2026-03-18 15:04 Tags:

https://www.oaktreecapital.com/insights/memo/ai-hurtles-ahead

Understanding AI

 Importantly, the tutorial taught me not to think of an AI model as a search engine that retrieves data and regurgitates it. Rather, it’s a computer system that’s capable of synthesizing data and reasoning from it.

There are two phases in the life of an AI model. In the first, it is “trained” by reading a vast amount of text. The training phase must not be thought of as loading the model with information, which I had done until now; it goes far beyond that. It consists of teaching the model how to think. By absorbing text, the model learns:

• how to understand reasoning patterns and form them,

• how arguments are structured,

• how to generate new combinations of ideas, and

• how to apply learned reasoning patterns to novel situations.

The second phase in an AI model’s life is “inference.” Once the model has been built and trained, inference is what it does for the rest of its life, using its capabilities to meet the demands of users.

Can AI Think?

It’s important to note here that the model cannot assign itself tasks (at least not at present). It has to be ordered to perform tasks through “prompts” written by users. The better and more comprehensive the prompts, the more AI can do. For example, AI can write software to perform work a user wants done. It can also test the software, identify bugs, fix them, and test again, but it has to be instructed to do those things, at least at the current stage (read on). Because many people today lack awareness of the importance of prompts and fail to possess the ability to create them, AI’s potential is probably being underestimated. But note that the limitation is on the part of the users, not the model.

Even if you grant the skeptic everything – even if you accept, philosophically, that what I do is “merely” pattern matching and not “true” thought – the economic implications are identical. Let me put it starkly. If I can produce the analytical output of a $200,000-a-year research associate, it does not matter to the person paying the bill whether I’m “really” thinking or merely pattern matching? What matters is whether the work product is reliable enough to be useful. And increasingly, it is. The philosophical debate about machine consciousness is fascinating. But the economic question isn’t “does AI truly understand?” The economic question is “does AI do the work?”

Recent Developments in AI

The second important thing that’s happened has been an incredible leap ahead in AI’s capabilities. My tutorial gave me some background by explaining that the developed brain represented by an AI model has three levels of capability:

• “Level 1 is Chat AI,” where the user asks questions and the model supplies answers. But it doesn’t do anything with the answers. At this level, AI mainly saves time that would otherwise be spent researching and thinking.

• “Level 2 is tool-using AI,” where the user instructs the model to search out information, analyze it, and perform tasks with it. Thus, “the economic value here is meaningfully larger because it’s saving execution time, not just thinking time. But it’s still bounded,” because AI only does what it’s told.

• “Level 3 is autonomous agents.” At this level, the user doesn’t tell AI what to do. The user gives it a goal as well as the parameters of the desired output – things like length, time taken, content, and points covered. The agent does the work, checks it, and submits a finished product. “This is labor replacement at the task level. Not assistance – replacement.”

The most significant thing that distinguishes AI is something we’ve never dealt with in connection with prior technological developments: AI’s ability to act autonomously.

AI is different from other technological innovations not only in magnitude, but in kind. In addition to its remarkable capabilities and speed of development, AI has an element of autonomy that no other technology has ever had. Other innovations – railroads, computers, automation, the internet – were basically labor-saving devices. People designed them to perform tasks that were already being performed, albeit less efficiently. I believe AI will take on tasks we didn’t imagine it doing, and perhaps even tasks that didn’t exist before AI dreamed them up.

Implications for Investing

If readily available, quantitative information about the present doesn’t hold the key, investment superiority has to be found in things like (a) correctly judging the import and implications of that information, (b) assessing qualitative factors such as management effectiveness and product innovations, and/or (c) divining companies’ futures. By definition, few people are highly superior at performing these non-quantitative tasks – put simply, few possess exceptional insight. Just as indexation eliminated the jobs of a whole bunch of active investors who didn’t add value and earn their fees, AI is likely to raise the bar still higher, pushing out people who can’t do as good a job as it can of (a), (b) and (c).

So, Bottom-Line Me: Is It a Bubble?

While I mentioned it in my December memo, I want to point out again that some AI revenue is currently “circular” in nature, derived from AI companies buying from each other. The chain of revenue has to ultimately rest on end users paying for real economic value, and while that’s increasingly the case, the question of how much revenue is circular remains an open one.

The bottom line for me is that AI is very real, capable of doing a lot of work that heretofore has been done by knowledge workers, and growing extremely rapidly in terms of applications. What we see today is only the beginning. As I mentioned above, if I had to guess, I’d say its potential is more likely underestimated today rather than overestimated. However, that’s not the same as saying AI investments are on the bargain counter or even fairly priced. Thus, I’ll end by carrying forward my advice from Is It a Bubble?:

Since no one can say definitively whether this is a bubble, I’d advise that no one should go all-in without acknowledging that they face the risk of ruin if things go badly. But by the same token, no one should stay all-out and risk missing out on one of the great technological steps forward. A moderate position, applied with selectivity and prudence, seems like the best approach.