Future of Discretionary/Fundamental Hedge Funds

Hey Everyone,

We all know that AI is literally revolutionizing a lot of things in the finance industry, so just curious to know what you all think about the future of fundamental hedge funds. Are quant funds going to outperform them in the long run? Will fundamental funds cease to exist in the future?

Suppose the current pace of improvement continues in the AI field. Don't you all think that the time is not far away when we can just give a bunch of industry reports, equity research reports of a certain industry to the AI and the AI will go through it within seconds and will make better assumptions, about a certain stock or industry, than human beings?
I know my question sounds stupid, but please share your thoughts.



Also, equity research reports suck for actual stock picking, the idea is to have a none-consensus view, not feed AI a bunch of views that aggregate to the consensus.

The fundamental landscape is generally in structural decline, but perhaps the term consolidation is better. The large, top firms have demonstrated why they should exist, even with the impact of quant/passive flows, they can monetise price distortions caused by them. We'll likely see the same trend continue, maybe at an accelerated rate over the next 5-10 years. More discretionary firms will die off, money will leave discretionary but the large shops with scale will continue to gain share and stay fairly stable.


don’t u think with consolidation, and through that a centralization of trading style (defined by time horizon, turnover, % idio), create a need for other firms to come in and offer a different product than the uber-levered, tight idio, high turnover active investor?

Also generally agree that, when I’m thinking about my career, I feel less worried about AI take over in LS investing (not the same in other products like rates/commods—Id generally feel more worried there). The reason being that a human can understand the true ins and outs of a business better than a machine. Like yes likely in a few years AI can build just as robust of a model as any podman, but even still it’s the assumptions that go into that model that I believe a human will have an edge in over the machine for the long foreseeable future. Also, to the extent an AI is controlling a majority of flows, HFTs will just back into future implied moves by the AI and front run all their moves cause they’re predictable and codified and the AI “investor” (not trader) likely will have some super high friction costs. Nobody knows when the coked out Pod PM is gonna YOLO his book on a random shitco cause he’s already down 3% ytd, which actually enhances “market efficiency” because the flows are more random than they’d be under an AI regime


Don’t see how you feel like l/s is more at risk than commods. Commods you physically deliver a product. You can’t automate that because it’s not nearly as liquid as an equity + requires the actual real world practicalities of delivery.

Your concern should be for any publically traded security. A macro swaptions RV trader or a physical natural gas trader is leagues safer from the dangers you describe. In the future illiquidity + low-competition auction markets are key.


Do you remember that scene in the matrix where agent smith goes on the vagaries of perception rant with Neo where he is trying to understand why Neo continues to persist? Quant / Machine Learning / AI has a hard time deriving nuances, edge cases, or simplistic human values. They tend to extrapolate the past where relationships of data may be spurious correlations or in fact random and break down in new systems. Agent smith fails to understand that Neo chooses to persevere. These data systems are trying to model human behavior which is very complex and nuanced and will not always work. A human element will always be needed. 


So, it's like fundamental analysts will survive, but how they do research will change, right? Maybe they will do their fundamental research and then use some basic quantitative techniques to back/support their assumptions. Basically, these new quantamental funds are the future, what do you think?


Yes precisely. We will have to work with the machines. Neo teams up with them to defeat agent smith if you remember. 

Most Helpful

So AI can do everything except the smart things you guys do… Do you realize how biased that sounds? You guys are so smart that of course nothing you do can be automated but artists are all idiots. You stop believing nonsense like this when you do literally any job outside of the industry. The reality is any paid profession has nuance and skill and sure you guys get paid a lot but I don’t believe it’s because the job actually is that difference in terms of knowledge or reasoning ability required if you think about it on a bit more grand of a scale and put intelligence in perspective. 

chess players thought what you guys think now. So did artists and writers. Then programmers. So did videographers and physicists. I really think a lot of you are in for a surprise because a lot of what you do is trivial. A lot of what you do is protected right now by limitations in multi-modal input and agentic behavior for these models. You won’t feel the way you do when these constraints go away. 

there’s no reason why humans are the end of history or why we are the last species on the planet. Think on a scale of intelligence in the universe with maybe like atoms on the left hand side then like rocks then plants then like bacteria then apes then us on the right. On this scale, I don’t think we are all that much smarter than apes and the distinction between the intelligence of humans looks small. But think of how much a relatively small amount of intelligence produces for a species - it means we essentially became the dominant species on the planet. And I don’t think there’s a philosophical reason why the scale of intelligence needs to stop with humans. 

AI speaks the language of autism and doesn’t differentiate between what is “easy” and “hard” for humans because of inherent abstraction or nuance. Like an autistic kid I think abstract philosophy and booking a flight are equally easy or hard. It’s just as easy or hard for it to understand how to book a flight versus reconfigure a Kubernetes server or do an LBO model

a lot of the assumptions people on this site hold are not really true for modern deep learning. For traditional statistical learning, increasing model parameterization decreases out of sample performance due to overfitting, but for deep learning there’s “double descent” phenomenon (increased parameterization increases, then decreases, then increases then decreases performance, which is bizarre). People used to be like “bro idk if we can have 3 variables in this regression what about overfitting” or like “bro idk if we can have a polynomial of high degree here bc overfitting.” Have you stepped back to think about what these parameter counts mean when people say “X model from OpenAI has 1 billion parameters”? How did we go from thinking a few variables was overfitting to a billion parameters being ok? This is honestly such a paradigm shift in how we think about how machine intelligence works that it’s mind blowing because it implies that the traditional problem of overfitting is no longer a hard constraint provided you work around it in an intelligent way. 

