E146: The 92% AI Failure: Unmasking Enterprise's Trillion-Dollar Mistake
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What is the focus of this podcast episode on AI?
An AI native solution. The framework is not just that AI is replacing what a human is doing, but how would you design the model with AI in mind? I think most of the material benefit you're going to see is when you clean sheet any process to be like, how would I design this process knowing all the AI tools I have from scratch? And how do I use both technology and humans? And by the way, I think the example for that is going to involve both for a long, long time. In fact, I think Humans are a core part of this solution. I think Invisible, we believe that's the human machine interface where all the value sits. But it's not necessarily just giving all your people on an existing process and a tool. It's redesigning the process to use all the tools at your disposal.
So let's talk about Invisible. Give me some specifics on how the company is doing today. I joined in mid-January. We ended 2024 at $134 million in revenue. Profitable. We were the third fastest growing AI business in America over the last three years. So how will deep seek affect invisible? The viral story was that it was $5 million to build the models they did. The latest estimates that have come out since in the FT and elsewhere would say it's closer to $1.6 billion. I think the number that's been cited from a compute standpoint is like 50,000 GPUs. So if you had just told that narrative as the exact same story, but with $1.6 billion of compute, I don't even think it would have been a media story. The fact that it costs over a billion dollars to build that model means it is a continuation of the current paradigm. Look, there are some interesting innovations they've had, a mixture of experts and
They did some interesting stuff around data storage that does have some benefits on reducing compute costs. But I think those are things we've seen other model builders experiment with already. If I think about types of data, they basically went around things that are base truth logic, like math, where there's a fair amount of synthetic data available. That's a fairly small percentage of the overall training tasks that I'd say most model builders are focused on. Tell me more about that. Think about training as kind of three main vectors. So you have base truth information where a lot of synthetic or kind of internet broad-based data exists. So math is a really good example of that. Then you have tasks like creative writing where there is no real kind of AI feedback. There's no synthetic data that's existing. There's no way to train those models without human feedback.
But the most interesting one is you have a whole set of base truth information where you also don't have enough synthetic data. So an example of that I would give would be computational biology in Hindi. The corpus of that is just not broad enough. Each branch of that tree and each topic you train off of will have a different approach. And tell me about what Invisible Technologies does exactly. We have two big components of our business, what I call reinforced learning and feedback, which is the process on any topic where a model is being trained. We can spin up a mix of expert agents on that particular topic. So that could be everything from, I mean, I use the example of computational biology in Hindi. Our pool has a 1% acceptance rate and about 30% of the pool is PhDs and masters.
So these are very high-end specific experts. The funniest one I talked about recently is like falconry in the 1800s, things where there's just not a lot of good existing data. And look, I think models are going to be built on the full corpus of information that is matter to humanity. So there's a lot of branches of that tree, and we bring all of the different experts to help train those models. But that's only half the business where we're seeing increased focus and demands on the enterprise side. The big challenge today and the kind of chasm that exists between what's called Silicon Valley and the enterprises, there's a demand for broad-based model development, which is really important.
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