Given token economics, we really need @apple’s new ceo to go all in on workstations that can run local, open source models Ideally, with a router that can flip between local models and frontier models when the former gets stuck. And America needs an open source champion — we really should not be comfortable with the Chinese owning the open source LLM market to the extent they do

AI agents running fully on local devices? Yes, really. Join @Stanford researchers @JonSaadFalcon and @Avanika15 for a look at OpenJarvis, an open-source framework pushing personal AI forward with local-first performance and efficiency. Plus, hear from @UCLA’s Jason Cong, a pioneer in domain-specific computing and AI systems research! The Developer Track at Advancing AI is stacked with sessions built for developers. Don’t miss it. Register now: amd.com/en/corporate/events/… #AdvancingAI #AMDevs

Now that we have Fable… should AI models be training us? @Avanika15’s and my mini-experiment: Can we turn Fable into your smartest partner in health and fitness? Fable is borderline *too smart* for this in how good it is at deep research, synthesis, reasoning. But the world’s best fitness trainer would probably also have… - Intake → personalization. Asks all about you, generates tailored workout plans. - Deep customization. Tell it who *you* trust for fitness advice. It draws from those sources. - Rich formats. Shows you lots of videos, not just text. We gave it a shot! Link below, all feedback welcome.

Good take My guess is - demand for intelligence is near infinite - but 80% of workloads will be running on 99% cheaper models within 12-18 months - 20% of workloads will still run on latest gen models where IQ maxing is important (scientific breakthroughs, higher level ochestrator agents?) - rough analogy might be what % of macbooks or gaming PCs sold have the maxed out specs for CPU/GPU, prices are falling much faster than Moore's law here though - this leads me to think the limiting factor will be energy and compute, not better models At Coinbase we're working hard on routing prompts to cheaper models where appropriate, and in some cases have been able to keep costs roughly flat, while token usage continues to grow exponentially.

My latest with @Avanika15 and @JonSaadFalcon. If America fails to create a level playing field for its open-weight ecosystem, it will cede a large portion of the AI future to China. We can and should act now.

“The next phase of the U.S.-China AI competition will be in no small part decided by which models become the default on the world’s local devices,” write @jdunnmon, @Avanika15, and @JonSaadFalcon. foreignaffairs.com/china/chi…

Curiously enough I did office hours today with a startup that cuts companies' LLM token costs by optimizing requests. They can cut costs by about half, which they split with the customer. So the TAM is a quarter of the model companies' corporate revenue. That's a big TAM!

As I wrote this, I saw X go into meltdown over tokens. You've seen the headlines: “Uber blows yearly AI budget in just one quarter.” “Meta employee burns 281 billion tokens in April.” But, the problem isn't spending. Spending works. Since 2023, the top quartile of our AI spenders doubled their revenue. The bottom quartile? Flat. It's blind spending. We don’t know which spend worked. A sales team has qualified leads. A support team has resolved conversations. These are units you can measure against. All a token tells you is the meter ran, not whether the work was worth it or not. Finance says, “half the budget,” engineering says, “double it” and you don’t know who’s right because there is no shared language of value. There’s no attribution, and no attribution means no allocation. For example, right now, all work, no matter the size or shape, defaults to frontier models. But meeting summaries and calendar updates don’t require GPT-5.5 Pro. In isolation this seems trivial, but re-route just 10% of a $10M AI bill from frontier to GPT-4 level intelligence you’ve saved nearly one million dollars. This sounds like a made-up stat — it’s not. It truly is that much cheaper. This is the future of finance: not blindly rubber-stamping or rejecting AI spend, but allocating it with the same rigor companies apply to headcount.

The token bill comes due: Inside the industry scramble to manage AI’s runaway costs techcrunch.com/2026/06/05/th…

How an open source project in Austria sent an open-weight company in China (Kimi) revenues soaring. Global atoms bouncing into one another. I suspect the Anthropic block also was a major catalyst. crossingriver.substack.com/p…

We partnered with @FireworksAI_HQ to train open-source models for legal. Here's what we found: 1) Hybrid legal agents can beat frontier models on quality and cost by routing selectively to a frontier advisor. We tested a hybrid setup where GLM 5.1 served as the primary worker, routing tasks to Opus 4.7 as an advisor when needed. GLM invoked Opus sparingly, just 0.83 times per task on average. The hybrid setup beat Opus on both quality and cost: 18% all-pass vs 14%, at $368 vs $954 across the same 100 tasks. 2) Post-training can push open models to frontier-level legal performance. On a 100-task slice of our Legal Agent Benchmark (LAB), SFT moved Kimi 2.6's all-pass rate from 11% to 15%, beating Opus' 14%. But the cost gap was even more striking: $84 vs $954 across the same 100 tasks, or ~11x cheaper. We're excited to continue working with @FireworksAI_HQ on the next generation of open-source legal agents.

Today we're announcing that hybrid agentic inference is coming to Perplexity Computer. Computer can split tasks between a local model running on your machine and frontier models in the cloud. This keeps private data on your device and maximizes token efficiency. Coming soon.