Shahar (🛰️,🎹)

Increasingly becoming evident that the answers lie in the study of the human brain and nature of sapience/consciousness rather than the algorithms we have made to work for silicon. The goal is an imitation of intelligence, not a vague approximation.

@axioniclabs research is being published at ICANN 2026. Our research on post-transformer alternatives will be highlighted in the main conference from 14–17 September at ICANN, held at University of Padua ResonatorLM explores an alternative to self-attention for long-context sequence modelling, using learned resonators to build a more efficient recurrent architecture. At @axioniclabs, we work on new model architectures and learning systems that challenge the assumptions behind today's AI stack. ICANN brings together researchers working on the foundations of neural networks. That is the hardest audience this work could have, which is exactly why we want it in front of them. Come talk to us if you're working on post-transformer architectures, continual learning or adaptive AI models.

Today, we raised a $13M series A to solve the GPU shortage Trillions are being spent on GPU CapEx while 80% of it sits idle The capacity already exists Virtualizing GPUs unlocks it Backed by Matrix, YC, and CEAS

We recently announced a new paper, centered around a post-transformer architecture designed for language modeling. A 🧵follows:

I think the days of companies relying only on frontier models are coming to an end. 1. Tokenmaxxing at frontier-model API prices makes no sense. Uber burned through its entire 2026 AI budget in 4 months, Microsoft moved engineers off Claude Code due to cost, and companies are realizing that running everything on frontier models can get expensive fast. Tokenmaxxing makes sense when you’re on a subsidized $200/month plan, but it’s unsustainable at API rates. 2. Companies will rely on a portfolio of models. Coinbase recently cut its AI spend nearly in half by switching engineers to Chinese open-source models like GLM and Kimi. Airbnb and Pinterest have done the same with Alibaba’s Qwen models. I believe this will be the default path forward: using frontier models for high-stakes work and cheaper models for everything else. 3. China’s open-source strategy is working. Chinese models are taking market share from frontier models at US companies. China is also building the full AI stack, from energy, like solar and nuclear, to data centers to domestic chips. The Chinese government is planning a $295B investment in AI data centers, with at least 80% of chips built domestically. 4. Frontier labs are in a Catch-22 situation. If they release great open-source models, they might undercut their own frontier API revenue. If they gate the best models behind a trusted list, companies will just lean on open alternatives more. The last major US open-source model was OpenAI’s gpt-oss series back in August 2025, which already feels like decades ago in the AI space. 5. The US needs to think about its AI strategy holistically. I believe restricting access to frontier models will only hurt American innovation. Banning US companies from using Chinese models won’t work either. Just look at how China took over the global electric vehicle market. To maintain our edge, we need the best closed and open models while scaling our energy grid and data centers much faster. 📌 More in my recent essay: creatoreconomy.so/p/18-hot-t…

Legacy Media types are calling this Alex Karp interview a “crash-out” so that’s your first clue that he is actually saying something extremely insightful. He is articulating what real “AI safety” looks like in the enterprise. Not abstract alignment research or certification by a government-run DMV for AI. Real AI safety for businesses is the ability to control their own data, model weights, and compute — so a frontier lab can’t hoover up their proprietary knowledge and turn it into their next product. As Karp explains, technical customers want “control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it’s not being transferred to someone else.” Don’t think that can happen? Just look at Figma. According to The Information, Anthropic “blindsided” its then-business partner with the launch of Claude Design. Figma’s founder said Anthropic had not been “consistently honest” with them. Anthropic’s chief product officer had even served on Figma’s board until three days before the launch of Claude Design. Figma’s stock has fallen sharply this year while Anthropic’s valuation has surged. This isn’t an isolated example. Anthropic has launched Claude Science, Claude Security, Claude Legal, and of course Claude Code — each expanding into categories previously served by companies building on top of their models. The pattern is consistent: watch where value is being created, then move in directly. Dominate the model layer, then use that position to capture the most lucrative verticals. Dario has argued that open source models powerful enough to compete with Anthropic are “dangerous.” But dangerous to whom? Not to enterprises that want to retain control over their data and workflows. Dangerous to a business model that benefits from customers having few real alternatives at the model layer. As Karp exposes, true enterprise safety isn’t trusting that a lab’s future roadmap won’t include your business. It’s retaining the ability to choose — at the model layer — who gets to see and use your alpha.

Sam Altman walked into YC, offering founders $1M in free tokens for a slice of equity. Jason's advice: run. OpenAI is watching your token usage, ranking the winners, then shipping the top 5 as native features. Get off the frontier models, use open source, and own your data. @Jason

Our Anthropic overlords deciding which prompts the peasants are allowed to use.

Totally agree and wrote this article on it last night trust-us.vercel.app/?s=1

POV: you're still using GitHub Copilot after June 1st, 2026