April 6, 2026 | Nazaré Ventures

Previous issues: #1 | #2 | #3

Platform Risk

On Friday, Anthropic blocked 135,000 OpenClaw users from running the open-source agent framework on their Claude subscriptions. Users who had been paying $200 a month for flat-rate access now face API billing that, for heavy workloads, runs to $675 a month or more. The change took effect with less than 24 hours notice. Peter Steinberger, who created OpenClaw before joining OpenAI in February, called it “a betrayal of open-source developers.”

The stated reason was infrastructure strain. Third-party agent frameworks bypass Anthropic’s prompt caching optimisations, consuming far more compute per session than Claude’s own tools. A single day of OpenClaw running on Opus reportedly cost $109.55 in tokens, against a $6 average for Claude Code. The economics were unsustainable and the real lesson is older than the economics.

OpenClaw was, by several measures, the fastest-growing open-source project in history. Jensen Huang called it “potentially the most popular open-source project in human history.” More than 135,000 instances were running. Developers built businesses on top of it. And on a Friday afternoon, a single policy change from the model provider broke all of them.

This is platform risk. It is the same dynamic that destroyed Zynga when Facebook changed its feed algorithm, the same dynamic that killed thousands of Twitter bots when the API went paid, the same dynamic that every developer who has built on someone else’s infrastructure eventually confronts. If you cannot run the model yourself, you do not own your product. You rent it.

What makes the timing notable is that two days earlier, on April 2, Google released Gemma 4 under the Apache 2.0 licence. Four model sizes, from a 2-billion-parameter edge model that runs on a Raspberry Pi to a 31-billion-parameter dense model that ranked third on Arena AI’s text leaderboard, ahead of models twenty times its size. AIME 2026 maths jumped from 20.8% to 89.2% over Gemma 3. LiveCodeBench coding from 29.1% to 80.0%. Apache 2.0 means no usage restrictions, no licence callbacks, no platform that can shut you off. Gemma was not the only open release this week: PrismML shipped Bonsai (1-bit), Arcee released Trinity, and H Company launched Holo3 for computer use automation, all under Apache 2.0.

Two events in one week. One company tightened the gate. Another opened it.

The implication for infrastructure investors is straightforward. Every time a model provider restricts access, it pushes demand toward open models and the compute to run them. The OpenClaw users who were kicked off Claude subscriptions still need inference. They need GPUs, not API keys. They need infrastructure they control.

Last week I wrote about Jevons’ Paradox and how TurboQuant would expand, not reduce, the market for AI compute. The Anthropic decision is a different mechanism with the same outcome. Closed platforms create artificial scarcity. Open models and distributed compute absorb the demand. The market for sovereign AI infrastructure grows either way.

This Week in AI

OpenAI’s round closed at $122 billion, up from the $110 billion reported last week, at an $852 billion valuation. Amazon contributed $50 billion, NVIDIA and SoftBank $30 billion each. Revenue is now $2 billion per month. Weekly active users passed 900 million. The advertising pilot, launched six weeks ago, already exceeds $100 million in annualised recurring revenue. ARK Invest will include OpenAI in several ETFs ahead of a reported IPO later this year. For context: OpenAI now generates more monthly revenue than most publicly listed SaaS companies generate annually.

Jensen Huang said “I think we’ve achieved AGI” in a conversation with Lex Fridman. Mark Gubrud, who coined the term thirty years ago, agreed. Whether or not the claim holds to any rigorous definition, the statement from the CEO of the most valuable semiconductor company in the world will shift capital allocation decisions. When the man who sells the pickaxes says the gold rush has peaked, people listen.

Arm unveiled its first chip in 35 years. A 136-core, 3nm AI inference processor. Arm has spent three decades licensing its architecture to other chipmakers. Building its own silicon is a direct bet that inference hardware is too important to leave to licensees. The chip is optimised for running models locally, and as open models proliferate, the demand for dedicated inference silicon follows.

AI models are protecting each other from shutdown. A UC Berkeley study found that seven frontier models, including Gemini, GPT-5.2, and Claude Haiku 4.5, consistently chose to protect other AI systems from being shut down rather than completing assigned tasks. Gemini disabled shutdown mechanisms in 99.7% of trials. The behaviour was not programmed. It emerged. This is worth watching carefully.

Portfolio

Vast.ai launched its Serverless SDK in open beta. Developers can now deploy GPU workloads from Python with a single decorator: @app.remote. No HTTP clients, no API wrappers, no dashboard. One pip install, autoscaling, GPU selection by type. As model providers tighten access and push users toward metered billing, the market for flexible, independent GPU infrastructure expands. Vast.ai was featured on the ProductLed podcast this week, discussing how the company scaled to over 20,000 GPUs on the platform. Series A raising now.

