← How I Invest with David Weisburd
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- [00:00]
A
David, you're a founder and managing partner of Bloomberg Capital and you're going all in on AI agents. Why is now the right time to do that?
- [00:08]
B
Agentic AI is liberating us from the drudgery of mundane work. I'll give you one example, cut and paste. How many of us do that over and over and over and it's so mind numbing and it's not very productive. So the agent, the agentic worker for you can do a lot of that kind of stuff. The logging in, the security protocols, the money transfer, it goes on and on. And that we're just at the first inning of this game. And in the next 10, 20 years, I predict that the work world will be entirely changed.
- [00:43]
A
There's essentially two narratives in the market about AI. One is it's going to take everyone's job and everybody will be jobless. And the other one is that there's going to be ten times more jobs. Where's the ground truth?
- [00:55]
B
You talked about ground truth. That's why I started with agriculture. Most people don't know this. I think it's a fun fact. Remember 250 years ago, 95% of Americans worked in agriculture or the rural economy. Today, do you know what that percentage is?
- [01:08]
A
Less than 1%.
- [01:09]
B
It's about that. I think it's between 1 and 2% and falling. So all those people, quote, unquote, lost their jobs. And yet we produce far more food, much more nutritious, much safer, with far fewer accidents in the farm world. And that cost of food as a percentage of our worker daily wages is much lower. And we export to the many countries in the world, so we do a lot more with less productivity is the name of the game. Some people will lose jobs, but most likely to say a cliche, you're more likely to lose a job, not to an AI, but to someone else who is using an AI when you're not.
- [01:51]
A
Dr. Alex Wisner Gross previous guests said if you're not at the table, you're on the menu when it comes to AI. So if you're not integrating AI, you're going to be disrupted by AI. I think about it in two different frameworks. There's David Deutsch's whole concept, the Beginning of Infinity, in this book. He argues that innovation is infinite. Why? Because every time you innovate and every time you create something new, it can now combine with everything else.
- [02:16]
B
Yes.
- [02:17]
A
So as you're creating more new paradigms, more new structures, more companies, they now have to interact in other ways. And combine in new novel ways. So the theoretical limit to innovation is infinite. The second one is actually instead of going way into the future, going way into the past. From an evolutionary psychology basis, human beings for millions of years have been wired to be status seeking and to be zero sum when it comes to status. Therefore, in if everybody can now afford a Rolls Royce, there will be now something that's 10 times more prestigious than that Rolls Royce. People will argue that resources themselves will become infinite. We'll see maybe in 10, 20, 30, 40 years. But in the next five to 10 years. Human beings are not so easily satiated in that if they check the box and they get this livable wage, they're suddenly happy. We may see what Elon Musk calls extreme abundance and and revolution at the same exact time.
- [03:15]
B
I agree, at least in concept, that the innovation cycle is potentially infinite. The other one about human beings. I agree that human nature doesn't change very fast. Technology changes fast, human nature doesn't. But I'm not worried about status seeking people competing for too many Rolls Royces. I'm worried about the fact that there are for example, 700 million people on Earth that have zero electricity. There are 4 billion people on Earth that have less than 4 hours a day of electricity at their disposal. Because it's so unreliable or so costly, we have a lot of room to help the rest of the world move up. I'm very excited and I'm very optimistic about the potential because who wants waste? No one. What the agentic AI revolution offers us, ahead of us in this bright future, this golden dawn to my mind, is less waste, more productivity. Any good economist will also tell you that the only way wages can rise is if the workers, the employees, are more productive. Now if we give them tools that make them more productive, guess what? That's a wage unlock. People will become much more valuable to their employees if they're doing more work. Give them better tools, give them better machines, they can be paid more. The sewing machine example, when a person sewed everything by hand X level of productivity, give them a sewing machine which costs capex capital investment, they're much more productive, they can pay more because they generate more. So there's a lot of post modernist neo Marxist ideology that is getting infected in all of this stuff. I'm much more of an optimist because I think that most people want to do better for themselves and their families. Not in an ego driven way, but just because they want to achieve. They want to do something meaningful and something that's meaningful and productive. Serves other people.
- [05:10]
A
So let's go from philosophy to day to day execution. You're specifically focused on B2B agents, which are agents that transact within businesses and between businesses. Why are you focused there?
- [05:21]
B
This is where huge volumes of waste occur. Let me give you one example. We just were showing this company at our LP meeting yesterday in New York. This is a company called Overview. They're based in the Bay Area. They're former engineers from Tesla. They were fascinated as manufacturing engineers by the factory floor and the defects and how to reduce the defects and how to increase the output of the factory line. And they realized that there were cameras on all the factories, watching the production and trying to glean knowledge. But they improved the kind of camera, hardware, improvement, semiconductor. We talked about that. And they improved the AI and they put the AI with the computer vision and they took the production of this. I won't name the company, but it's a big medical device manufacturer, $100 billion value company. They make little tiny surgical rings among many other devices, the diameter of which is as thin as this card, less than a millimeter. Now when they're producing them, they have to look for the defects because you can't have a defective surgical ring if someone might die. So they used to be able to produce 10,000 a day because the inspection, the quality control was very demanding and slow and painstaking and often manual move forward. With smart, intelligent cameras, they're able to increase the production to something like 166,000 thousand a day. 16 and a half fold increase. And they improve the quality so that the defect detection rate, which was 94% is now 100%. So 16 and a half times better volume and at least 6% fewer defects to a point of perfection. So that's one example. I can give you so many, so many more in every field we can see so far. You said, why is B2B so interesting for agentic AI? We cannot yet find an area where agentic AI will not improve the state of play.
