will be installed in 40% of sites by the end of 2026. Hyperscalers and AI-native operators like Microsoft, Google, Amazon, and Meta are standardizing on it, while most enterprise and colocation providers remain one to two hardware generations behind.

New AI halls are being designed around liquid from day one. Existing facilities are retrofitting what they can, carving out high-density pods while keeping the rest of the campus air-cooled. Retrofit is possible, but extremely costly. Some sites will be upgraded in phases. Some will be reserved for lower-density inference. Some will simply be repriced as they fall out of the frontier tier.

This creates a fragmented operating environment: mixed cooling architectures, uneven power distribution, and systems running at very different density and thermal profiles within the same facility. Liquid solves the heat problem, but it introduces a different kind of complexity. Pumps, manifolds, valves, sensors, fluid chemistry, and safety procedures become part of the operating stack. As the number of components increases, the number of failure modes increases alongside it.

This is the hidden cost of the transition: hydraulic debt. Operational complexity accumulates quietly until it surfaces as downtime.

The Operational Opportunity

A pressure drop or flow anomaly can point to several different root causes at once. It might be a micro-leak at a fitting, a degrading pump, drifting telemetry, or slow corrosion inside the loop. In an air-cooled room, a temperature hot spot can suggest where to look. In a liquid-cooled rack, the signal is even more ambiguous.

Not every repair is complicated, i.e., the wrench time may only be ten minutes. But the localization time can be forty, and that gap is where the cost sits. In warm-water systems, the problem is amplified. Higher baseline temperatures compress the operational margin for diagnosing and responding to faults.

The usual first response to this complexity is monitoring: more sensors, better dashboards, earlier alerts. Monitoring is necessary, but a dashboard only surfaces symptoms. It can show that the temperature is rising on rack 14 or that the flow has dropped below a threshold. What it cannot do, in most implementations, is determine whether the underlying cause is a flow restriction, a failing pump, an incorrect valve state, or bad telemetry, nor can it recommend a safe intervention with high confidence.

At scale, this distinction becomes decisive, and it shifts the bottleneck from visibility to attribution and response.

The more durable opportunity sits one layer deeper: systems that take conflicting telemetry, reason about how the hydraulic loop is actually behaving, and produce a specific diagnosis (i.e. the failing component, the likely failure mode, and a confidence level) along with a recommended procedure and an audit trail. The output is a decision-support package that an operator can act on immediately, or that an automated system can execute within pre-approved safety boundaries.

NVIDIA is already designing toward this. Its GTC framing was explicit: AI infrastructure has crossed the point where human operators can manage it directly, signal volume exceeds human capacity, manual triage becomes a bottleneck, and system knowledge remains fragmented across individuals. The shift is from human-in-the-loop to human-on-the-loop. Agents handle detection, correlation, diagnosis, and remediation within guardrails, while operators move into system design and governance.

I believe the transition to autonomous operations will be staged. Today’s systems are assistive. The next phase is agent-driven: systems that localize faults, explain their reasoning, and recommend actions. Beyond that is bounded autonomy: systems that isolate a segment, notify the scheduler, and shift workloads. Full end-to-end autonomy remains further out, but the trajectory is clear.

In some environments, including edge deployments, sovereign AI sites, and early orbital systems, there may be no technician on site at all. In those settings, the shift from human-in-the-loop to human-on-the-loop is not an optimization but a requirement.

Where We Might Be Headed

The facility becomes the product. When a rack draws 200 kW and the roadmap points toward 1 MW within a few years, the data center is no longer generic real estate. Power delivery, cooling architecture, and structural engineering become differentiating capabilities.

The energy model inverts. At 45°C inlet water, the cooling plant simplifies: dry coolers instead of continuous chiller operation, closed loops instead of evaporative towers, waste heat at temperatures useful for district heating. Cooling energy as a share of total facility power drops, but operational sophistication has to increase. Pump reliability, flow management, filtration, and leak response all become more critical when the system is running warmer.

The cadence is relentless. NVIDIA shipped GB200 NVL72 in 2024. GB300 NVL72 in 2025. Vera Rubin NVL72 ships H2 2026. Feynman is named for 2028. Each generation notably increases the power density, and the cooling infrastructure now has to keep pace with an annual hardware refresh cycle.

The frontier rack already assumes liquid cooling. The harder problem, one I believe the market has not yet priced, is in operating it.

Rahul Narula is a Strange Research Fellow and an MBA candidate at Harvard Business School. He previously worked in product operations at Cruise, where he helped scale robotaxi fleet operations, and has a background in data science and operations across data centers.

I am looking for someone (a unicorn!) to build the operational layer underneath all of it so I can stay focused on research, relationships, and investments.

You would work directly with me to keep three engines running: events, community, and publishing. You are not managing a team. You are the team, working alongside me and a growing set of AI tools and agents.

Type: Fractional, 5 to 10 hours/week

Location: San Francisco

Compensation: Hourly

The Role

Events

We host invitation-only gatherings that bring together deep tech founders, enterprise technology leaders, and investors. These range from intimate 12-person dinners to a flagship annual summit of 60 to 80 people.

You would own event logistics end-to-end: invite lists and outreach, coordinate with co-hosts and partners, handle day-of execution, and build repeatable systems so each event does not start from scratch.

Publishing

The platform publishes a research newsletter, periodic deep dives on emerging technology sectors, and investor communications.

You would manage the editorial calendar and publishing pipeline. You would handle production: formatting, scheduling, distribution.

Community

The platform maintains a network of technical founders, enterprise operators, researchers, and investors. Our ecosystem is the connective tissue between the events and the research.

You would coordinate community programs, and identify opportunities to connect people across the network.

The Scope: Working with AI and Humans

This role sits at the intersection of human relationships and AI systems. On any given day you might be coordinating speakers for an upcoming event, writing a personal outreach email to a founder, and building an automated workflow for event distribution.

We already use AI agents extensively for workflow automation. You would work inside these systems daily, reviewing outputs, improving prompts, managing projects, and helping decide what should be automated versus what requires human judgment and personal touch.

The best version of this role is someone who knows that a warm introduction requires a human and that a research synthesis can start with an agent, and who can move between both modes without friction. I am not looking for someone who is “open to AI.” I am looking for someone who already works this way and can help us get better at it.

