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AI Engineer NYC 2026 Main stage · 6 min

AI and the Future of Fundamental Investing

Jack WoodruffKepler
Vinoo GaneshKepler

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.

The baseline rises; a divergent view still matters

5:55:41 ↗

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.

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

5:57:42 ↗

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.

Use the agent to choose what deserves a closer look

6:00:17 ↗

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

Timestamps follow the original livestream. Select a time to watch that moment. Transcripts are machine-generated with reviewed name and terminology corrections; occasional recognition errors may remain.

5:55:41

Hi, I'm Jack Woodruff.

5:55:44

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

5:55:50

fund based in Greenwich, Connecticut. Before founding Candlestick, I was a partner and

5:55:57

portfolio manager at Surveyor Capital, which is a division of Citadel.

5:56:02

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

5:56:08

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

5:56:12

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

5:56:17

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

5:56:24

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

5:56:29

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

5:56:36

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

5:56:43

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

5:56:50

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

5:56:58

everybody has and then how you synthesize that data is going to change completely.

5:57:05

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

5:57:10

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

5:57:18

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

5:57:24

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

5:57:31

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

5:57:35

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

5:57:41

Very cool.

5:57:43

So at Citadel, you had 18 people covering 400 stocks.

5:57:46

And at Candlestick, you had 30 stock pickers.

5:57:48

Could one great analyst cover all 400 by 2030?

5:57:53

So the biggest issue is humans are still the

5:57:58

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

5:58:05

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

5:58:13

Investment management is a time management exercise.

5:58:16

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

5:58:22

I'd probably know it better than anyone else in the world.

5:58:25

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

5:58:30

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

5:58:35

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

5:58:43

So, you know, what can AI do?

5:58:47

Can AI help me cover more names?

5:58:50

Perhaps.

5:58:51

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

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because that work used to take me hours to prepare,

5:59:03

to get me ready for what it was.

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And now maybe an AI program can help me prepare a lot of that work

5:59:10

in five minutes, which used to take me six to eight hours.

5:59:14

So that allows me to do other things.

5:59:16

Again, it's a very competitive industry.

5:59:19

So it's not like if I get rid of all these people

5:59:22

or if I have five analysts, whereas before I had 30,

5:59:26

yeah, maybe those people can do more.

5:59:27

but now all of a sudden this opens up time

5:59:32

that I could spend doing either more proprietary research,

5:59:36

channel checks, more sort of like industry backdrop work

5:59:41

because again, like you're going to need to find these divergent views.

5:59:44

So it's like if I do the exact same thing I was doing before

5:59:49

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

5:59:51

well, someone else is going to be still working 16 hours a day

5:59:54

trying to find that extra thing

5:59:58

that's going to

6:00:00

make you money.

6:00:02

Because 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.

6:00:16

Very 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?

6:00:24

Well, it's funny.

6:00:26

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

6:00:29

You're not going to be reading transcripts.

6:00:31

I actually don't believe that to be true.

6:00:33

If you're truly close to a name,

6:00:38

you're going to still need to process a lot of that information.

6:00:41

Now, you might be able to get summarized versions

6:00:44

that can cut down the time it takes you to do those things,

6:00:47

but you still need to get that information sort of into your head.

6:00:52

we used to have a lot of analysts

6:00:54

that would be going through a lot of big data

6:00:57

and things like that

6:00:57

I definitely think that can be outsourced

6:01:00

and that will be super easy

6:01:02

I would be surprised if a lot of these guys

6:01:04

aren't necessarily doing that stuff

6:01:09

what I believe

6:01:11

the AI is going to do

6:01:12

or the agent or whoever it is

6:01:14

is going to be

6:01:18

pushing you in the right direction

6:01:20

to work on the right names.

6:01:21

Like I said, this is time management.

6:01:23

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

6:01:26

and that's where the winners are going to be,

6:01:28

is the people who can figure out how to use these tools

6:01:31

to get you to spend the right amount of time

6:01:34

working on potentially finding a divergent view.

6:01:39

Very cool.