October 13, 2026Main stage2 min read

AI and the Future of Fundamental Investing

Jack Woodruff sees AI changing research preparation and analysts’ roles while leaving a central investment problem intact: deciding where limited human attention can uncover a divergent view.

Jack WoodruffKeplerSenior Advisor

Vinoo GaneshKeplerCEO

From the conference livestream · 5:29:04–5:35:06 on YouTube. The player starts and stops at this session’s boundaries.

5:29:05 ↗

The baseline rises; a divergent view still matters

Faster synthesis raises the baseline for an investor. It does not, by itself, produce a view the market has missed. Jack Woodruff draws on founding Candlestick Capital Management and managing portfolios at Surveyor Capital to explain where he expects AI to change the work of stock picking.

Investors still assemble a mosaic of evidence. In Woodruff’s view, returns still depend on finding a divergent view. What changes is the baseline information everyone can obtain, how quickly they can synthesize it, and the roles built around that work.

He expects the jobs of portfolio managers and analysts to change substantially, but avoids drawing a settled five-year organization chart. The tools are still new to many users. Seeing early adoption is not the same as knowing which working arrangements will win.

The distinction matters for evaluating a research assistant. Faster preparation may become table stakes rather than a lasting advantage. The question is what an investor can do with the time and understanding that preparation creates.

5:31:06 ↗

Four hundred stocks still have to fit into someone’s attention

The interviewer makes the scale question concrete: Woodruff once had eighteen people covering four hundred stocks, and later thirty stock pickers at Candlestick. Could one exceptional analyst cover all four hundred by 2030?

Woodruff returns to the human bottleneck. Information still has to get into the decision-maker’s head. Investment management, as he frames it, is a time-management exercise. Spending every hour on one stock might produce extraordinary understanding, but that is little use if there is no attractive opportunity in the name. Covering more stocks creates more possibilities while reducing the attention available to each.

AI can change that allocation. Woodruff offers an illustrative contrast: preparation that used to take six to eight hours might take five minutes. He offers those times as a hypothetical, rather than a measured result. It could expand coverage or raise the analyst’s baseline knowledge before deeper research begins.

But a competitive investment business does not stop once the old workload gets cheaper. Time can move into proprietary research, channel checks, and understanding an industry’s context. If one team finishes its previous work in two hours, another may spend the rest of the day searching for an additional insight.

This is why a staffing calculation alone misses his argument. Fewer people might produce the same initial research, but the competitive opportunity is to ask what additional research the team can now afford to pursue.

5:33:40 ↗

Use the agent to choose what deserves a closer look

Asked which junior-analyst tasks would look absurd in five years, Woodruff rejects an easy answer: that analysts will no longer read annual filings or earnings transcripts. An investor close to a company still needs to process its information. Summaries can shorten the work without eliminating the need to understand it.

He is more willing to hand over the labor of processing large datasets. That can remove substantial preparatory work, but the useful destination is more specific than producing additional summaries. He wants the agent to point the analyst toward the right names and the right questions.

The system should help decide where an extra hour of research is most likely to matter. A bigger pile of summaries can consume the time it was meant to save. Pointing the analyst toward a promising name or an unresolved question gives that time somewhere useful to go: the search for a divergent view.

Full transcript 88 timestamped passages

Machine transcription with reviewed terminology and names. Some recognition errors may remain. Timestamps refer to the livestream; subtitle downloads are timed to the session clips.

5:29:05Hi, I'm Jack Woodruff.

5:29:07I am the founder and former CIO of Candlestick Capital Management, a long short equity hedge

5:29:13fund based in Greenwich, Connecticut. Before founding Candlestick, I was a partner and

5:29:20portfolio manager at Surveyor Capital, which is a division of Citadel.

5:29:26Very cool. Thanks, Jack. So the reason we did this this way is we've had this conversation

5:29:32before, and I think this is one of the most enlightening folks to chat with, given that

5:29:36Jack has spent his career picking stocks in an environment that's changing more than it

5:29:41ever was before. So we'll treat this really as a fireside shot. So Jack, paint the picture for us.

5:29:48What does fundamental investing look like in the age of AI, say five years from now?

5:29:53Well, it's hard to say. Like long-term stock picking, I don't believe is really going to

5:30:00change much at all. There's always been a mosaic and there's always been alpha creation by finding

5:30:07a divergent view. So that isn't going to change at all. I think that that still will stay the same.