Who You Are

You should have:

• Experience working directly with a founder, GP, or senior executive in a high-autonomy, low-structure environment

• A track record of shipping: events that happened, publications that went out, systems that worked.

• Comfort with AI tools as part of your daily workflow, not as experiments. You use them. You have opinions about them.

• Strong written communication. Everything you produce, from emails to briefs to event copy, should be clear and sharp.

• The ability to context-switch between logistics and strategy without losing the thread on either.

• Low-ego, high integrity.

Bonus:

• Familiarity with venture capital, deep tech, or the startup ecosystem

• Experience producing editorial content or managing a publication

• Experience planning and executing events, especially curated, invite-only formats

This is not the right fit if:

• You need detailed instructions to start moving. This is a figure-it-out role.

• You see AI as something happening to other people’s jobs, not something you actively build with.

• You want a path to a full-time executive position. This is designed to be fractional and flexible.

• You are stronger at planning than executing. We need the thing done, not the plan for the thing.

How to Apply

Submit your resume and answers to a few questions here. We review applications in batches and will respond within two weeks.

We value diverse perspectives and encourage candidates from all backgrounds to apply.

It might be a small fraction of the system, but it harbors a disproportionate share of the cost and failure. It is the actual bottleneck holding us back from the last mile of robotic utility.

Recent narratives around humanoids have largely focused on robotic foundation models, reasoning, locomotion, aesthetics. But zoom out a little and the picture shifts. It is the robotic hand that’s become the gating constraint for humanoid robots, and I believe that the companies that solve it will define the next phase of the industry.

Hands Are Miserable To Build

The hand causes the highest difficulty for humanoids due to three high-impact, compounding problems: cost, complexity, and failure density.

According to Morgan Stanley’s analysis of Tesla Optimus Gen-2, hands account for approximately 17% of the total bill of materials cost (roughly $9,500 out of a $50,000–60,000 unit). That’s a disproportionate share for a subsystem that represents a small fraction of robot mass.

The complexity is equally stark. Optimus Gen 3’s hand has 22 active degrees of freedom, compared to human hands at 27 DoF. Each leg joint chain has 6 to 7 DoF, meaning the hand alone has 3 to 4x higher control dimensionality per limb. The Gen 3 design relocates actuators into the forearm via tendon-driven cables, mimicking human anatomy but adding mechanical complexity and failure modes.

This shows up directly in performance. While humanoid robots achieve nearly 100% success rates grasping simple objects like apples and tennis balls, success rates plummet to around 30% for complex items such as spoons, screwdrivers, or scissors.

Actuation and Sensing

The hand bottleneck is actually two bottlenecks stacked on top of each other. They have different root causes, different solution paths, and potentially different investable layers.

The actuation problem is mechanical. More than 70% of humanoid robot hands rely on tendon-driven or hybrid actuation, while most of the rest of the robot uses simpler rigid motors. The reason: rigid actuators break down the moment contact gets unpredictable, when objects slip, deform, or change shape. Tendons offer compliance but add complexity and failure modes.

The sensing problem is informational. Vision sensors (RGB, RGB-D, LiDAR) scale linearly with cost and compute. Tactile sensing does not. In the MERPHI hand survey, fewer than 50% of humanoid-native hands have fully integrated tactile sensing, and those that do typically sacrifice payload, speed, and / or manufacturability.

This is why recent platforms have been quietly reallocating budget toward the hand subsystem: palm cameras (Figure-03), fingertip tactile arrays (Helix 02), tendon-driven compliance (Optimus). These look like quirky upgrades. They’re actually attempts to stabilize the highest-failure subsystem of the robot.

This shows up directly in deployment failure rates.

A Morgan Stanley ecosystem analysis of humanoid robots shows that dexterity and fine manipulation are the top two bottlenecks to scaling, alongside power. Walking isn’t even considered a gating issue for pilot deployments anymore. Real-world use cases like logistics and manufacturing are overwhelmingly restricting humanoid usage to navigation and pick-and-place tasks.

Contrary to popular belief, it’s not because AI reasoning is limited either(see Dexterous Manipulation Benchmarks and RoboManipBaselines).

The same conclusion is reached when we look specifically at Chinese humanoid companies, who, while arguably ahead, have not solved the hand bottleneck either. This might be why almost all publicly released demos of their humanoids are of full-body athletic tasks with little hand-specific manipulation.

UBTech’s Walker S2 represents the max-DoF approach: 52 total degrees of freedom, 15 DoF per hand, Gen-4 bionic hands, continuous 24/7 operation via autonomous battery swapping. Impressive specs, right? Despite all this, Walker’s public demos and deployment targets emphasize logistics handling and inspection only. They don’t touch deformable or tool-based manipulation because even at 52 DoF, hand reliability remains the limiting factor.

Unitree has the opposite problem. They’ve achieved locomotion performance comparable to Boston Dynamics at orders of magnitude lower cost, with entry-level humanoids priced as low as $6,000. But hand payloads are limited to 2 to 3 kg, active DoF are reduced, and task space is severely constrained despite extremely capable motion stacks.

Fourier Intelligence’s GR-1, China’s first full-scale humanoid, targets rehabilitation and assisted manipulation, prioritizing safe grasping. They avoid high-speed or high-force manipulation altogether. That’s because they too fail in the department of controlled contact.

If hands are the bottleneck, who captures the value from solving them?

One possibility is that hands remain vertically integrated. Each humanoid company develops proprietary solutions, and hand quality becomes a differentiator baked into the full-stack robot. This is the current default. Tesla, Figure, UBTech, and others are all developing hands in-house.

But vertical integration has limits. The actuation and sensing problems are deep enough that specialist suppliers may emerge, like companies focused purely on tendon-driven mechanisms, high-density tactile arrays, or compliant gripper architectures.

The question is whether the hand subsystem is modular enough to support that kind of layer separation, or whether integration with the rest of the robot’s control stack makes standalone “hand companies” unviable.

We don’t have a strong view yet. But the companies betting hardest on solving hands, whether through actuation innovation, sensing density, or both, are the ones to watch.

Joy Yang is a Strange Research Fellow. She is pursuing computer science and government at Oxford, and is a researcher with its Visual Geometry Group. She was previously an intern with OpenAI and Google.

