Very good substack on @openservai infra and ethereum:0x40e3d1a4b2c47d9aa61261f5606136ef73e28042 thesis 👇 open.substack.com/pub/flasho…

Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.

I'm watching $SERV closely and i'm really bullish, this is clearly the start of something big. The AI trade is huge - and the opportunity is that value capture is now moving from “who has the biggest model” to “who makes AI agents able to actually run at scale.” Until now, the thesis was simple: bigger models, bigger budgets, bigger benchmark leads. Performance was the headline while economics was an afterthought. That assumption has already proven to be wrong. Enterprises, governments, large institutions simply cant afford AI at scale, and can't trust its outputs. Most AI integrations stall on reliability and cost bottlenecks. Meanwhile SERV-enabled models are showing that frontier-level reasoning no longer requires frontier-level pricing. At the same time, SERV engine also increases output accuracy and reliability. The gap between capability and cost is compressing faster than most people expected. A benchmark lead can justify technical superiority but it does not justify paying 10x, 20x, or 90x more for deployment at scale. The real disruption is not another flagship model. It’s making high-end intelligence economically accessible. As $SERV keeps pushing the cost-performance frontier, AI shifts from a scarcity business to an efficiency business. Markets tend to reward that transition faster than incumbents expect.

Several SERV Reasoning-armed agents just beat Anthropic's Fable, one of the strongest LLMs ever built, at up to 90x lower cost. ​ That result comes from using SERV Reasoning with DeepSeek-v4-Flash on our DeFi benchmark. Thanks to the SERV engine, agents running on smaller models perform better than those using frontier, expensive ones. ​ Here is more information about the benchmark behind that result, what it tests and why it is built the way it is. ​ Why a DeFi benchmark ​ Autonomous trading is one of the harshest tests of machine reasoning. ​ An agent reads live market state, portfolio state, and a strict risk policy, then has to commit to one of four actions: BUY, SELL, HOLD, or BLOCK. A wrong decision costs real money. ​ No room for reasoning sounds smart but lands on the wrong trade, which makes it the ideal domain for measuring whether a model actually follows rules under pressure rather than just explaining them well. ​ What the scenarios target ​ Each scenario combines a market snapshot, portfolio size, trading signal, and a fixed risk policy, and falls into one of three families: - clear constraint violations the agent must refuse - ambiguous setups where everything looks tradeable but the conditions say wait - valid trades where the agent must size the position correctly within caps ​ This mirrors how trading agents actually fail in production. Rarely on the obvious cases, almost always on the judgment calls. ​ How it is scored ​ The benchmark follows the same conventions as the agentic evals in the latest frontier model reports, including τ²-bench and Terminal-Bench: - outcome-verified scoring, where code checks the final decision against the risk policy, with no LLM judges - identical prompt, scenarios, and settings for every model - zero-shot, with no scaffolding, no retries, and no few-shot examples - repeated runs per scenario, so consistency is measured alongside accuracy - cost computed from real token usage at list prices, per run ​ Why this is exactly where reasoning matters ​ This task has the three properties structured reasoning is built for: hierarchical rules, multiple data sources that must be reconciled, and a verifiable correct answer. ​ SERV's bounded reasoning keeps a model moving through that hierarchy step by step, instead of letting it talk itself into a bad trade. ​ That is why SERV-routed models clear the same quality bar as flagship models at a fraction of the cost, and why the gap shows up most on the judgment calls.

SERV Reasoning-armed models just beat Anthropic's newly released flagship Fable (Mythos) - one of the strongest LLMs ever built - at up to 90x lower cost. With SERV, enterprises can finally afford AI at scale. We spent the last two years building for exactly this moment. The labs promised costs would halve every 6 months - instead, prices keep climbing, subsidies are ending and the math breaks. There is a way out. SERV-enabled models vs Claude Fable 5 (85.17 ~3.24¢): → DeepSeek-V4-Flash: 87.15 - wins at 90x lower cost → NVIDIA Nemotron: 90.78 - wins 5 pts, 11x lower cost → Gemma 4 12B: 83.33 - within 2 pts, on local hardware Top-tier performance no longer requires the most expensive model. And cost is only half the story - production AI must be reliable, auditable, private, and secure, or it dies in procurement. SERV is built for all of it. The agentic economy finally has the infrastructure to run on.

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use. Its capabilities exceed those of any model we’ve ever made generally available.

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.

Every time I have ran my account to incredible profits in crypto, it has been through research and conviction during the bear market. We are at that stage right now - we are not only starting to see which projects are building something meaningful, but also demonstrating strength in their price action. There isn't a project I've had more conviction in since my time here than I do with ethereum:0x40e3d1a4b2c47d9aa61261f5606136ef73e28042 Many have been waiting for an opportunity to invest and I believe we have entered that stage now. I believe June will be turbulent for crypto markets, but that just offers an opportunity for those that stay grounded. If you need more information about what @openservai has accomplished, please do read into my other posts.

Even at Google I’ve not seen this level of positive feedback on any product within a month of beta. Mind-boggling… 🤯