5:30:13Now, how people synthesize all of the information and where table stakes go to in terms of what

5:30:22everybody has and then how you synthesize that data is going to change completely.

5:30:28The traditional roles of, say, a portfolio manager or an analyst are going to be completely thrown

5:30:34upon their heads. And I still think it's too early to say what that's all going to look like

5:30:42as these tools are just so new, especially to the users. You've got a lot of people doing

5:30:47a few things, but it's just not clear to me that it's like we're there and oh my gosh,

5:30:55it's going to be these new things. We have a bunch of other things to kind of talk about, but

5:30:59But I think it's going to be super fascinating to watch how it all plays out.

5:31:05Very cool.

5:31:06So at Citadel, you had 18 people covering 400 stocks.

5:31:09And at Candlestick, you had 30 stock pickers.

5:31:12Could one great analyst cover all 400 by 2030?

5:31:16So the biggest issue is humans are still the

5:31:21I mean, like at the end of the day, you have to get the information into your head.

5:31:28So it's really all about sort of how man and machine can work together.

5:31:36Investment management is a time management exercise.

5:31:40What I mean by that, if I spent all of my time working on one stock, that'd be great.

5:31:46I'd probably know it better than anyone else in the world.

5:31:49But if there wasn't anything to do in that stock, there isn't much to do.

5:31:54Well, if I do two, well, then I have half as much time and so forth until I go.

5:31:59So you start getting diminishing returns on how much time you could spend on any particular stock.

5:32:07So, you know, what can AI do?

5:32:11Can AI help me cover more names?

5:32:14Perhaps.

5:32:15Maybe it allows me to raise the table stakes on what I just kind of like have as a general knowledge

5:32:23because that work used to take me hours to prepare,

5:32:26to get me ready for what it was.

5:32:28And now maybe an AI program can help me prepare a lot of that work

5:32:33in five minutes, which used to take me six to eight hours.

5:32:38So that allows me to do other things.

5:32:40Again, it's a very competitive industry.

5:32:43So it's not like if I get rid of all these people

5:32:45or if I have five analysts, whereas before I had 30,

5:32:49yeah, maybe those people can do more.

5:32:51but now all of a sudden this opens up time

5:32:56that I could spend doing either more proprietary research,

5:32:59channel checks, more sort of like industry backdrop work

5:33:04because again, like you're going to need to find these divergent views.

5:33:08So it's like if I do the exact same thing I was doing before

5:33:12and it's like, hey, wow, I can get my work done in two hours,

5:33:15well, someone else is going to be still working 16 hours a day

5:33:18trying to find that extra thing

5:33:21that's going to

5:33:23make you money.

5:33:26Because just doing this stuff is obviously like what we used to pay an analyst to do a year or two ago is not what we're going to be paying an analyst to be doing in a year.

5:33:40Very cool. And on that, what is something today that a junior analyst does that you think will be absurd if they're still doing in five years?

5:33:48Well, it's funny.

5:33:50It's like, oh, you're not going to be reading 10Ks.

5:33:53You're not going to be reading transcripts.

5:33:55I actually don't believe that to be true.

5:33:57If you're truly close to a name,

5:34:02you're going to still need to process a lot of that information.

5:34:05Now, you might be able to get summarized versions

5:34:08that can cut down the time it takes you to do those things,

5:34:10but you still need to get that information sort of into your head.

5:34:16we used to have a lot of analysts

5:34:18that would be going through a lot of big data

5:34:20and things like that

5:34:21I definitely think that can be outsourced

5:34:24and that will be super easy

5:34:26I would be surprised if a lot of these guys

5:34:28aren't necessarily doing that stuff

5:34:30but

5:34:32what I believe

5:34:34the AI is going to do

5:34:36or the agent or whoever it is

5:34:38is going to be

5:34:42pushing you in the right direction

5:34:43to work on the right names.

5:34:45Like I said, this is time management.

5:34:47It's all about how do I know to work on the right things,

5:34:50and that's where the winners are going to be,

5:34:52is the people who can figure out how to use these tools

5:34:55to get you to spend the right amount of time

5:34:58working on potentially finding a divergent view.

5:35:03Very cool.