#观点/访谈

I think @NaveenGRao is one of the most under-discussed but brilliant entrepreneurs of our time. He is able to consistently see where a market is going and place himself ahead of it. - Founded Nervana and sold to Intel for $400M. - Founded MosaicML and sold to Databricks for $1.3BN. - Raised $450M seed round at $4.5BN valuation for Unconventional. This success has given him license to say things that few people would have the confidence to say. - “Most software investors today are f*****”. - “Agentic coding is overrated and uninvestable”. - “It is completely techno-optimist to assume companies will have their own models and own their own intelligence”. This discussion rallies against some of the conventional beliefs of the valley in a way you have not heard before. My notes from the discussion with Naveen below. (Link in comments). 1. Are Software Investors F**ked? Pure software investors are "kind of f**ked" because SaaS will no longer be where margin is gained. Since around 2010, cloud services let founders spin up products quickly and chase $100M to $200M markets. As applications shift toward the intelligence layer, Naveen does not see software being a huge market in 20 years. Do you agree software investors are f***** in a much more infra/hardware centric landscape @PalmerLuckey @mttgrmm @shaunmmaguire 2. Why Agentic Coding Is Overrated and an Uninvestable Category Agentic coding is overrated as a market because the value moves to the model provider. The harness and the IDE are not very sticky, and people move between tools fluidly. Outside of the model providers, it is a category he would not invest in today. What is the single biggest reason why the coding agent should not sit within the model provider @ScottWu46 @matanSF 3. Is It Completely Techno-Optimist to Assume That Companies Will Own Their Own Intelligence With Their Own Specialized Models? Owning your own intelligence takes scale. A firm like Cooley can invest to make it happen, while a mom-and-pop shop in Nebraska will buy an out-of-the-box service instead. Big players will customize because rivals using AI to supercharge their margins force them to. 4. Where Is the Value in the Robotics Stack? The value in robotics will not sit in the model alone. Naveen invests in companies building robotics models and expects them to end up building end-to-end devices, selling the robot together with a model that does something very well. Where will the most value accrue in the robotics stack? Model? Data? Hardware? @tonyzzhao @joshavata @bercankilic 5. Why Steve Jurvetson Is My Favorite Investor Meeting Of roughly a thousand investor meetings, the one that stood out was with @FutureJurvetson. He geeked out on numeric representations and marked up the papers Naveen sent him like a technical colleague. In 2014, when no one had pitched a hardware startup in 10 years, he was keen to invest because he had been thinking about the space himself. 6. How American Investors Differ From European Investors Founders in the US are given permission to think bigger. In Europe, the investor network is so conservative that a founder with a crazy idea is written off as mad, while in the US the same founder is seen as awesome for thinking big. EF has largely moved focus to the US and SF specifically, would you agree with this @Alicebentinck @matthewclifford? 7. Why the Inference Market Will Be $10 Trillion Within 10 Years AI models alone will be a trillion-dollar market in the next few years, and inference will reach $10 trillion within 10 years. Cutting the cost of delivering a token by one order of magnitude effectively 10Xs the market, since the base of potential uses grows more than 10X, which Naveen ties to Jevons paradox. What does no one see that everyone should see about the inference market @GavinSBaker @lqiao @bernhardsson

The only way out is through. They're calling it the Bugpocalypse. LLMs are being used to find long-hidden cracks in the software infrastructure, and it's happening at a furious rate. 3% of all compromises in computing history happened in the last week, claims one source? I believe it. There are people saying public access to the frontier models ought to be locked out, or heavily guardrailed, over this. The problem with that idea can be summed up in one sentence: when lockpicks are outlawed, only outlaws will have lockpicks. The lockpicks will be Chinese open-weight models, if nothing else. There's no way to stop those from becoming ubiquitous. There are two futures in front of us. One in which only the bad guys have lockpicks, and security holes go undiscovered until they're exploited. Another in which everybody has lockpicks and is aggressively red-teaming their own software so it gets fixed before the holes are exploited. The corpus of human code is huge but finite. The number of defects in it has to be finite too. We can fix those defects now; we have a million friendly robots to do it with. The only way out is through.