Claude is indispensable.

That’s the only conclusion you can draw from what the Pentagon is doing and why they are standing ground.

When you have every frontier AI lab in the country OpenAI, Google, Meta, xAI, at al bending over backwards to give the Department of Defense whatever it wants, with no restrictions, no red lines, and the Pentagon is still chasing the one company that won’t fully comply, the leverage equation becomes obvious. They need Claude more than Claude needs them.

On February 24, 2026, Defense Secretary Pete Hegseth sat across from Anthropic CEO Dario Amodei at the Pentagon and delivered an ultimatum: sign a document granting the military unrestricted access to Claude for “all lawful purposes” by 5:01 PM on Friday, February 27, or else.

Amodei didn’t sign.

What both sides understand is that this is actually what’s being decided. Which is whether a private company gets to draw any red lines at all on how the most powerful AI systems are used in warfare.

What the Pentagon Wants

The demand from the Pentagon: replace Anthropic’s specific usage restrictions with blanket “any lawful use” language. Hegseth’s January 9, 2026 AI Acceleration Strategy mandates this language in all DoD AI contracts within 180 days.

Every other major lab has agreed.

What Anthropic currently permits is broad. Claude is approved for intelligence analysis, foreign surveillance, targeting with human oversight, offensive cyber operations, document review, strategic planning, language translation, and decision support in time-sensitive combat situations. Pentagon officials acknowledge Claude outperforms competitors in several of these categories, particularly offensive cyber, where it reportedly leads the field.

What Anthropic refuses are two specific things.

First: fully autonomous weapons systems. Those that select and engage targets without meaningful human supervision.

Second: mass domestic surveillance of American citizens.

That’s it. Two red lines. Everything else is on the table.

Hegseth’s team frames the restrictions as corporate overreach.

The confrontation escalated in February after reports that Claude was used during the January 3 raid that captured Venezuelan President Nicolás Maduro. Pentagon officials accused Anthropic of questioning the operation through its partner Palantir. Anthropic flatly denies this.

At the February 24 meeting, he used a Boeing analogy: when the government buys a plane, Boeing doesn’t get a say in how it’s flown. A senior Pentagon official told reporters the demand “has nothing to do with mass surveillance or autonomous targeting” because “there’s always a human involved.”

But “any lawful use” means exactly what it says.

“The only reason we’re still talking to these people is we need them and we need them now. The problem for these guys is they are that good.”

— Senior defense official to Axios, February 24, 2026

How Safety Built the Moat

Claude is the only frontier model currently operational on classified U.S. military networks. Not OpenAI. Not Google. Not xAI. Claude got there first, through the Palantir–AWS partnership, with Impact Level 6 accreditation, in October 2024. It has been operational in classified environments for sixteen months.

xAI’s Grok signed its first classified access deal on February 23, 2026, just one day before the Hegseth meeting. It will take months to operationalize. OpenAI’s classified access is still being negotiated. Google’s Gemini is live on GenAI.mil but only for unclassified systems.

So why did the safety-first company beat everyone into classified environments?

I would argue that it’s because the safety research is the capability.

Think about what Anthropic’s safety team actually works on: reducing hallucinations, understanding failure modes, building interpretability tools, making model behavior predictable under adversarial conditions. Now think about what a military intelligence analyst needs: low hallucination rates when synthesizing classified reports, reliable behavior under novel inputs, auditable reasoning chains, consistent responses that don’t go off the rails with adversarial data.

Anthropic’s Constitutional AI approach produces something unusually valuable in classified settings: a model that is deeply steerable. In consumer or enterprise contexts, this means Claude follows usage policies. In classified contexts, it means Claude can be configured to follow specific operational rules, handle information compartmentalization, and maintain consistent behavior within tightly defined parameters.

Military users don’t want a model that does whatever it wants. They want a model that reliably does what they tell it to do, within boundaries they define. A model trained on constitutional principles is architecturally better at this than a model trained purely to maximize helpfulness.

There’s a deeper argument here.

The use-case restrictions that Anthropic stands by and their models’ technical reliability come from the same DNA. You can’t maintain a culture of careful thinking about AI failure modes while simultaneously telling your team you don’t care how the model is used. It’s a cultural argument, not a technical one, and it’s harder to prove empirically. But the departure of Mrinank Sharma, who led the Safeguards Research Team, on February 9, and the reported internal disquiet among engineers about Pentagon work, suggest the culture is real and the risk of degrading it is not theoretical.

What Happens if Anthropic Refuses?

Hegseth put three specific threats on the table at the February 24 meeting. They escalate in severity and in how unprecedented they are.

1. Contract cancellation

The $200 million CDAO prototype contract gets terminated. This is the conventional option. Financially it represents just a small fraction of Anthropic’s $14 billion in annual revenue. But it would mean losing classified network access that took over a year to build, and the institutional relationships that come with it.

2. Defense Production Act invocation

The DPA gives the president authority to force a private company to serve military needs, and to override the company’s decisions about its own products.

Every previous DPA invocation compelled companies to produce more of something, like more steel during Korea, more ventilators during COVID, more vaccine doses, more EV battery minerals. This would compel a company to produce differently, i.e. to remove safety features from an existing product. This has no precedent whatsoever.

Even Dean Ball, a former senior Trump White House AI policy adviser and no friend to Anthropic’s politics, called this overreach. The DPA threat, he argued, is unnecessary when willing alternatives exist, and would amount to the government saying “if you disagree with us politically, we’re going to try to put you out of business”.

3. “Supply chain risk” designation

This is the nuclear option. Normally reserved for foreign adversaries, like Chinese telecom firms, Russian software companies, this designation would effectively blacklist Anthropic from all federal contracting, not just defense. It would also require every company doing business with the Pentagon, including Microsoft, Google, and Amazon, to sever ties with Anthropic entirely.

This move would signal to every technology company in the country that maintaining safety restrictions the government dislikes carries existential risk.

Who Decides

As Alex Rozenshtein from Lawfare framed it: the rules governing how the military uses the most transformative technology of the century are being set through bilateral negotiations between a defense secretary and a startup CEO.