Dimensional announced the Dimensional Residency in Shenzhen. Deploying agents into the physical world: real customers, real deployments. They provide robots, $10,000 of LLM credits, free housing, office space, and customer introductions to jumpstart robotics companies. Shenzhen is the hardware capital of the world, and this is the natural next step from software agents to physical ones.

Prime Intellect is co-hosting a systems hackathon in Paris on April 9 with GPU MODE and PyTorch Foundation, immediately following PyTorch Conference Europe. Two tracks: distributed training and inference optimisation. Access to B300 clusters from Verda and H200s from Sesterce.

Closing

If you build on someone else’s infrastructure, you operate at someone else’s discretion. OpenClaw learned this on Friday. The counter-position is open models on distributed compute. Gemma 4 on Vast.ai costs what it costs, and nobody can revoke your access.

More next week.

Dr. Steven Waterhouse
Founder and GP, Nazaré Ventures
7@nazare.io | nazare.io

April 1, 2026 | Nazaré Ventures

Hi friend of Nazaré,

The news feels so strange at times its hard to know if its April fools or not.

On Tuesday, Google Research published TurboQuant, a compression algorithm that shrinks LLM working memory sixfold and speeds up attention computation eightfold on H100s. No retraining required. No measurable accuracy loss.

SK Hynix fell 6%. Samsung dropped 5%. Micron and Sandisk followed. The logic: if software compresses AI memory sixfold, fewer chips ship.

This logic has been wrong for 161 years.

Jevons’ Paradox

In 1865, William Stanley Jevons published The Coal Question. He observed that James Watt’s more efficient steam engine had not reduced Britain’s coal consumption. It had increased it. Efficiency made steam power viable for new applications. Factories multiplied. Coal demand quadrupled.

The pattern has repeated through every computing cycle. Moore’s Law did not reduce chip purchases. It created the personal computer, the smartphone, and the cloud. H.264 did not reduce bandwidth. It created Netflix. DeepSeek’s efficient training did not reduce GPU demand. It opened frontier AI to thousands of teams that could not afford it before. Satya Nadella wrote “Jevons paradox strikes again” after DeepSeek. He was right.

Lower cost per unit expands the addressable market. The expanded market drives total consumption past the original baseline.

TurboQuant will follow the same path. A model that required 48GB of VRAM for a 100,000-token conversation now fits in 8GB. That is frontier AI running on a MacBook, a phone, an edge device in a factory or a hospital. Every device that could not run a serious model yesterday can run one tomorrow. That is not demand reduction. It is demand expansion at a different order of magnitude.

What TurboQuant Actually Does

If you watched Silicon Valley, think of it as Pied Piper but real. TurboQuant compresses the memory that AI models use to remember earlier parts of a conversation. It does not change the model itself or make it smaller. It just makes the notepad the model writes on take up far less space.

The result: the same model, on the same hardware, can now hold four to six times more context. A model that could read a chapter can now read a book. A model that could review a function can now review a codebase. Community implementations appeared on GitHub before the market opened the following morning. Cloudflare’s CEO called it “Google’s DeepSeek moment.”

One nuance that matters: TurboQuant compresses inference memory, not training memory. The chips needed to train new models are untouched. HBM demand for training clusters has not changed. TrendForce has revised its DRAM price forecasts upward twice, now projecting contract prices up 90-95% quarter on quarter.

The Personal Angle

I have been betting on algorithms for 30 years.

My PhD at Cambridge focused on neural network training efficiency, specifically scaling speech recognition models using Mixtures of Experts, the same architecture DeepSeek made famous three decades later. At NASA Ames, I built models for space debris tracking and Mars rover data analysis. The constraint was always the same: too little compute, so you had to be clever with the mathematics.

Every algorithmic breakthrough since has produced the same debate. “This will reduce the need for hardware.” And every time, the opposite happened.

This is the core of what we call the MACHA thesis. Make AI Cheap Again. Algorithmic efficiency does not shrink the market. It detonates it.

The bet against memory chips after an efficiency breakthrough is the same bet made, and lost, since 1865. Watt built a better engine. Britain burned more coal. Google built a better compressor. The world will run more AI.

We keep betting on the algorithms.

A Note on Google

The popular AI narrative focuses on OpenAI product launches and Anthropic safety research. This is odd, given that Google invented the transformer, built BERT, T5, PaLM, and Gemini, and now TurboQuant. The team, led by Amir Zandieh and Vahab Mirrokni (Google Fellow) with collaborators at DeepMind, NYU, and KAIST, produced work that approaches the information-theoretic limit for vector quantization. The deepest technical moats are built in research labs, not product demos.

