---
title: "Deep Dive | Ex-a16z Partner Benedict Evans: LLMs Still Need the Last Leap from Tech Breakthrough to Product"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2024-08-07T17:45:00+08:00"
canonical: "https://ffcap.cn/en/research/20240807a07kqa00"
source: "https://ffcap.cn/en/research/20240807a07kqa00"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20240807A07KQA00"
---

# Deep Dive | Ex-a16z Partner Benedict Evans: LLMs Still Need the Last Leap from Tech Breakthrough to Product

_Unique Research · Insight · August 7, 2024 · Essay by Benedict Evans (ex-a16z partner; independent analyst)_

Hundreds of millions tried ChatGPT; few became habitual users. Enterprises ran pilots; few put models into core workflows. Part of that is time — but LLMs may also be a trap: they _look_ like products when they are not. Product–market fit still has to be found the hard way.

## Adoption always takes longer than the demo

Marc Andreessen’s line: every failed idea of the dot-com bubble can work today — after broadband, PCs, e-commerce infra, ads, and behavior change. U.S. retail took **20+ years** to move ~20% online. Early iPhone: **5.4M** units in year one; real takeoff ~2010. Cloud still only ~1/3 of enterprise workflows decades after Benioff’s browser pitch — even as Silicon Valley treats cloud as settled.

ChatGPT was faster: ~**100M users in ~2 months** (late 2022). OpenAI didn’t wait for new devices or carrier builds — a site/app on 25 years of internet infra. But “use” often means curiosity once or twice, or every few weeks — not daily habit.

## Pilots ≠ production

Bain-style surveys separate experiments from real deployment: almost everyone is testing; few wire LLMs into core processes. Coding and marketing show clear pull; legal/HR slower. Accenture: ~**$300M** genAI work across ~**300** projects last year (~$1M average) — mostly trials. BCG expects ~**20%** of 2024 revenue helping large companies “deal with” genAI. Morgan Stanley CIO survey: **30%** of large-company CIOs see no product deploy before **2026**. Enterprise sales cycles can outlast the time since GPT-3.5 shipped.

## The “looks like a product” trap

LLMs look general, look magical, look finished — a chatbot _feels_ like a product. Outputs can be wrong in hard-to-manage ways: you cannot ship raw prompt → raw answer. Bing’s early LLM search push (make “Google dance”) is the cautionary tale; patience may beat forced launch. Capex raced ahead of mostly experimental budgets — skipping the painful bottom of the S-curve where prior platforms learned PMF (App Store wasn’t in the original iPhone plan; Berners-Lee’s first browser was more editor than publishing platform).

## Bubble — or anti-bubble?

Startup stack is Silicon Valley’s bet that LLMs are a _technology_ needing classic customer discovery — not one chat product that ends history. If models eventually do everything, many of those startups would be unnecessary — an oddly optimistic “anti-bubble” reading. Dot-com dreams did come true eventually; a J-shaped S-curve is possible. **It will not happen this year.**

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Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20240807a07kqa00
On-site reading page: https://ffcap.cn/en/research/20240807a07kqa00