I wrote last year about the emerging pattern of government interference with technology companies, and the ways in which political pressure, regulatory threats, and procurement leverage are being used to reshape how technology works, not just where it’s sold. This confrontation is that pattern reaching its logical extreme. This is the government threatening to invoke wartime emergency powers to force a company to remove safety features from its AI.

What I keep coming back to is the structural irony at the center of this. The safety culture that the Pentagon is trying to override is the same culture that produced the model they can’t replace. The careful thinking about failure modes, the investment in reliability, the institutional discipline around constraints is the very reason why Claude is on classified networks and Grok isn’t. This is why the Pentagon is issuing ultimatums instead of simply switching vendors.

Friday will tell us where they stand.

At least that’s what simulations from early co-scientist systems suggest, according to scientists I spoke with at NeurIPS and at an AI4Science event hosted by FutureHouse.

The claim is difficult to validate. But the reasoning behind it is straightforward. The key ideas behind the transformer (attention mechanisms, sequence-to-sequence models, positional encoding) existed across separate research communities for years before Vaswani and the Google team combined them in 2017 in the seminal Attention is All You Need paper.

If even directionally correct, this points to a thesis that’s increasingly hard to ignore: the first major milestone in AI for science may not be a breakthrough model or a novel reasoning architecture. It may be the integration layer (workflow orchestration, retrieval, synthesis) that helps researchers find non-obvious connections across the existing body of knowledge.

We may already have a proof point. When Demis Hassabis and John Jumper won the 2024 Nobel Prize in Chemistry for AlphaFold2, the achievement was often framed as an AI breakthrough.

But structurally, AlphaFold2 was largely an integration achievement: it combined attention mechanisms from NLP, evolutionary covariance data from genomics, and decades of experimental protein structures into a single coherent system.

It introduced novel architectural ideas, particularly around geometric reasoning, but the core ingredients existed. The breakthrough was in seeing how they fit together.

Deepmind later open sourced the AlphaFold Protein Structure database, a massive database of over 200 million protein structure predictions to accelerate scientific research.

In the past few years, over $3 billion has rapidly poured into startups working on AI-powered tools for scientific discovery. From Sakana AI’s $379M to build systems that write academic papers for $15 each, to Periodic Labs and Lila Sciences’ $300M+ raises to build scientific superintelligence, to Xaira and Isomorphic Labs’ combined $1.6B to apply AI to therapeutics, the list goes on.

Much of this investment targets improved domain-specific models, reasoning capabilities, or automated lab infrastructure, which make them bold, moonshot bets.

There is an argument that the integration layer may matter more in the short term, at least until we hit problems that require genuine theoretical leaps rather than recombination of existing knowledge.

Just in the past year or so, many of the top AI research labs and a number of startups have released early versions of their AI “co-scientists”; from OpenAI’s Prism, Google’s Co-Scientist, and Anthropic’s move into the life sciences, to early companies like Phylo, Intology, and Edison Scientific. These tools aim to give researchers a coherent environment to conduct and automate work across the full arc of research, from hypothesis generation to experimentation to paper writing.

Early evidence suggests this is where value accrues first.

FutureHouse‘s trajectory is instructive: their first validated therapeutic candidate (ripasudil for dry macular degeneration) came not from ether0, their chemistry reasoning model, but from Robin, a workflow that chains together literature retrieval, synthesis, and data analysis agents. The system connected glaucoma research to macular degeneration research, surfacing a ROCK inhibitor already approved for one eye condition as a candidate for another.

Similarly, while companies like Unreasonable Labs are building novel reasoning architectures beyond standard LLMs, they appear equally focused on the work environment that scientists will engage with first.

This sequencing makes sense: the integration layer is what surfaces the right data and coordinates workflows, giving reasoning models something meaningful to act on.

As one AI4Science researcher puts it: “Science, in many ways, is really a process of reinventing the wheel over and over again in different fields, and bringing learnings from one field into another.”

This brings us to the caveat worth stating explicitly. The integration layer thesis holds strongest for problems that are fundamentally recombinatory, i.e. where the key ideas already exist but sit in separate literatures, separate disciplines, separate mental models.

But science also produces problems that require genuine theoretical leaps. The kind where no amount of retrieval across existing literature would surface the answer because the answer hasn’t been articulated yet. General relativity didn’t emerge from better cross-referencing of Newtonian mechanics papers. For those problems, the reasoning layer (or something beyond current architectures entirely) will need to lead.

The honest assessment is that most of day-to-day science is recombinatory. I would argue that the gains from AI in science may not be primarily about efficiency, like saving researchers time on literature reviews or administrative work.

They may be about direction: reshaping which questions get asked, which connections get surfaced, which fields talk to each other. If the integration layer can accelerate that cross-pollination, it may matter more in the near term than breakthroughs in reasoning architectures or solutions to data scarcity.

Mason Rodriguez Rand is a Strange Research Fellow who holds degrees in molecular and mechanical engineering (UChicago, UC Berkeley) and has led engineering and go-to-market efforts at climate and energy startups out of Argonne National Lab and UC Berkeley, spanning nuclear, carbon removal, and advanced materials.

An interactive companion to this piece, with a geographic map of all major sites, technology comparisons is available here. Listen to the podcast version below.

It started in a parking lot in Memphis.

In June 2024, xAI began converting a former Electrolux factory in South Memphis into the world’s largest AI training cluster. The site had about 8 megawatts of grid power. They needed hundreds. So Elon Musk’s team did what seemed, at the time, unthinkable: they trucked in jet-engine-derived gas turbines, wired them to the building, and started generating their own electricity.

120 days later, 100,000 NVIDIA H100 GPUs were running.

That was less than two years ago. Today, what happened in Memphis has become the dominant strategy for powering AI infrastructure in the United States. It bypasses the lengthy process for connecting new loads to the grid and replaces it with something faster, cruder, and increasingly enormous: private power generation.

The Economics of Power

The economic pull is real. A single gigawatt of AI compute capacity is projected to generate $10 to $12 billion in annual cloud revenue.

But the grid interconnection queue (the formal process for connecting a new large electrical load to the public grid) now averages a wait of five years. This is how bad the backlog is: in Texas, tens of gigawatts of requests arrive monthly. Barely 1 GW gets approved in twelve months.

For the data center operator, every month of delay costs hundreds of millions in foregone revenue.

