| AI, 2025 and 1997
I took 65 flights this year, and gave a lot of presentations to a lot of people, because lots of things are changing. Indeed, things are changing so much that I moved from making one macro tech trends deck to two. This is the last newsletter issue of the year, and so I’m supposed to write a recap of everything that changed, and there’s a lot you can say, but looking back, it seems to me that the important thing is how much the conversations have become separated and specialised.
ChatGPT blew up three years ago, and for at least the first two years there were only a pretty narrow set of questions: how much better would the models get, how many would there be (would Google/China/open source catch up?), and how far could the models just do ‘the whole thing’ versus needing lots of product on top. Now all the conversations have broken apart, from the top to the bottom of the stack, and there are three-hour podcasts for all of them.
So, we now have detailed analysis about ‘infra’, which splits into chips, data centres, electricity generation and connections.
Faster chips haven’t really meant much to anyone further up the stack since the 90s, and data centres haven’t been interesting to non-specialists since the early days of Google, but now these are the stack and they’re the constraint for everyone else: the big four platform companies will have spent around $400bn this year and are far behind demand.
So suddenly everyone needs to know what EUV means, and where Oracle’s CDS trades, and look up what ‘gigawatt/hours’ are in Wikipedia, and we discovered that no, we can’t double the production of gas turbines next year.
I don’t think Nvidia is Cisco (Sun Microsystems is a much more interesting comparison), but this does look like fibre, particularly in the way people start to confuse ‘there is more demand than supply’ with ‘any investment will pay off’, and complicate the balance sheets (remember IRUs?). But for now, Jensen Huang keynotes are the new Steve Jobs keynotes.
The next level up in the stack, in the models themselves, there’s a basic split. On one hand, there is an enormous number of new acronyms, reflecting proliferating technical sophistication and complexity, as people iterate around a handful of core concepts, and, meanwhile, it’s very clear that there is no defensibility to a SOTA model – with the right engineers and a billion dollars, anyone can make one.
But on the other hand, the gurus of the field (and I choose this work deliberately) make Buddha-like prognostications on how the field might evolve, all of them wrestling with the fact that we don’t really know how or why this works so well, nor what it can’t do, nor what separates it from people.
We can say that progress has continued, and lots of people say ‘exponential’, but
we have neither an empirical nor a theoretical way to know whether we are now building out a technology that will get much better and more mature but not change in character – like the internet in 1997, say – or whether we’re at the beginning of a vastly bigger change.
The vibes seem to be shifting towards the former view, that we need other unknown breakthroughs (Demis Hassabis, Ilya Sutskever, Yann LeCun are on this side), but this is only vibes – we don’t know.
Going up the stack again, into the product layer, even this summer it really felt like there was no product strategy. All the labs made a chatbot, and the chatbots all looked the same. The models might be better or worse (though not very), but the products were all the same – the biggest difference between Claude and ChatGPT was the colour of the icon.
That’s changed a lot since then, or at least become a lot more clear. Google’s reaction has come on stream: the models caught up, but much more importantly so did the product and distribution, with a lot of survey data now showing it with half or 2/3 the use of ChatGPT already. The bear case for OpenAI, indeed, is that it’s MySpace but without a network effect, or Netscape. On the other hand, Sam Altman doesn’t sit still, and the company is ‘flooding the zone’ with product ideas and press releases, trying everything, all at once, yesterday, at every level of the stack.
Really, it seems to me that OpenAI is moving as fast as it can to swap mindshare and expensive stock for tangible infrastructure, market position, and consumer lock-in, because today, all it really has is a lab that’s as good as anyone else’s, a brand, and 800m users who mostly only visit one a week and could easily switch. It’s a mile wide and an inch thick.
I think about the web in 1997 a lot, metaphorically, and about mobile in 2005 and 2010. We knew this was huge and there was a lot going on, but most of what was going on turned out not to matter, and we didn’t really know how this would work. I have an i-mode phone, I worked on a DVB-H project, and I used Pointcast. It’s not just that we didn’t know who would win – we didn’t know where the competition would be.
Microsoft won browsers, and I’m old enough to remember that Internet Explorer was actually a better web browser than Netscape (sorry Marc) but it turned out that winning browsers didn’t get Microsoft anything.
That doesn’t mean we can’t make predictions, though. Looking at that stack – commodity infrastructure doesn’t tend to have super-normal margins in the long term; if models remain commodities too then the same will apply; and if the right use-cases and interaction models haven’t been invented yet, the companies that work it out probably haven’t been founded yet. |