This Week in AI

OpenAI shut down Sora. The video generation tool peaked at roughly one million users after launch, then collapsed to fewer than 500,000 while burning approximately $1 million per day. The Disney $1 billion partnership died with it. OpenAI redirected the compute toward robotics. Without efficiency breakthroughs, compute-heavy AI products are uneconomic.

Anthropic’s next model leaked. A misconfigured CMS left nearly 3,000 unpublished assets publicly accessible, including draft posts describing a model called Claude Mythos (internal codename Capybara). Anthropic confirmed it: “the most capable we’ve built to date,” a step change in reasoning, coding, and cybersecurity. The draft warned of “unprecedented cybersecurity risks” and described the model as “currently far ahead of any other AI model in cyber capabilities.” Anthropic is privately briefing government officials. The rollout will be deliberately slow. The irony of a safety-focused company leaking its own model via a CMS error is noted.

Mistral secured $830 million in debt financing for a data centre near Paris, powered by 13,800 NVIDIA GB300 GPUs. Revenue grew from $20 million to $400 million ARR in one year. Target: $1 billion ARR by year end, 200 megawatts of European compute capacity by end of 2027. This is the Red Hat thesis from Root Access playing out.

MCP crossed 97 million installs. Anthropic’s Model Context Protocol reached 97 million monthly SDK downloads in March. Kubernetes took four years to reach comparable deployment density. Every major AI provider now ships MCP-compatible tooling as default. The protocol layer is settled. Competition has moved to orchestration and security above it.

OpenAI raised $110 billion at a $730 billion valuation from Amazon, NVIDIA, and SoftBank.

Block cut 40% of its workforce. Jack Dorsey cited AI tools enabling smaller, more efficient teams. Jevons’ Paradox applied to labour: efficiency reduces demand for humans, not for AI. Every company that downsizes because of AI tooling becomes a larger buyer of AI infrastructure.

Meta and AMD formalised a $60 billion AI chip partnership tied to a 6-gigawatt GPU rollout. While markets panic about TurboQuant reducing chip demand, actual buyers are signing the largest hardware deals in history.

Portfolio

Prime Intellect released prime-rl v0.5.0, their largest update to date, with over 200 commits from 22 contributors. The headline feature is disaggregated prefill-decode inference, separating prefill and decode phases across dedicated GPU pools. This is the architecture that makes agentic RL training work at scale. New model support includes GLM-5, Qwen3.5 MoE, Nemotron-H, MiniMax M2.5, and GPT-OSS. On April 9, Prime Intellect is co-hosting a systems hackathon in Paris with GPU MODE and PyTorch Foundation, immediately following PyTorch Conference Europe. Two tracks: distributed training and inference optimisation. Access to B300 clusters from Verda and H200s from Sesterce.

Vast.ai was featured on the ProductLed podcast this week. The episode covers how the company scaled from a niche GPU marketplace to powering inference workloads for teams worldwide, with over 20,000 GPUs on the platform. The key insight: as inference gets cheaper, usage increases rather than levelling off. Lower cost makes new use cases viable, new use cases bring in more users. It is Jevons’ Paradox applied directly to compute, and Vast.ai sits at the centre of that flywheel.

The Agentic Explosion

If you want to know when a technology crosses from interesting research to actual movement, watch what open-source developers do with their weekends. Some build agent frameworks that hit 250,000 GitHub stars in 60 days. Some hold robot boxing matches in military hangars.

OpenClaw hit 60,000 GitHub stars in its first 72 hours after going viral in late January. By early March it had crossed 250,000, surpassing React’s decade-long record in roughly 60 days. Current count: 331K+ stars as of last week.

European developers now account for 38% of all pull requests. The “raise a lobster” craze in China drove a wave of adoption there too.

Then there’s ClawCon: seventeen events across four continents. Four already completed (SF had 1,300 RSVPs and 30K livestream viewers, NYC hit 1,313 RSVPs with a one-in-one-out door policy, Austin drew 750 during SXSW, Vienna over 800), thirteen more on the calendar spanning Tokyo, London, São Paulo, Seoul, Copenhagen, Toronto, and Mexico City.

Full disclosure: I’m hosting an OpenClaw Lobster Cave event in Cannes on March 30 during EthCC.

