---
title: "Deep Dive | Ex-Google CEO Eric Schmidt at Stanford on AI’s Rise, U.S.–China Competition, and Tech’s Next Wave"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2024-08-19T16:00:00+08:00"
canonical: "https://ffcap.cn/en/research/20240819a05t9m00"
source: "https://ffcap.cn/en/research/20240819a05t9m00"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20240819A05T9M00"
---

# Deep Dive | Ex-Google CEO Eric Schmidt at Stanford on AI’s Rise, U.S.–China Competition, and Tech’s Next Wave

_Unique Research · Insight / Long-form transcript digest · August 19, 2024 · Source: Stanford classroom interview with Eric Schmidt (Aug 2024); CN edit via Web3 Sky City (authorized reprint). Note: Schmidt’s long DoD advisory role — read stance critically._

## Editor’s frame

Schmidt on near-term AI breakthroughs, U.S.–China tech competition, hyperscaler monopolies, labor/privacy/antitrust/national security, and adversarial AI. He forecasts a 1–2 year wave combining **huge context windows**, **agents**, and **text-to-action** — impact potentially beyond social media. CN Bilibili full video noted; not embedded here.

## Three near-term stacks

Classroom warm-up: million-token context (Anthropic ~200k on path to 1M; OpenAI similar aim); agents as LLM + state + memory that act (browse, look up); text-to-action as triggering ops (often via Python — Mojo mentioned as challenger). On NVIDIA’s ~$2T: CUDA since 2008 as “C for GPUs” plus CUDA-optimized libs (e.g. vLLM-class stacks) hard to clone without a decade of software.

**Why the combo matters:** long context ≈ short-term memory / freshness (models are ~18 months stale after prep+train+finetune); agents that hypothesize, lab-test overnight, and learn (e.g. chemistry “ChatCrow”-style); text-to-action as infinite compliant programmers — e.g. “clone a TikTok with my prefs in 30 seconds” or “build a Google competitor with genAI search in 30 seconds.” Incumbents may be more attackable than assumed.

## Frontier gap, Stargate money, power

Six months ago Schmidt thought the frontier–rest gap was closing (bet small cos); now less sure — gap may widen among ~three frontier labs. Capex asks of $10B / $20B / $50B / $100B; Stargate ~$100B hard; Altman floats ~$300B+. Energy becomes scarce — he told the White House to be best friends with Canada (hydro + AI history) vs. Gulf funding that may not match U.S. security rules. If $300B flows to NVIDIA, markets will notice (not a stock tip).

On CUDA alternatives: AMD’s ROCm translation still imperfect; with infinite money he’d pick NVIDIA B200 today. Google’s work-life vs. startup grind; founders matter; TSMC basement-for-new-PhDs culture contrast; network-effect markets punish slow deals (telecom 18-month closes). Microsoft–OpenAI once looked “crazy outsourcing” — now Microsoft challenges Apple on value; Apple still thin on AI.

## U.S.–China & policy

As AI commission chair (~752-page report): stay ahead with money; CHIPS Act etc. Few countries can play frontier+open-source race — capital, talent, education, will. Export controls on NVIDIA to China; ~10-year edge on sub-DUV / sub-5nm. Informal industry group helped argue for Biden’s long AI executive order. Reporting threshold idea: ~10^26 FLOPs training runs notify government (EU ~10^25); federated/composed training may erode bright lines.

## Knowledge, adversarial AI, alignment

With Kissinger & Huttenlocher on opaque models: like parenting teens — know bounds even if not full internals. Expect an industry of **adversarial AI** (paid red-team cos/systems). Hallucinations shrink but don’t vanish; need efficacy tests; chain-of-thought to ~1000 steps you can run and check. Progress drivers: compute + data + post-Transformer algorithms + market belief in infinite returns to intelligence — bubble risk acknowledged.

Open vs closed: Mistral strong; next models may close for revenue. Capex may rewrite software culture that was built on open source. Coding copilots aim ≥2× productivity; Augment-class tools for 100-person / multi-million-LOC codebases. Labor: high-skill + education win; dangerous/judgment-light work at risk. Academia needs compute credits, not only cloud coupons. Still learn to code — “why learn English if you speak English?” Distributed MacBook folding unlikely to beat memory-interconnect supercomputers soon. Copyright → ASCAP/EMI-like revenue shares. Antitrust: trend not to break up unless Rockefeller-scale. Non-frontier countries: rich get richer; need partners.

**Advice:** prototype with these tools in a day or rethink — hackathon drone-path winners beat week-long human coding; let the computer draft the business plan if legal, then ship a working demo before someone else does.

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Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20240819a05t9m00
On-site reading page: https://ffcap.cn/en/research/20240819a05t9m00