@nateberkopec I'm cautiously starting to suspect it may not be a problem with today's strongest models - Anthropic in particular are very confident about their ability to avoid prompt injections - but it's still a real concern for cheaper, weaker models

David Silver is one of the all-time greats. He was the creator of AlphaGo, which changed the course of history by showing that neural nets could achieve superhuman performance for the first time. He also has a unique intuition and knack for forecasting when things will happen in AI. With computer Go, he predicted the Lee Sodol outcome 10 years before, right to the year. He got the year right for Starcraft and won a bet inside Deepmind. Same for AlphaProof. Same for the IMO. David's prediction for superintelligence: 3 years. !!! He has a strong sense that now is the time to build a superleaner, and that superlearners are now possible, thanks to the enormous progress in compute, simulators, autoresearch, and the sophistication of technology in the stack since the AlphaGo days. What is a “super learner”? A super learner discovers all knowledge from its own experience. Start from nothing (no internet pre-training), and go beyond human knowledge. Drop it into any stream of experience, simulated or physical, and the superlearner learns. He expects the progress will be rapid. When you get the recipe for a superlearner just right and remove humans from the development loop altogether, a superlearner can learn extremely quickly. Just look at AlphaGo. AlphaGo took two years to develop with humans in the loop. When David and his team at @GoogleDeepMind got the formula for AlphaZero correctly, it surpassed the human-developed algorithms in just a few hours. Human data is a shortcut, but you have to let go of the human data to shoot beyond it. In January, David left GDM to found @IneffableLabs, a research-led lab with one mission: build a "super learner" that discovers all knowledge from its own experience, and make first contact with superintelligence. He joins us with co-founder Lasse Espeholt, who built IMPALA and SEED RL, the foundations of scaled-up RL, and now runs Ineffable's compute. A few things we cover in this conversation. — What a super learner is: depth (start from nothing, go beyond human knowledge) and breadth (drop it into any stream of experience, simulated or physical). The human brain is the only existence proof — Why they're giving up human data: AlphaGo took two years with humans in the loop. AlphaZero outperformed it in a few hours. "You have to let go of the human data to shoot past the competition" — Crucibles: simulated worlds with enough emergent complexity (physics engines, millions of interacting agents) for an agent to rediscover technology, science, maths and language on its own — Simple rewards, emergent complexity: evolution optimized fitness and got all of life. Don't breadcrumb the objective, give the agent something simple to optimize in a world with a lot to learn about — Go as the existence proof: one agent is the most boring game imaginable, two agents is "an infinite onion of layers of complexity" — Why human language is probably not the endpoint for reasoning, and how training on it bakes in a flat earth and the four humors — Can superintelligence be built in simulation? If the sim is Turing complete, computation, economics, language and maths are all expressible, and it happens 10-100x faster and safer — Alignment: language-based monitoring is reaching its limits as agents learn to say one thing and do another. Shadow actions, superintelligences monitoring superintelligences, and building a real science of how they interact peacefully — Accelerate in simulation, pace in the real world: Ineffable will gate real-world deployment on evidence that it goes well — Timing as the determinant: Silver called Computer Go to the year, then StarCraft, then the IMO. He thinks this is the moment — Predictions: True superintelligence within three years. Timestamps: 0:00 – Introduction 1:09 – What held true from Go to today, and what the LLM era changed 3:35 – Why leave DeepMind to start a new lab 5:58 – Why "Ineffable" (and the secret second reason) 7:03 – Lasse's path: scaling RL at DeepMind and Google Brain 9:31 – Defining a super learner and first contact 12:35 – Super learners vs. LLMs that make discoveries 13:59 – Day to day: the RL stack, crucibles and human-journey milestones 16:13 – Don't breadcrumb the objective: simple rewards, emergent complexity 21:16 – What a crucible looks like 24:14 – Go: one agent is boring, two is infinite 25:41 – Will agents rediscover language? 27:22 – Removing human priors: AlphaGo to AlphaZero 32:05 – Keeping human bias out of the crucibles 33:35 – Confidence, timing and super forecasting 36:24 – Is compute the driver? 38:13 – The ceiling on simulation and Rich Sutton's objection 43:44 – Alignment and the science of superintelligence 47:45 – Monitoring agents that don't think in English 51:01 – Pacing the frontier 52:54 – The compute setup: Vera Rubin on GCP 55:18 – Training, inference, simulation and RL scaling laws 57:40 – IMPALA and SEED RL 1:00:58 – 20 people and the compute-per-person ratio 1:04:49 – A research-led culture 1:05:47 – Predictions