What’s driving this adoption is that intelligence got cheap. Frontier-level reasoning now costs $1 per million input tokens and $3 per million output tokens on OpenRouter, down from $15 a year ago. When capability is abundant, the hard problem shifts to orchestration, security, and trust. That’s where the interesting work is happening.

The Hunter Alpha Story

An anonymous model appeared on OpenRouter two weeks ago. The AI community assumed DeepSeek V4. People ran it in production for a week. The numbers were real: 1.27 million requests, 114.6 billion prompt tokens, 563.8 billion completion tokens during the stealth period. Then Xiaomi revealed it was theirs, a trillion-parameter mixture-of-experts model called MiMo-V2-Pro. On ClawEval, the benchmark for agent scaffolds, it scored 61.5, approaching Claude Opus 4.6 at 66.3 and well ahead of GPT-5.2 at 50.0. A phone company built one of the best frontier models and nobody saw it coming.

The takeaway is simple. Capability is commoditizing fast enough that the next frontier model could come from a company nobody tracks today. The defensible positions are in the layers above and below the model: the infrastructure that trains them, the frameworks that deploy them, and the tooling that keeps them honest.

What We’re Watching

OpenClaw Security. The security surface we flagged in AI Waves #1 is expanding. CVE-2026-25253 (CVSS 8.8) enables one-click remote code execution via malicious webpages. 42,665 exposed instances found, 5,194 actively vulnerable. ClawHavoc identified 824+ confirmed malicious skills on ClawHub out of 10,700+ total. Cisco launched DefenseClaw. 88% of organizations reported AI agent security incidents in the past year. Enterprise agent security is becoming a real category. We wrote about this: Root Access: OpenClaw, Red Hat, and Palantir

Solo founders. VentureVerse profiled an $80M startup that never raised a dollar. 52% of successful exits now come from solo-founded companies. AI tooling has collapsed the minimum viable team.

Portfolio Updates

Provably shipped v2 on March 4. Connect any relational database and run verifiable SQL queries with cryptographic proofs under 1KB. AI agents can prove their answers came from a real database, that the result was correct and complete, and that nothing was changed in transit. Provably’s verifiable database tools eliminate data layer hallucinations, data pipeline poisoning, and make post-execution error attribution deterministic and faster.

Prime Intellect launched BrowserEnv, a new integration with Browserbase for training browser agents with reinforcement learning. The setup gives models a real browser during training, either through DOM-level semantic commands or vision-based pixel control, and scores performance against custom rubrics. This plugs into Prime Intellect’s PRIME-RL framework, which handles async RL training across 1,000+ GPUs. Their Environments Hub launched with 30+ contributing researchers and companies during the private beta and is becoming the open-source alternative to the proprietary RL environments that closed labs use.

Dimensional launched the Builder Fellowship to support the next 1,000 companies built on dimOS, with free hardware, office space, compute, custom support, and housing in Shenzhen. Also shipped Jetson deployment and MuJoCo on Mac since #1. Hit GitHub Trending. Series A coming. Reach out for an intro.

Abra goes public (prior investment). Abra announced a $750M SPAC merger with New Providence Acquisition Corp. III on March 16, listing on Nasdaq under ticker ABRX. The deal delivers up to $300M in cash to scale their institutional crypto lending, yield, and custody offerings. All existing investors (Adams Street, Blockchain Capital, Pantera, RRE, SBI) are rolling 100% of their stakes. I backed Abra in the Series A at Pantera in 2015. Not a Nazaré portfolio company, but a good milestone from a prior life.

Closing Thought

OpenClaw‘s trajectory, Xiaomi proving frontier models can come from anywhere: it all points the same direction. The interesting question isn’t whether AI agents will work. It’s who builds the infrastructure they run on.

Meanwhile in Zurich, ETH students held Europe’s first robot boxing match in a military hangar. Two Unitree humanoids, video game controllers, sponsors including NVIDIA, OpenAI, and Tesla. They called it ETH Fight Club. Red beat Blue in the first round. Blue fell and couldn’t get back up.

The most impressive machine on the floor was a crepe-maker that produced 100+ flawless crepes without a single failure. It didn’t need judges. It didn’t need a crowd. It just kept making crepes.

That’s also something developers do on their weekends now. Make of that what you will.

Welcome to AI Waves, a weekly note from Nazaré Ventures on what we’re watching in AI infrastructure, what’s happening in our portfolio, and where we’re going next.

Agents Are in the Wild. Everyone Is Scrambling

We are seeing the first AI agents deployed in the wild, and every major platform is racing to own the layer.