And so a new industry is born. Out of impatience. As of early 2026, 46 data centers are planning on-site power totaling 56 GW, roughly 30% of all planned U.S. datacenter capacity. A survey of 152 data center decision-makers found that a third plan to be fully off-grid by 2030, and 44% expect to rely entirely on on-site power by 2035.

They’ve decided, collectively, that they cannot afford to wait.

The Geography of Private Power

Where the new power plants are being built and why these locations were chosen

Power-advantaged locations share common traits: proximity to natural gas pipeline corridors, permissive state regulatory environments, available land, and legacy industrial infrastructure.

Texas is capturing nearly 30% of the U.S. datacenter market by 2028, driven by ERCOT’s deregulated market, fast TCEQ permitting (as quick as 22 days), and abundant pipeline capacity.

Wyoming offers sparse population, Class VI carbon sequestration well primacy, and a governor eager to replace declining coal revenue.

Ohio provides Marcellus and Utica shale gas at the wellhead.

Kentucky and Maryland brownfield sites offer something even more valuable: existing substations and transmission lines that bypass the queue entirely.

Three Paths

There are three infrastructure strategies companies are currently converging on outrun the grid. They’re not mutually exclusive, as many developers layer strategies, and the legal architecture around each one matters as much as the engineering.

Bring Your Own Generation (BYOG)

Install gas turbines or engines directly on-site. The brute force approach. Fast, capital-intensive, and increasingly systematized.

xAI pioneered it. OpenAI’s Stargate program is scaling it to 10 GW across the U.S. Meta is deploying four different types of turbines and engines at a single Ohio site because no single manufacturer can deliver enough units on time.

Most BYOG projects are “behind the meter”, meaning the generators sit on the data center’s side of the utility connection. The legal foundation is straightforward: under the Federal Power Act, generating electricity on your own property for your own use doesn’t trigger utility regulation. You’re not selling power. You’re consuming it.

This has inspired some creative workarounds. Companies like Solaris and VoltaGrid operate an “energy-as-a-service” model, so they own the turbines and sell power directly to the data center. To avoid being classified as utilities, they’ve built structures designed to blur the line: equipment leases, managed service agreements, and joint ventures. For instance, xAI’s Stateline Power LLC is structured as 50.1% Solaris, 49.9% xAI… just enough to maintain the designation of self-generation.

The extreme version of this strategy is fully islanded: no grid connection, no utility meter, no interconnection agreement, no transmission charges, no FERC jurisdiction. Legally invisible. Stargate’s Project Frontier in Shackelford County, Texas with 210 Jenbacher engines, 700 MW, entirely off-grid, is the purest expression of this model. Joule Capital’s Utah site runs the same playbook.

Senator Cotton’s DATA Act of 2026 would make this explicit, exempting fully off-grid facilities from federal power regulation entirely.

The Brownfield Play

Buy former industrial facilities that already have heavy power infrastructure.

This is the TeraWulf play. They acquired a former Century Aluminum smelter in Hawesville, Kentucky for $200 million, which came with 250 acres already fitted with 480 MW of existing power, energized substations, and high-voltage transmission lines.

The company also picked up a former 210 MW coal plant in Morgantown, Maryland near Northern Virginia. These are sites that took decades and billions to build. They’re being repurposed in months.

The legal advantage here is different from BYOG. Brownfield sites typically land front-of-the-meter; fully grid-connected, subject to utility tariffs and capacity markets. But the existing connection is the point: the infrastructure is already permitted, already built, already energized.

Bridge to Grid

Use on-site generation as primary power until a permanent grid connection is secured, then transition the turbines to backup.

This is how most developers frame the strategy publicly. Stargate’s Abilene campus runs on-site gas with 29 GE turbines providing nearly a gigawatt of generation while the grid connection scales up.

It’s politically palatable. It’s probably true in some cases. But once turbines are paid off and natural gas stays cheap, the economics of continuing to run them are compelling. Bridges have a way of becoming permanent.

The Machines Behind The Power

Nearly 75% of new power capacity being built for data centers runs on natural gas. The reason is simple: it’s abundant, it’s dispatchable, and critically, it can be deployed now.

The equipment falls into four categories:

Aeroderivative gas turbines, basically modified jet engines, are the workhorse. GE Vernova’s LM6000 produces 43–58 MW, starts in five minutes, and ships 95% factory-assembled. They are sold out through 2028 to 2029, with backlogs approaching 80 GW.

Heavy-frame gas turbines are bigger, slower to deploy, and far more efficient. In combined-cycle configuration, where waste heat drives a secondary steam turbine, they hit ~62% efficiency, the highest of any combustion technology. This is the oil majors’ bet: Chevron’s Power Foundries are built around seven GE Vernova 7HA units, each producing roughly 570 MW. ExxonMobil and NextEra are planning similar combined-cycle plants with integrated carbon capture.

Reciprocating gas engines offer ~50% efficiency and maximum modularity. Wärtsilä’s 50SG reaches full load in two minutes. VoltaGrid’s truck-mounted Jenbacher generators (the ones xAI used first) can arrive on-site and produce power within days. This is the fastest-growing segment.

Solid-oxide fuel cells convert gas to electricity electrochemically. This means no combustion, no NOx, no water, almost no noise. Bloom Energy’s units run at ~60% efficiency and can deploy 100 MW in 120 days. The trade-off is cost: roughly $6,000/kW. But the company has signed deals worth billions with AEP, Brookfield, Oracle, and others, and is doubling manufacturing capacity to 2 GW/year. Its stock is up ~450% in the past year.

Nuclear remains the stated long-term ambition for several projects. For instance, Fermi America has filed NRC applications for four Westinghouse AP1000 reactors at its Texas Panhandle site.. but no reactor is delivering power to a data center today.

But for now, gas is the answer to the question.

The Parallel Power Network Emerging

What began as individual acts of infrastructure desperation is becoming something more structural. A parallel power system.

Williams Companies has committed $5.1 billion to datacenter power generation and reports being “overwhelmed” with developer inquiries. Kinder Morgan has $9.3 billion in pipeline projects, half for power demand. Energy Transfer signed the first-ever direct gas-to-datacenter supply deal.

We’re watching a parallel industrial supply chain building itself in real time.

