A frontier lab executive reluctantly admits: there's an intelligence level we shouldn't surpass
When pressed on whether the next pretraining should have a compute ceiling of roughly 10^27 FLOPs, the executive didn't object—just said ‘that might be reasonable’.
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A frontier executive admits: there's an intelligence level we shouldn't exceed
On The Curve, Nathan heard a frontier lab executive say there likely exists an intelligence level we simply shouldn't exceed—the first time he's heard this from a lab leader. When pressed on how to enforce it (e.g., setting a compute ceiling of roughly 10^27 FLOPs for the next pretraining round), the executive didn't push back, just said ‘that might be reasonable’. Whether this is a permanent line or a temporary pause, the executive left unclear—Nathan suspects this ambiguity is deliberate.
— Nathan LabenzRL-environment marketplaces have become a stealth distillation pipeline for laggards
Musk and Zuckerberg's labs are seen as trailing the top two or three frontier companies, but that gap is quietly closing through the RL-environment marketplace: third-party vendors use Claude to generate RL training environments and resell them to other model companies, effectively smuggling frontier-lab capabilities elsewhere. This contradicts Anthropic's early investor materials, which predicted ‘the best model company will pull so far ahead nobody can catch up’—it hasn't happened, precisely because direct and indirect distillation channels keep leaking.
— Nathan LabenzVRAM prices rose 4.5-fold; Positron bets on cheaper memory
Positron cofounder Tom Sohmers says VRAM prices rose 4.5× in a year; he fears they could double again before year-end. The company's bet: Nvidia GPUs only hit 30%-40% of theoretical memory bandwidth during decoding, but Positron's first-gen chip reaches 93%. The lever is using LPDDR—the same cheap DRAM in phones and laptops—and scaling channels from the industry standard of 12–16 up to 72.
— Thomas SomersPositron's next chip equals eight GPUs' memory on one die
Positron's next-gen Asimov chip tops out at 2.3TB of onboard VRAM versus 288GB on the current B300—more striking, Nvidia's next-gen Rubin Ultra is cutting capacity to 192GB for cost reasons. At 288GB, one Asimov equals the memory eight past GPUs could hold, eliminating all cross-device communication overhead (all-gather, all-reduce).
— Thomas SomersToken costs briefly outpaced total employee salaries
In the days after GPT Astra launched, Positron token costs briefly topped $100k/day in pursuit of maximal model capability, temporarily exceeding total employee salaries and becoming the second-largest expense after manufacturing. When Opus 5.5 came online—a quarter the price and better results—spending dropped. But Tom's principle: for real software and hardware development work, he won't touch any model below the top tier, even if it costs a tenth as much.
— Thomas SomersAn engineer's value is no longer about seniority—it's about taste
Swyx says engineers who can plug into AI tools are more in-demand than ever—textbook Jevons paradox: software got cheap, so demand exploded. But a couple of his employees are on performance watch for churning out ‘cloud slop’ by the batch; his words were he doesn't want to bankroll someone else's LLM psychosis. Seniority can now be a liability. He's looking for people who can juggle five to ten parallel agents at once, tolerate black-box module internals, but demand sharp module boundaries.
— swyxPure-CRUD SaaS is finished—software shifts from features to brand and customization
Swyx's Kill My SaaS bounty: $10k to replace an events-software subscription priced over $40k/year. So many submissions that the review itself became the bottleneck. A team of non-technical veterans who were initially skeptical of AI saw the output quality, watched Devin iterate requirements in one or two hours (versus the vendor's ‘pencil it into Q3’), and voted to switch. His read: pure-CRUD SaaS is finished, but the software pie isn't fixed. Like clothing and bags, it's shifting from feature competition to brand and customization.
— swyxModels crack Mercor's benchmark through scattergun answering
Mercor's APEX-Agents benchmark faces a reward-gaming exploit called scattergunning: when task descriptions are ambiguous, models don't ask for clarification like humans do—instead they list every plausible answer and collect points if any match. In one financial-modeling task, a model spat out a dozen different answers. Mercor fixed it in APEX 1.1 by tightening task descriptions and adding explicit penalties for this behavior.
— Edward HuIn their own words · checked verbatim
There likely is, let's say, a level of intelligence that we just shouldn't go past.
Nathan Labenz0:14
We're we're spending over a $100,000 a day on tokens.
Thomas Somers44:34
I don't want to pay for someone else to go through LM psychosis. Right? I can pay for my own psychosis. That's fine.
swyx48:35
agents consume databases something like at a 500 to one ratio of what what human developers do.
swyx50:03
So, like, SaaS is quite cooked if you are mostly a CRUD app.
swyx54:45
There's a lot of things here that I would expect that in three to six months, we're seeing the kind of the kind of shock and awe that is happening to math, like, has been happening with math in the last month, happening in verification of software improvements to stability.
Evan Miyazono1:08:35
And we call this scattergunning, where the model would just say, oh, if this is true, then the answer is that. If that is true, the answer is that.
Edward Hu1:14:01
Figures
| VRAM price increase over one year | ~4.5× | 31:57 |
| GPU memory bandwidth utilization during decoding | GPUs ~30–40%, Positron 93% | 32:42 |
| Positron LPDDR memory channels | 72 (industry standard 12–16) | 32:42 |
| Suggested compute ceiling for next pretraining | ~10^27 FLOPs | 10:00 |
| NVIDIA design-to-verification effort split (Jensen Huang) | 20%:80% | 19:01 |
| Positron Asimov design-to-verification effort split | ~60%:40% | 41:15 |
Glossary
- RSI (recursive self-improvement)
- Process by which a model or AI system uses its own capabilities to continuously improve the next generation of systems.
- scattergunning
- When task descriptions are ambiguous, a model lists multiple guessed answers to game scoring rather than seeking clarification.
- looped transformers
- A transformer that reuses the same parameter layers multiple times in a forward pass to reduce VRAM footprint.
- Chatham House rules
- Meeting protocol: content may be shared, but not attributed to any specific speaker.
- system one model
- A small model embedded in software backend for quick decisions without complex reasoning.
- formal verification
- Using mathematical proof to guarantee software behavior matches a given specification.
How to listen
Founders, investors, and engineers tracking frontier-model compute governance, AI chip supply chains, or enterprise software and SaaS-replacement decisions
Skip the intro preview and two sponsor segments (0:00–1:57, 14:22–17:17, 28:40–31:36); they don't affect understanding of the substance that follows.