AI agents could flatten business and speed up work

Transcript

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

1. 11:24

   You've been talking about agents for quite some time. Just how do you see the development of agents and the use of agents? What is it going to lead to? so agents it's first important to define agents one way of understanding the path was we went from chat gpt which is language to language to essentially systems that have tool use so they they they you can talk to them and they can invoke tools then the next thing which we're all gaga over is agents and an agent can be understood as an input you know a request or a fact pattern.

2. 12:00

   or something and then a set of reasoning and a set of activities of which there's a lot and then a summary this works well agents work well in business flows but it also if you think about it it kind of works in everything uh travel agents right what does a travel agent do he or she knows your preferences they talk to other people they sort of integrate it and they make a recommendation of where to go a human one that's easy in almost everything if you look at human business activity professional activity you can work it out as a series of agents that are concatenated so you have the the person that designs the car you have the person that manufactures the parts you have the person that that assembles the car and you have the person that services the car almost all of that can be agentic and if you combine it with robots which

3. 12:53

   are not occurring as quickly as the software, you could imagine replacing that human workflow by agents plus robots. How is it going to reshape business in the medium term? I have a strong view that almost none of the companies that are listening to this will actually do the necessary changes in their culture. i'm sorry to be so blunt but historically companies are organized around belief systems they have ways of operating everyone's comfortable they show up in the morning everything's predictable and so forth this is a very very powerful flattening of the organization i'll give you an example this is not an ad for for uh anthropic but they brought out something called clawed tags and a clawed tag sits in your uh basically in in your slack and you can talk to it so you and i each have our own tag that we talk to but there's other tags sitting in the

4. 13:59

   background that are also monitoring systems and you're the ceo which you would naturally be in our company um you would have access to everybody's tags and you would be able to have your tag run around and look at what everyone else is doing so as a matter of productivity wouldn't you like to know that all the people that are working with you are working on on the right things wouldn't you like to have a phone call or the equivalent of a phone call if someone was exfiltrating data because they're going to a competitor in your case because you do so much investing wouldn't you like to have an immediate summary of a business opportunity and then have it sort of take off and have everybody go, oh my God, we have to deal with this right now.

5. 14:44

   So business time, so business will be flatter, smarter, and more intense, faster. And that's, by the way, that's not a new fact. That's been true in business for 100 years. Why so slow? Why so slow to happen? Why so slow to happen? Why do people hate change so much and making de-layering so difficult? That's a question for people who understand humans better than I do. All I'll tell you is that human organizations, and if I can be blunt, European human organizations, will be extremely change-resistant.

6. 15:17

   The way these things work is typically the small startups pioneer it, and then eventually the large companies get there. If you look, I'll give you an example. Anthropic is busy writing most of its code using its own code. In other words, the human tells it to write something, it writes the code, and then the code gets rewritten by the computer. So it's better and better and better. This is a huge productivity change.

7. 15:50

   How quickly can other companies that are doing software do this? It depends on how quickly they understand it. Do they have leadership? Are they technically strong enough? Typically, the tip of the sphere are in tech, intense businesses. Think of shareholders, stock markets, those kinds of things, information-centric because you can move quickly. There's one other point to make, which is that the gains that are occurring right now are in software, cyber, and math.

8. 16:21

   Software because the system is learning. As it writes code and it runs the code, it gets a signal and it says, oh, that was bad, tries again. so the faster the learning loops the smarter it gets cyber because writing software is the same as writing software cyber attacks it's just it's just evil right and so we have this huge set of threats now from these models being used for the same purpose they weren't designed for that but it's a natural and again because they can just keep learning and math because it's so-called scale free you don't need more data you can just do more math the mathematicians invent stuff in their own head. Whereas in physics and chemistry, you have to have real or simulated data.

9. 17:04

   So the industry is moving the fastest in what are called the scale-free parts and will be the slowest where the data collection is horrible. Biology, for example, is so complicated that we hardly know why the details of cellular cells work. There are so many examples where we just don't have enough data to train the initial models. Now, people are working really hard on this because of the importance of getting to medicine. I have friends who think that in the next 15 or 20