Enterprise AI adoption is real but harder to turn into ROI

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 55:39

   there are certain domains in which the LLM is useful and works if we just throw millions of dollars at it. And let's just imagine that we have unlimited money. We don't, of course, but right now they seem to. And so that's what they're going to do. And it seems like in some domains it works. Your view, it sounds like, is that in terms of businesses, enterprises, that enterprises are not benefiting that much from these LLMs. And the pushback to that would be, well, we are seeing an increase in enterprise usage. We're seeing these companies hitting huge numbers in terms of their annualized revenue. I think OpenAI hit, I want to say, $70 billion ARR. We should always put an asterisk next to the ARR number, but still, big number. Some would say the enterprises are using these products a lot? Are they actually getting an ROI on that? That is a completely

2. 56:36

   separate issue. Maybe they are, maybe they aren't. But that would be the pushback. Is it your view that enterprise usage is just not a tributary that is going to work for LLMs and that you would need some other architecture, maybe a modular architecture, to make it truly useful? Well, I think it's proving to be harder than they anticipated. I think the ROI right now is very mixed. I think there is heavy investment in this technology by enterprise, but a lot of this is experimental.

3. 57:03

   The sense of trying to figure out how can this be useful. Early attempts to have slam dunks in the enterprise application faltered. I think we can look at Microsoft Copilot product as a instructive example, right? It's a no-brainer of a product. It's what I said we should be doing. This is the lowest hanging fruit in knowledge worker productivity is natural language interfaces into tools. This is what the Copilot product was that had been pushed into the Microsoft 365 Office Suite, Microsoft essentially shut it down earlier this year because it turns out to be harder than they thought to talk to an LLM about what you want and have it produce on the other end the proper output to control a program.

4. 57:43

   Now, I think we are going to solve that problem, but it's going to take more time and be more bespoke and complicated. We're going to have to open up code-based APIs into each of these programs because code is a language that LLMs speak. There's going to have to be custom tuning. It just proved to be harder than they thought it was going to be. So the future I see is I do think there's going to be wide enterprise use of LLM.

5. 58:04

   tools. But I think it's proven harder than they hoped to actually make this happen. I think there's going to be many specialized products, just like we did with computer programming. Computer programming agents powered by LLMs are an incredibly bespoke product. They were the first big commercial breakthrough in part because the people working on them are also computer programmers. So it's their area. They know that field very well. You're going to have to have a similar expertise probably come in for other applications of these tools to other parts of enterprise. It will happen. It has to. LLMs are good at understanding and producing text.

6. 58:38

   Knowledge work operates on a backbone of human text, right? Spoken language and written language, but it's just going to be harder than we thought. And so we're going to have bespoke tools that are going to take a while to get right. We're going to have more modular architectures for certain types of applications. And there's other types of things we're imagining that'll just never come to fruition. So that will be a sector, but it's proving harder than people thought. So the question is, can Anthropic hold out long enough, have their successful IPO,