Ed Zitron on big tech, backlash, boom and bust: 'AI has taught us that people are excited to replace human beings' His blunt, brash scepticism has made the podcaster and writer something of a cult figure. But as concern over large language models builds, he's no longer the outsider he once was I f some time in an entirely possible future they come to make a movie about "how the AI bubble burst", Ed Zitron will doubtless be a main character. He's the perfect outsider figure: the eccentric loner who saw all this coming and screamed from the sidelines that the sky was falling, but nobody would listen. Just as Christian Bale portrayed Michael Burry, the investor who predicted the 2008 financial crash, in The Big Short, you can well imagine Robert Pattinson fighting Paul Mescal, say, to portray Zitron, the animated, colourfully obnoxious but doggedly detail-oriented Brit, who's become one of big tech's noisiest critics. This is not to say the AI bubble burst, necessarily, but against a tidal wave of AI boosterism, Zitron's blunt, brash scepticism has made him something of a cult figure. His tech newsletter, Where's Your Ed At, now has more than 80,000 subscribers; his weekly podcast, Better Offline, is well within the Top 20 on the tech charts; he's a regular dissenting voice in the media; and his subreddit has become a safe space for AI sceptics, including those within the tech industry itself - one user describes him as "a lighthouse in a storm of insane hypercapitalist bullshit".

I spend a lot of time thinking about hype cycles, across industries (Big Data/AI, IoT) and ecosystems (New York). Whether you use the Carlota Perez surge cycle (see this great Fred Wilson post) or the Gartner version, hype cycles convey the fundamental idea that technology markets don't develop linearly, but instead go through phases of boom and bust before they reach wide adoption. Hype cycles are a great framework for investors (and founders), because entering the market at the right time is both crucial and very hard. Everything else being equal, you'd want to invest after the crash, early in the "deployment cycle" (Perez) or the "slope of enlightenment" (Gartner), when competition is comparatively limited but the market shows early signs of actual adoption. Easier said than done of course, because it is exactly the moment when things look the most uncertain.

I spend a lot of time thinking about hype cycles, across industries (Big Data/AI, IoT) and ecosystems (New York). Whether you use the Carlota Perez surge cycle (see this great Fred Wilson post) or the Gartner version, hype cycles convey the fundamental idea that technology markets don't develop linearly, but instead go through phases of boom and bust before they reach wide adoption. Hype cycles are a great framework for investors (and founders), because entering the market at the right time is both crucial and very hard. Everything else being equal, you'd want to invest after the crash, early in the "deployment cycle" (Perez) or the "slope of enlightenment" (Gartner), when competition is comparatively limited but the market shows early signs of actual adoption. Easier said than done of course, because it is exactly the moment when things look the most uncertain.

I spend a lot of time thinking about hype cycles, across industries (Big Data/AI, IoT) and ecosystems (New York). Whether you use the Carlota Perez surge cycle (see this great Fred Wilson post) or the Gartner version, hype cycles convey the fundamental idea that technology markets don't develop linearly, but instead go through phases of boom and bust before they reach wide adoption. Hype cycles are a great framework for investors (and founders), because entering the market at the right time is both crucial and very hard. Everything else being equal, you'd want to invest after the crash, early in the "deployment cycle" (Perez) or the "slope of enlightenment" (Gartner), when competition is comparatively limited but the market shows early signs of actual adoption. Easier said than done of course, because it is exactly the moment when things look the most uncertain.

Artificial intelligence (AI) has advanced unevenly over most of the past 50 years. Occasional spurts of breakthrough progress were followed by long winters of stagnation. When I started my career as a research engineer in the late '80s at Carnegie Mellon, most of us predicted that the autonomous vehicle being tested at the time would be on the roads shortly and a common thing by the end of the second millennium. For years, barriers such as technology cost, organization capability, and inappropriate policies kept AI from going mainstream. About 10 years ago, however, the pattern began to shift: advances in computing power, training data, and learning algorithms led to one performance breakthrough after another.