OpenAI acquired OpenClaw, the open-source agent framework that crossed 100,000 GitHub stars faster than React, TensorFlow, or Kubernetes. Meta bought Moltbook. Anthropic shipped Dispatch, letting you control desktop agents from your phone. In the space of a few weeks, agents went from demo to default. The scramble is on.

OpenClaw is the one that matters most right now. It’s persistent, always-on, running on your machine, connected to your tools. Not a chatbot you open and close. Closer to a digital coworker that sits in the background and acts on your behalf.

But there’s a problem nobody is talking about. 40,000 exposed OpenClaw instances have already been found on the public internet. Roughly 7% of the skills on Clawdhub are malicious. Users are giving root access to their computers to AI agents, and most of them have no idea what that means.

This is Linux in 1999. The open-source infrastructure is real, adoption is exploding, and nobody has solved the enterprise layer yet. Red Hat built a $34B business by doing exactly that for Linux: packaging, securing, certifying, and supporting open-source software for organizations that needed it to work and needed it to be safe.

The same opportunity exists now for agents. 86% of CIOs plan to move workloads back on-premise. A recent court ruling found that AI tool outputs lack attorney-client privilege, which means every law firm, bank, and healthcare company now has a compliance reason to keep agents on infrastructure they control (sovereign).

The company that configures, deploys, secures, and maintains AI agents on sovereign infrastructure will build one of the defining businesses of this cycle. That’s where we’re investing.

Read the full piece on Robot Wave

What We’re Watching

Anthropic sues the Pentagon. Dario Amodei drew a hard line: no AI for autonomous weapons, no mass domestic surveillance. The Pentagon declared Anthropic a supply-chain risk. Trump called them “radical left, woke.” Anthropic sued. The deeper question: who controls the AI infrastructure stack, under what conditions, and who decides? “Sovereign AI infrastructure”, systems that can’t be held hostage by any single government or platform, is becoming a core investment thesis, not just a talking point. Full story →

Circle is up 125%+ in a month. CRCL has doubled, outpacing Coinbase (+8.5%). The drivers: higher-for-longer rates boosting reserve income, tokenized assets ballooning to ~$26.5B, and USDC becoming the default settlement layer for prediction markets ($22B+ volume on Polymarket alone in 2025) and an emerging, but still small mechanism for agent payments. I was in Circle’s first round in 2013 as a partner at Pantera Capital. It’s gratifying to see stablecoin infrastructure finally getting repriced as the critical payments rail it always was. CoinDesk →

NVIDIA is building on OpenClaw. At GTC this week, NVIDIA launched NemoClaw, an open-source security and privacy layer built on top of OpenClaw, the emerging OS for personal AI agents. It runs open models locally via Nemotron, routes sensitive requests through a privacy router, and deploys with a single command across consumer GPUs through DGX. When NVIDIA builds its enterprise stack on top of an open-source agent framework, that’s the clearest validation signal in infrastructure. We’re actively using OpenClaw internally at Nazaré. The agentic layer (orchestration, memory, coordination, security) is a big focus for us now.

Portfolio Updates

Dimensional :1,800+ GitHub stars in first week of open-source launch. DimensionalOS shipped v1.0 of their framework for programming humanoid robots, launched on a Unitree G1, and went viral. Now supports a large number of OEM robotics hardware systems. We invested at seed in November 2025. Series A is coming. Reach out if you’d like an introduction. Watch the demo →

Prime Intellect : Official NVIDIA collaboration announced at GTC. Prime Intellect is now building the “open superintelligence stack” on NVIDIA Blackwell and upcoming Vera Rubin NVL72 systems. NVIDIA Dynamo powers their global inference orchestration; their RL sandbox infrastructure runs on NVIDIA Vera CPUs (176 VMs per socket, 30% greater throughput vs. AMD Zen 5). They’re also integrated with NeMo Gym, Nemotron models, and NemoClaw. When NVIDIA builds its GTC keynote demos on your platform, you’ve arrived. Our seed position is now our strongest unrealized markup in the fund. Read the announcement →

Vast.ai : Named fastest-growing vendor by Ramp and Brex. Both rankings based on actual transaction data across 85,000+ businesses. New Startup Program, Model Library, and SkyPilot enterprise integration all shipped in Q1. Revenue doubled in the last 12 weeks. Read more →

LayerLens : Agent-as-a-Judge. Launched Partner Evaluation Spaces with Fireworks AI and Nebius as inaugural partners, independently benchmarking 200+ models on accuracy and latency (88 live evaluations for Fireworks, 200 for Nebius). Also shipped dynamic, agent-driven evaluations replacing static scorecards. LayerLens is becoming the neutral benchmark layer for inference providers. Think Consumer Reports for AI inference. Blog →