also most people’s intuitions about modern machine intelligence expressed on this site are based on the approaches from 10 years ago. So I guess they are intelligent opinions but they are just totally outdated. It’s actually not at all true that you need large samples to learn from in order to generate domain specific intelligence. That’s just not how modern models work. The modern approach is to take a foundation model with massive data then adjust it to a domain specific use case with parameter efficient fine tuning approaches and there’s actually little evidence that suggests you need more than a few hundred samples. 

even complex phenomenon can be captured by these models and they don’t need a million examples anymore. Have you ever really thought about how these deepfaked onlyfans models work? The implications of that are actually insane because historically you could not get a consistent image of one person from a diffusion model but now you can, which is what allowed these AI influencers to now exist. It can with one example of a mode produce enough content to fool all the simps that pay for onlyfans. The implication here is these llm models can understand specific context provided (eg a base image) and work from within that context, though now it mostly can only do this with images. 

Sure, right now you need a human to babysit a model and chat with it. It can’t easily take continuous input from the user’s screen or from external sources because of the costs of running the model GPUs at inference time. (A continuous feed of external input would be too costly basically since each update would essentially be another query.) step back and think about this - a lot of the value of humans in many domains will exist primarily because GPUs may be too expensive so we need a human in the loop to help allocate GPU usage. But this constraint will not exist forever. Eventually multi-modal and agentic systems will exist that are open source and can be run on rentable GPU units. When this happens a lot of things will change. 

tldr I basically think most highly paid knowledge workers should just buy NVDA, MSFT and similar stocks as a career hedge. perhaps kind of for similar reason as someone might buy gold as an inflation hedge. The scenario where a lot of workers are redundant imo is a real possibility and maybe it doesn’t materialize but if it does I would at least buy a hedge. 


Finally someone who gets it.

ML has existed for decades and wasn’t a problem. The reason was because it needed sizeable datasets to do the tasks that LLM models can do today. The difference is now LLM models can look at one example and produce excellent results. 

Even if the job isn’t affected, this is an HF forum, imagine the consequence on any large auction-based market. Volatility is going to get destroyed. Alpha opportunities are going to get harder to find. This is a secular trend, not a rule, and has been a trend for the past 40 years. 


Exactly, modern ML models are quite data efficient with retraining the final layers only. The training data is not really an issue anymore. AI and LLMs are not really intelligent in any way, but they do not need to be to do the work of most employees in big firms. 99% of professional white collar jobs in America don't involve doing anything very complex, even if the jobs are hard to get.

However I also think that the jobs won't go anywhere, because the real reason those jobs exist is to justify the pay of people above them. Once a firm has a certain org structure and way of operating it's very hard to change and no manager has any incentive to change it. Even if all the employees run LLM queries for 10 minutes and fill their schedule with unnecessary meetings every day, those jobs will stay.

Instead of buying tech I would just buy VOO or QQQ, which are highly tech weighted and have much better terms on portfolio margin and leverage than any single stock.


It depends I think. What really matters is the purpose of the job—absolute return or image/presentation.

IB/MBB will never get hurt because their business model depends on 5 polished ivy kids doing “deep research.” 

HFs and public markets will because anybody has the freedom to deploy the tech and make a profit.


People need to understand that AI, and other emerging technologies, are simply tools to be utilized to help make a decision based on current information provided within the input field.

No, it will not take over, anything. In fact, it will do the opposite - it will require more deployment of experts in the field to help reprogram the tools to customization to help firms accurately interpret the data provided.

Also, errors will happen. I’ve yet to meet a developer who was error-ridden free.

No pain no game.

This is false. When has technological development ever caused more jobs for an industry? 


Generally the claim is always that we’ve adapted to every technological and social shift just fine so we will do fine here. I actually don’t think we have. 

The reality is that male employment as a percent of working aged males in the US is basically TODAY as low as it was in the Great Depression and essentially has been in steady decline over the last 50 years. it’s just not true that technology or social changes can just create infinite jobs. The only reason why unemployment hasn’t increased overall is that the percentage of women working has increased over the same time period and because the denominator of published unemployment rates stats are fudgeable, but when you remove this effect it’s clear that the least motivated or capable or lucky (or whatever it is) men have increasingly and consistently left the labor market. The published statistics on unemployment are extremely subjective. The more objective measure to compare now versus decades ago (which is what you need to do to evaluate the claim that we haven’t had changes to employment from innovation and change) is to look at the employment rate and not the unemployment rate and to keep the denominator as the group of people that have consistently been in the labor force over the same time period - imo this is men 20-45 or so. The reality is that people don’t actually do well after labor market shifts in the modern era and they typically do not reskill that effectively in the aggregate. There just isn’t an infinite demand for labor. If that claim were true I wouldn’t expect to see that the percentage of able bodied men working would have declined as steadily as it has. So if your faith in your employability is based on history imo this doesn’t actually make much sense. 


We need to start approaching the level of AGI for fundamental investing to be truly disrupted. At that point, almost no job is safe but society will adapt and new jobs will be created. But before AGI, there will continue to be a ton of interesting, useful applications of AI which essentially will speed up the research process/add efficiency.

On when we will approach AGI is up for debate. However, what I’m certain of is that when it comes, it won’t be a result of linear progression. A single or multiple divergent breakthroughs will need to happen. Feeding current technology more information is likely not going to get us to AGI. There is growing evidence that suggests that after a certain point that models do not improve non-specific, general reasoning ability with more data.

As a side note, if significant breakthroughs do not occur in the next few years, I believe AI driven valuations in the public and private markets will drop significantly once the markets realize the non-linear nature of AI capability growth. This isn’t a prediction but more so a bear case outcome.


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