AI is in a supply crunch today, but what happens when we come out of it? How and where will supply, demand, price, capacity and capex get back into equilibrium? Today, model labs can name their price, but why won’t they end up as low-margin commodity infrastructure?
Many people would like to analyse which jobs, companies and industries are most exposed to AI, and assign scores, build charts, and map that against the progress of LLMs. I think this is mostly impossible: you don’t know how the jobs will change, you don’t know what else will change around this, and you can’t measure work like that anyway.
OpenAI has some big questions. It doesn’t have unique tech. It has a big user base, but with limited engagement and stickiness and no network effect. The incumbents have matched the tech and are leveraging their product and distribution. And a lot of the value and leverage will come from new experiences that haven’t been invented yet, and it can’t invent all of those itself. What’s the plan?
How far do LLMs give us a step change in how good a search and recommendation system can be? Do they let you build one without needing a vast user base of your own?
With every platform shift, we want to measure the growth but we’re confused about what to measure. That’s partly a problem of data and definitions, but it’s really a question about what this is going to be.
Generative AI chatbots might be a life-changing transformation in the nature of computing, that can replace all software, but so far, most of its users only pick it up every week or two, and far fewer have made it part of their lives. Is that a time problem or a product problem?
Software ate the world. Uber and Airbnb didn’t sell software - they disrupted and redefined markets. But what kind of disruption are we talking about ?
It matters that Apple’s new Siri will be late, and it matters more that Apple didn’t realise. Is it more than that?
OpenAI’s Deep Research is built for me, and I can’t use it. It’s another amazing demo, until it breaks. But it breaks in really interesting ways.
Every week there’s a better AI model that gives better answers. But a lot of questions don’t have better answers, only ‘right’ answers, and these models can’t do that. So what does ‘better’ mean, how do we manage these things, and should we change what we expect from computers?
A quarter century after ‘don't be evil’ a judge has found that Google is abusing its monopoly in search. But no-one knows what happens next, and whether this ruling will change anything. Will Apple build a search engine? Will ChatGPT change search? Does it matter?
Hundreds of millions of people have tried ChatGPT, but most of them haven’t been back. Every big company has done a pilot, but far fewer are in deployment. Some of this is just a matter of time. But LLMs might also be a trap: they look like products and they look magic, but they aren’t. Maybe we have to go through the slow, boring hunt for product-market fit after all.
Meta has spent at least $50bn on VR and AR so far, but we’re still in the VR winter: the devices aren’t good enough or cheap enough and the user base is flat. But no matter how good the devices get, how many people will care?
Apple has showed a bunch of cool ideas for generative AI, but much more, it is pointing to most of the big questions and proposing a different answer - that LLMs are commodity infrastructure, not platforms or products.
How do we build mass-market products that change the world around a technology that gets things ‘wrong’? What does wrong mean, and how is that useful?
How do we think about a fundamentally unknown and unknowable risk, when the experts agree only that they have no idea?
Generative AI means things that were always possible at a small scale now become practical to automate at a massive scale. Sometimes a change in scale is a change in principle.
We’ve had ChatGPT for 18 months, but what’s it for? What are the use-cases? Why isn’t it useful for everyone, right now? Do Large Language Models become universal tools that can do ‘any’ task, or do we wrap them in single-purpose apps, and build thousands of new companies around that?
Can you write laws, or lay down ethical principles, for a technology that will be used in entirely different ways, for different purposes, in different industries? What does that mean if it’s changing entirely every 18 months?
Tech regulation gets a lot of headlines, and seems like a big deal, but most people in tech don’t seem to care much. It’s boring, and years away, but more fundamentally, it really doesn’t affect what people are working on.