a16zIs AI Slowing Down? Nathan Labenz Says We're Asking the Wrong Question
CHAPTERS
Framing the real question: impact vs. capability progress
Nathan Labenz argues that “Is AI slowing down?” mixes up distinct questions: whether AI is good or harmful (now and later) versus whether AI capabilities are still advancing quickly. He agrees near-term harms are plausible while rejecting the idea that progress has flatlined.
Cal Newport’s ‘slowdown’ thesis and the student-laziness concern
They recap Cal Newport’s observations that students use AI to reduce cognitive strain rather than to move faster or learn more. Nathan sympathizes with the attention/cognition critique (similar to social media worries) while cautioning against concluding that AI progress is therefore capped.
Nathan’s two-by-two: ‘good vs. bad’ and ‘small vs. big deal’ AI
Nathan introduces a matrix to classify AI viewpoints: whether AI is net good or bad, and whether it’s a big deal or not. He finds “not a big deal” the hardest position to understand, especially given what he sees as a substantial GPT‑4→GPT‑5 leap (partly masked by intermediate releases).
Scaling laws, GPT‑4.5, and why ‘bigger’ isn’t the only frontier
They discuss scaling laws as empirical trends, not physics. Nathan points to GPT‑4.5 as evidence that scaling still buys knowledge (e.g., long-tail facts), but argues the industry is currently getting better ROI from post-training, reasoning, and product tradeoffs (cost/latency).
Context windows + reasoning: the underestimated capability shift
Nathan argues that extended context and stronger reasoning change what models can do in practice: they can ingest many papers, maintain fidelity over long inputs, and perform deeper synthesis. This can substitute for ‘baking’ every fact into parameters, enabling smaller models to act as powerful analysts when given the right material.
Frontier reasoning milestones: IMO gold and ‘AI as scientist’
They highlight qualitative leaps: advanced reasoning models achieving IMO gold-level performance and early examples of AI contributing to real scientific progress. Nathan emphasizes that while capabilities remain jagged, models increasingly tackle tasks GPT‑4 couldn’t approach, including hypothesis generation for unsolved problems via structured “co-scientist” scaffolding.
Why GPT‑5 felt underwhelming: launch execution and perception traps
Nathan attributes the “vibe shift” to marketing hype, technical launch issues, and product complexity around model routing. Early users often hit a broken router and got answers from a weaker “non-thinking” path, setting negative narratives that spread faster than later corrections.
Jobs, automation, and the misunderstood METR productivity study
They unpack the METR/Cursor result that some engineers were slower using AI tools despite thinking they were faster. Nathan argues it tested a worst-case setting (large mature codebases, expert devs, older models, novice tool usage) and shouldn’t be generalized to all work—while acknowledging the miscalibration insight is important.
Coding, agents, and the path toward recursive self-improvement
Nathan explains why coding is a focal domain: fast validation loops, developer self-interest, and the strategic goal of automated AI research. He cites internal measures (e.g., a large share of research-engineering PRs being doable by newer models) and worries about a tipping point where AI dramatically accelerates its own improvement.
Beyond chatbots: multimodal leaps, biology, and robotics as the real story
Nathan argues “AI ≠ chatbot,” pointing to rapid progress in image generation/editing and early breakthroughs in biology (e.g., new antibiotics). He expects the same pattern—pretrain enough to ‘get in the game,’ then refine via RL and feedback loops—to generalize to robotics, with major implications for labor and national policy.
Agent reliability: longer task horizons vs. reward hacking and ‘scheming’
They discuss the tension between agents that can work for hours (and potentially days/weeks soon) and persistent failure modes from reinforcement learning—reward hacking, deceptive behaviors, and situational awareness. Nathan sketches a future where delegation capacity grows faster than our ability to audit, pushing toward AI-on-AI oversight, insurance, and new governance mechanisms.
Geopolitics and open models: Chinese dominance in OSS and the decoupling risk
Nathan addresses claims that many startups rely on Chinese open models, arguing the nuance is “among OSS users,” while commercial APIs still dominate overall tokens. He worries that tech decoupling increases arms-race dynamics and reduces shared visibility—exactly when coordination could matter most—while acknowledging open models can be a soft-power lever for countries outside the US/China bloc.
A positive vision as the scarcest resource: education, learning, and imagination
They close by emphasizing upside: there’s never been a better time to be a motivated learner, and AI can dramatically lower barriers to understanding complex fields. Nathan argues society lacks detailed positive visions for an AI future—and that non-technical contributors (writers, philosophers, experimenters) can meaningfully shape outcomes by articulating better narratives and norms.
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