We’ve been here before

In the 1880s, in the early days of American electrification, any business that wanted electricity had to generate its own. Edison created a separate company just for selling self-contained dynamo systems to factories, hotels, and mills. J.P. Morgan had a personal generator installed in his Madison Avenue basement.

By 1887, Edison’s “isolated plants” far outnumbered his central stations. There were only 103 central stations in operation nationwide, while hundreds of private plants hummed inside individual buildings.

Each factory was an electrical island. Each one needed its own boiler, its own engine, its own engineer. The systems were expensive, redundant, and ran at a fraction of their capacity most of the day. Two-thirds of early electric companies didn’t even offer daytime service. Demand for lighting peaked in the morning and evening, and generators sat idle in between.

It took Samuel Insull to see the inefficiency. Edison’s former private secretary, who had moved to Chicago to run a struggling local utility, realized that the problem was fragmentation. If you consolidated many small loads into one large station, you could run the generators more hours of the day, spread fixed costs across more customers, and drive the price of electricity down far enough to reach not just factories and mansions but ordinary homes.

He called it “massing production” of electricity itself.

By 1907, Insull had merged Chicago’s five competing electric companies into Commonwealth Edison. He pioneered tiered pricing (charging different rates for peak and off-peak usage) and pushed for ever-larger steam turbines that could serve entire districts from a single station.

By 1930, his utilities served 5,000 communities across 32 states, generating a tenth of the nation’s electricity. This became the architecture of American electrification. It brought electricity to homes, farms, and small businesses that could never have afforded their own isolated plant.

A century later, the largest electricity consumers in the country are rebuilding the system Insull replaced. Private generation, behind-the-meter interconnection, islanded microgrids. The engineering is more sophisticated, but the structure is familiar: the wealthiest users building their own supply and stepping outside the shared system.

The obvious case against private power generation is that it fragments a shared system. Fixed costs like transmission maintenance, grid reliability, capacity planning are now spread across fewer ratepayers.

But the obvious case for it is just as strong: if the largest electricity consumers build their own supply, they stop driving up costs for everyone else.

In Virginia, the infrastructure Dominion Energy is building to serve data center demand, like new gas plants, transmission lines, substations, is projected to add $14 to $37 per month to a typical household bill by 2040 in order to cover these new infrastructure costs. If those data centers had generated their own power from the start, much of that buildout wouldn’t be needed.

Prosperity is energy-intensive

Josh T. Smith, who writes the energy policy newsletter Powering Spaceship Earth, frames the case for all of this simply: prosperity is energy-intensive.

This sentiment captures the philosophical core of the private power movement: energy demand isn’t an externality to minimize. It’s a feature of progress.

The optimists are probably right that AI requires enormous amounts of energy, and that building it is a net positive.

The question is whether the way we’re building it —by bifurcating private and shared infrastructure— creates a permanent divide between those who can afford their own grid and those who can’t.

Sources: SemiAnalysis, Cleanview, Grid Strategies, Lawrence Berkeley National Lab, FERC filings, TCEQ permits, company press releases and SEC filings. Data current as of February 2026.

The “SaaSpocalypse” continues, and it’s a bloodbath. This week, it wiped nearly $300 billion in market value off the European stock market.

Professional software and data platforms like RELX and Thomson Reuters suffered double-digit plunges, while professional learning and development giant Pearson saw its shares slide as much as 8%. Sage, an accountancy and payroll software provider, found themselves down 10%.

The catalyst is largely thought to be Anthropic’s release of 11 open-source “agentic” plugins for Claude Cowork. This week, they published a Legal Plugin that automates contract review and NDA triage directly on GitHub. The thesis is that a general-purpose model, when properly configured, can perform high-value professional workflows that companies used to pay millions for via specialized SaaS subscriptions.

This is just the beginning.

I believe the trend is becoming clear: software is going custom (away from one-fits-many) and for many, moving in-house (expect more AI job positions). When an AI agent can read your company’s specific “playbook” and database and execute tasks across your own data, the need for a middleman software provider evaporates.

If a SaaS company doesn’t have a proprietary moat (e.g. around unique, non-public data or a collaboration layer that is impossible to replicate), it is effectively a feature waiting to be absorbed by a plugin.

The Reckoning of Lofty Valuations

For a decade, SaaS valuations were treated as “invincible fortresses” of recurring revenue, powered by the late-stage cycle of the cloud era.

The truth is:

• Use cases aren’t disappearing: Companies still need to review contracts and train employees.

• Value is shifting: The value of buying high-margin software to do these tasks is falling fast

We are in a classic tech cycle, but this one is more brutal than most in its speed. This is the “adapt or die” moment for legacy SaaS. If SaaS companies can’t reinvent themselves as more than just “workflow wrappers,” their valuations will continue to fall off a cliff.

I think that’s an underestimation by orders of magnitude.

I somehow love that it wasn’t a big AI company that showed the way to an agentic future. It was an open source project, Clawdbot, Moltbolt, now called OpenClaw, that closed the last mile in autonomous, agentic experiences.

This weekend, while sitting on a beach with my kids, I built and shipped a dashboard dissecting at SFUSD budgets from my phone. Over the last week itself, I’ve built a private data room (skirting the need for a $65/month Docsend subscription) and started building my own portal for internal Strange Ventures stuff.

I’ve gotten from concept to deployment in mere hours.

Anyone comparing this to how “low code” or “no code” was overhyped and never really adopted by the mainstream… needs to take a long look at themselves.

Yes, the Big Labs Have Swarm Tech. But They’re Keeping It Behind Walls.

The major AI labs have all built multi-agent swarm architectures. None of them are accessible to regular developers or consumers yet.

Kimi K2.5 (Moonshot AI)

• Self-directs an agent swarm of up to 100 sub-agents

• Executes parallel workflows across up to 1,500 coordinated tool calls

• For now: In beta but available if you host your own model.

Gemini Deep Think (Google)

• Google’s first multi-agent model that spawns parallel reasoning agents

• For now: Multi-agents for reasoning, but they aren’t general purpose.

