How fast is AI? Weak links and the 2% growth line<br>Explore the curve<br>How fast is AI?<br>AI is moving fast. The economy is slower.<br>For 150 years, US living standards rose ~2% a year, through electricity, the transistor, and the internet. AI may be another general-purpose technology. The question is how fast task-level progress becomes measured growth, and how much depends on the weak links still handled by people.<br>I built this to hold both facts at once: the 2% line, computers' shrinking GDP share, weak-link business chains, the forecasts, the objections I find hardest to answer, and role horizons I update as the evidence comes in.
US income / person (history)World income / person≈2% / year trendBaselineFull automationIncomplete automation
Source: US and world history approximated from Maddison/BEA patterns; the world line grows slower early, then accelerates as the rest of the world catches up. Forward paths follow the published “Continuing the Past” growth scenarios. · Aghion, Jones & Jones (2019)
~100M<br>ChatGPT users<br>in ~2 months, fastest ever at the time
150 yrs<br>of ~2% growth<br>through every prior transformative tech
4.3% → 3.0%<br>computer share of GDP<br>falling since 2000, despite more chips
20+ yrs<br>self-driving diffusion<br>from “solved in 5” to still-rare
The big idea<br>A chain is only as strong as its weakest link.<br>Most businesses are chains of tasks, handoffs, approvals, and accountability. Make a few links cheap and the total result can still be capped by the remaining links. Pick a business below and change the tasks AI can already do.<br>Pick a businessSoftware companyHospitalBankRetailer<br>To ship a product feature , this business runs 9 tasks in a chain. AI makes the routine ones strong; the human judgment, accountability, and physical tasks stay weak.
38<br>Specs
58<br>Design
95<br>Code
82<br>Review
88<br>Tests
92<br>Deploy
85<br>Monitor
35<br>Roadmap
42<br>Customer
Each bar is a real task. Click to automate it to 100%.
Overall chain strength<br>59%
0 of 9 tasks automated
Getting stronger. Keep raising the lowest tasks, not the ones that are already high.<br>Automate the strong tasksFix the weak links tooReset
A billion times the compute<br>The compute a dollar buys has doubled every couple of years for decades; by now it is roughly a billion times what the mainframe era got. Productivity growth still sits near 2%. The cheap input is not always the scarce input. Judgment, attention, and trust often still gate the result.
Free doesn't mean infinite<br>In the model, driving one task's cost to zero raises total output by roughly that task's share of the economy. If software is a few percent of GDP, infinitely cheap software makes us a few percent richer, once. Lasting growth means moving the next bottleneck, and the next.
The puzzle<br>Transformative technologies, and still 2% a year.<br>Electricity, internal combustion, antibiotics, semiconductors, and the internet all changed everyday life. Measured growth still stayed close to its long-run path: each wave kept the 2% line going as the previous one ran out of steam.
Real income / person ≈2% / year<br>ErasElectricity1882Mass production1908Transistor1947Microprocessor1971The Internet1991Generative AI2022
Source: US real income per person, 2025 dollars (approximated for display).
Computers are everywhere, but their price falls faster than their quantity rises. The weak links capture the value.
Source: Computers’ share of US GDP, approximated from the BEA/BLS value-added pattern.
The chart that surprised me<br>Computers are everywhere, so I assumed their slice of GDP had grown. It has fallen by about a third since 2000. Prices dropped faster than we bought more, so the abundant input got cheap while the scarce inputs, people, trust, coordination, kept most of the value.<br>That is the weak-link pattern in one chart: making an input cheap is not the same as automating the work it sits inside.
Where we are today<br>Dates, predictions, and bottlenecks.<br>The page tracks claims that can be checked: adoption milestones, failed forecasts, and places where the remaining bottleneck is still human. The pattern so far: adoption can be very fast while economic transformation is slower.<br>Milestone timeline<br>Mar 2004Verified<br>DARPA Grand Challenge: zero finishers<br>Not a single autonomous vehicle completed the desert course. The starting gun for self-driving, and a reminder of how hard the physical world is.<br>DARPA Grand Challenge (2004)
Oct 2005Verified<br>Stanford's "Stanley" wins the DARPA Grand Challenge<br>One year after zero finishers, Sebastian Thrun's team completed the 132-mile course. Twenty years later, robotaxis are still rare outside a few cities.<br>DARPA Grand Challenge (2005)
Sep 2012Verified<br>AlexNet ignites the deep-learning era<br>A deep neural network crushed the ImageNet benchmark, kicking off the modern wave of AI capability gains.<br>ImageNet / AlexNet
Mar 2016Verified<br>AlphaGo defeats Lee Sedol<br>DeepMind's system beat a top human Go player 4–1, years ahead of expert expectations for the game.<br>AlphaGo vs. Lee...