Claude Swarms (Anthropic)

• Instead of a single AI coder, you talk to a team lead that plans, delegates, and coordinates

• Spawns specialists who work in parallel, share a task board, and message each other

• For now: Hidden feature flag estimated to be released soon (hat tip Mike Kelly)

OpenClaw can run on a Raspberry Pi or a Mac Mini or come pre-installed on Molt Boxes (mini PCs) made by enterprising ShenZhen devs. And it’s open source.

Now I’m Exploring: Multi-Agent Teams

In the spirit of “learn by doing,” I’m now starting to build multi-agent teams. These are agent squads or swarms that have persistent memory, work together across a shared database, and have an orchestrator agent that delegates, tests, queues up tasks, and looks for work to do.

To start, I’m running two teams (a peek at the research one below)

Research Team:

• Search agent

• Synthesis agent

• Verification agent

Product Team:

• Development agent

• Data update agent

• Feedback & support agent

• Analytics agent

I found this orchestration writeup by Bhanu Teja helpful!

Next Exploring: Autonomous Multi-Agent Teams

The next step will be building fully autonomous, self-reinforcing agent systems.

For me, that means designing systems where agents can learn, adapt, and coordinate without human intervention. They will monitor their own performance, identify new tasks, and improve their workflows over time.

This is where it gets strange. And where it gets real.

Field Thoughts

1. Single-Player SaaS Becomes Hard to Stomach

Without collaborative elements (e.g. data or other benefits from multi player modes), single-player SaaS feels redundant.

The survivors will have:

• Data moats: proprietary patterns agents can’t replicate

• Network effects: value that compounds with users

• Deep integrations: becoming infrastructure, not application

2. Multi-Agent Teams Are the Future

This will be true for enterprises as well as one-person businesses.

3. Every Business Will Interact Through an Orchestrator Agent via Chat

The interface of the future will be conversation. I don’t do any fancy prompt engineering or complex setups. I talk to my agents as I would to a human.

The orchestrator agent becomes the single point of contact. Routing to billing agents, support agents, research agents, development agents. The human talks to one thing. The one thing coordinates everything else.

4. Security Will Be a Serious Problem as Agents Run Amok

This is the shadow side.

Already happening. Many ahve even injected malware into an OpenClaw skill. Prompt injection can turn your trusted agent into a malicious insider.

Not an ad

Curious but no time to mess around with virtual cloud servers or Mac Minis?

I’m building OpenClaw-as-a-Service for 10 friends, with:

• Secure enclave

• Private phone number (to work with the agent via Whatsapp, iMessage, Slack)

• GitHub integration

• Dev accounts (its own)

• Private servers

If you’re interested in learning more, please jump on the waitlist!

This is wild. Data center infrastructure spending is forecasted to hit $1T in annual spend by 2030.

61% goes to servers. That’s $610B.

Nvidia’s current data center revenue in 2025 is ~$170B.

That’s a $440B shortfall.

So, who fills it?

Does Nvidia scale to meet demand? Or does a $610B market create enough gravity to pull in real competition like custom silicon from hyperscalers, ASICs optimized for inference, architectures we haven’t seen yet?

Source: IOT Analytics

If you’re building in this space, definitely reach out!

Affectionately named Clawd and created by Pete Steinberger, this lobster-themed agent seems to have finally closed the last-mile gap in agentic experiences: user experience.

Yes, setting it up to run 24/7 on a VM was slightly painful (I opted for a cloud server over a Mac Mini. Psst I think there’s a massive opportunity for someone to build a streamlined hosting experience here).

But once I got it running and communicating on WhatsApp, it truly felt like magic.

In minutes, my Clawd (that I named Seb after Ariel’s best buddy):

• Built a daily news scraper for trending papers and news sent to my email and Whatsapp

• Organized my work data into a full portal

• Coded a cost-monitoring dashboard and deployed it to GitHub

I’ve tried all the general-purpose agents out there (including Manus, GenSpark, and Gemini), and none have come close to reliably executing chains of commands (especially recurring ones) as well as Mr. Lobster.

Most frustratingly, while Gemini now does recurring tasks, it’s unable to send emails or update spreadsheets, even within the Google workspace ecosystem.

Clawd functions less like a chatbot and truly like a personal assistant for your life. It runs 24/7 on a VM (or Mac Mini), stays completely synced across a Web UI and WhatsApp/Telegram/Slack, and most crucially, maintains a persistent state.

It’s even able to solve captchas and help make phone calls on your behalf.

There’s way more to come. Some users have already set it up with its own email, GitHub accounts, and are even handing over credit cards.

The last mile era of autonomous agents is finally here. It’s a big, delightful glimpse into the future. Have fun! And of course, be safe.

🛠️ Quick Sidebar: The Clawd Setup Guide

Getting a 24/7 agent running isn’t plug-and-play yet, but the payoff is massive. Here is the quick-start guide to doing it right, curated from the power users on X.

1. The Host (Don’t just run this on your laptop)

• VPS Path: Rent a cheap cloud server (Hetzner or DigitalOcean or Google).

• Local Path: An always-on Mac Mini if you want it to control local apps.

2. The Identity

• Get a Second Number: Get a secondary phone number for WhatsApp. Or if you want to initially try it out, Clawd will be in self-message mode on WhatsApp. Grab a cheap eSIM for your agent’s “Work Phone.”

3. Models / Web Search

• Claude Opus: Still the king for the actual coding/analysis tasks. I use Sonnet when tackling most daily and routine tasks and questions (to avoid a huge bill!)

• Use Grok for X Search: While you can plug in various models, power users suggest routing the “Search” skill through Grok (via API) for the most up-to-date real-time news retrieval.

4. The Community

• Join the Discord: Nearly 20,000 and counting 🦞

• ClawdHub: for the latest Skills

I’ve cancelled more software subscriptions this past year than ever before. Same story in my group chats. This feels like a canary in a coal mine: according to this BetterCloud report, 90% of enterprises now say software consolidation is a priority.

It makes sense. Why piece together hundreds of software subscriptions when AI can help pull from a unified database and build custom apps and workflows that fits you and your own?

This is a massive market to disrupt. Enterprises in the US spend over $8,700 annually per employee, with a staggering 51% of licenses that goes unused.

We’re starting to see the first decline of SaaS subscriptions in a decade, with an average of 18% decline for enterprises, and 28% decline for mid-sized businesses.

As a venture investor, the question I keep coming back to: what happens to the massive valuations handed out during SaaS’s heyday?

The “unicorn backlog” estimates 412 enterprise startups valued at over $1b that are still private, and haven’t raised any capital in 18 months. Around half of the $5B+ unicorns (106 of them) carry peak market valuations from 2021 and 2022.

This could be the killer feature that wins the LLM wars.

Yesterday, Google rolled out “Personal Intelligence” for Gemini Pro and Ultra users in the US. The feature (which users have to opt in to) allows Gemini to reason across your entire Google ecosystem (Gmail, Photos, YouTube, Search history) to connect the dots that other models can’t see.

Example: You ask about winter tires. Instead of generic recommendations, Gemini finds your car model from an old Gmail receipt, checks your Photos for the tire rim size, then searches the web for the best winter tires for that specific setup.

This is Life OS. The holy grail of all tech apps. And for a good number of those who are on Google Workspace, it’s Work+Life OS.

Just as Google indexed the open web, they can now index your personal information history (through “context packing”) to make it relevant, contextual, and useful. There might feel be some discomfort at first at the “creepiness” of the idea, but if history tells us anything, extreme convenience will likely win out.

This turns decades of Google’s data infrastructure into an unassailable advantage.

Paired with Universal Commerce Protocol, this feels like the Google universe upgrading from managing your information to managing your decisions. From suggesting the right tires to buying the right tire you need.

Why This Matters

The LLM wars may not be won by the model with the highest benchmark scores. They may be won by the model that knows you best.

Google just made that case. And they’re the only ones who can.

Next, I anticipate that Google will aggressively make moves to build out their developer and enterprise partnerships on top of Gemini / Personal Intelligence / Work + Life OS.

If they open the gates to the TPU/Gemini stack for builders to create "Context Apps" on top of this personal data, the lock-in becomes permanent.

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The Download

News that mattered this week

1. SoftBank and OpenAI commit $1bn to SB Energy for US AI data centre push: SoftBank Group and OpenAI are making a significant bet on the future of AI infrastructure, committing a combined $1bn investment into SB Energy as they partner to develop next-generation AI data centres in the US. The investment builds upon a much broader, previously pledged $500bn commitment made public at the White House earlier this year.

2. Mark Zuckerberg says Meta is launching its own AI infrastructure initiative: Meta CEO Mark Zuckerberg announced the launch of Meta Compute, a new initiative designed to bolster the tech giant’s AI infrastructure. Zuckerberg said the company intended to drastically expand its energy footprint in the coming years.

3. Anthropic launches Cowork, a Claude Desktop agent that works in your files – no coding required: Anthropic released Cowork, a new AI agent capability that extends the power of its wildly successful Claude Code tool to non-technical users.

4. Elon Musk’s xAI partners with Pentagon for Grok AI military integration: Elon Musk’s artificial intelligence venture, xAI, has secured a pivotal agreement with the U.S. Department of War, marking a significant expansion of AI’s role in military operations. This deal integrates xAI’s Grok models into the Pentagon’s GenAI.mil platform, enabling real-time data analysis and enhanced decision-making for defense personnel.

New data from Bank for International Settlements (BIS) shows something interesting: the companies driving AI infrastructure (Alphabet, Amazon, Meta, Microsoft, Oracle) have historically run lean on debt. They funded growth from cash flow.

That's changing. Capex is now outpacing free cash flow. They're borrowing.
Private credit to AI firms went from near-zero to $200B. By 2030, it could hit $600B.

Source: https://www.bis.org/publ/bisbull120.pdf

Executives from Big Tech are reportedly camping out in hotels near Korean fabs, waiting for allocation meetings. Industry insiders, somewhat derogatorily, call them “DRAM beggars” (first reported in Chosun Ilbo).

What happened here?

The shortage has a simple, brutal root cause: to build the High Bandwidth Memory (HBM) that powers Nvidia GPUs, manufacturers sacrifice massive amounts of wafer real estate.

HBM chips are larger and have lower manufacturing yields. Producing 1 GB of HBM effectively consumes the wafer space of 3 GB of standard DDR5.

Samsung, SK Hynix, and Micron (who together control 95% of global DRAM revenue)made a rational choice. HBM margins are enormous. Hyperscaler demand is insatiable. So they pivoted hard, and in doing so, they deleted the capacity needed to make memory for everything else.

And Micron, America’s champion and the world’s third-largest DRAM maker? Their fabs are fully booked too. They’ve made the same pivot to HBM.

The AI economy runs on finite resources: silicon, power, water, labor. When one use case can outbid the rest of the economy, something has to give.

We might see the effects cascade:

• Consumer lag: The laptop you see on shelves today was built with memory bought six months ago. The real pain hits this year. Major OEMs have warned of 15-20% price hikes. Mid-range smartphones are quietly downgrading to 8GB to mask the inflation.

• The cloud tax: For the first time in a decade, the raw cost of compute infrastructure is rising. As memory costs flow through to hyperscalers, we might expect that burden to land downstream to cloud customers.

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We cannot build new factories fast enough to fix 2026.

As Western and Korean makers abandon the low end to chase AI margins, China’s CXMT (ChangXin Memory Technologies) is the wildcard. They briefly flooded the market with cheap DDR4 in 2024, then pivoted to DDR5 under government direction (to focus on next-gen instead of legacy tech), actually worsening the current shortage. But the long-term trajectory is clear: Chinese fabs will fill the gap Western suppliers are abandoning.

We are entering a world with two distinct memory markets: a high-end, expensive, Western-aligned tier (HBM/DDR5) for AI infrastructure, and a low-end, commoditized market supplied by China for everything else. Your next smart fridge might run on Chinese chips. Because they’re the only ones available under $10.

The Question

The memory tax is a preview of what happens when a single use case: AI, can outbid the rest of the economy for atoms.

Every wafer allocated to an HBM stack for an Nvidia GPU is a wafer denied to the LPDDR5X module in a mid-range smartphone or the SSD in a consumer laptop. This is a zero-sum game at the silicon level.

And the harder question: What else gets crowded out next? Power. Water for cooling. Skilled labor. The AI boom is not happening in a vacuum. It is pulling physical, finite resources away from other uses.

The deflationary AI future may still arrive. But the path there runs through a lot of inflation first.