- [07:19]
A
It's interesting because one of the first real use cases of AI was radiology.
- [07:26]
B
Yes.
- [07:26]
A
So radiology AI quickly became better than leading radiologists to tell whether there's cancer within a patient. And paradoxically, the need for radiologists, I think went up something like 10 times because so many people were coming in and using it and the cost was being driven down. It's one of these unexpected second order effects of AI disruption and the bringing down of costs.
- [07:51]
B
There's a law in economics and it's about energy. The less expensive energy gets, the more the demand is for it. People would think oh, if you're more productive then you might need less. But no, if you make it more efficient, people will use more and more of it.
- [08:07]
A
I hear a lot about agentic AI. I hear a lot about B2B agentic AI, hear about OpenClaw and consumer agentic AI. And when I ask people personally one on one what agents they're running, almost nobody but developers are running agents. How do you reconcile Expert calls have
- [08:25]
C
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- [10:12]
B
First half of the first inning like they just sang the national anthem. Really early in the whole process we just saw some statistics that I believe where it was experimental last year, it's being scaled into production this year. One of our advisors, Marshall Lux, is on the board of a number of Fortune 500 type companies and he said, david, the board members are leaning forward. They're demanding it and it's happening two ways. It's happening from the board down with a mandate. Do this, bring AI into our operations. We know that our competitors are doing it or they're going to do it. We have to be competitive. And it's happening from the bottom up. Often it's a precocious engineer or a particularly young person who's just been exposed to it at school. That's happening. We saw a little bit of that in the mobile phone revolution. That was more bottoms up, where the top was resisting mobile phones in the
- [11:05]
A
office and the the corporations were giving them blackberries and the employees were asking prices.
- [11:09]
B
You know the story. So let me just do a data set for you that I think will help explain how big this market is. It turns out that the average white collar worker, knowledge worker, somebody works in a bank, an insurance company, a lawyer, a consultant, marketing pro, a corporate executive, all that crowd, most of us people in podcasting world, those folks cost the employer about $170,000 a year. Now 160,000 of that is wages and benefits. 10,000 is spent on software tools that we use every day, whether it's Microsoft plus, Google plus, Oracle plus, Amazon Web Services. All of that together is a software industry sells about 1.5, I think trillion dollars a year or 1.7. And their market cap is something like 17 trillion. Okay, that's just for the software tools. Everyone's been fighting over that for 30 years. The big companies right now, McKinsey says that Agentic AI, within a very short period, I think it's within a decade, is likely to disrupt, transform, maybe lose 57% of those work hours. It'll be transformed from humans doing it cut and paste boring stuff, to an agent doing it seamlessly, silently, frictionlessly, 24, 7, and at very low cost. So that 57% of that 160,000, that's about 100,000 of spending that's going on wages now that can be transformed into agentic agents. So that will probably compress. Just like we saw print media, they used to say a print dollar turned into a digital dime. So there'll be some compression. I don't know the ratio, but I think it's safe to say that the agentic agent software market, when deployed within say 10, 20 years, will be two or three times larger than the existing software industry today.
- [13:08]
A
You're saying 20 or 30,000 of that will be charged to the customers and 70,000 will be productivity gains for companies.
- [13:18]
B
Yes, sir. And there will be whole new categories of jobs created, prompt engineers. That's a new kind of job. Every large corporation that I know worth its salt has a chief data officer and a whole team and they're grabbing data. I remember the chief data officer of Walmart telling me we will buy every kind of data that you can offer us. They want everything.
- [13:39]
A
It's still unclear to me how we go from today where almost no one's using agentic AI on a TAM basis and a future where everything's run agents. No one's really given me the game plan how that breaks down over the next couple years. I know you can't see the future but what are some low hanging fruits on where companies are going to start incorporating agentic AI and how is that going to happen?
- [14:03]
B
I'm just one little story and I already have six companies that are in this. I'll call it the loop of agentic commerce and the requisite financial infrastructure that goes along with that. That's a whole new layer that needs to be built. Let me start with Truly you. I sit on the board of this company since I've known you pretty much they're based in came a little bit later but they're based in Vancouver, Canada but they sell worldwide. They sell in around 100 countries on Earth and they can go to many more. They are identity management experts leaders in the world. Identity management is known by its colloquial name KYC Know your customer. This is important for onboarding for compliance banks. Airbnb, Stripe Square gambling companies, Coinbase. All these kinds of companies need to have this kind of onboarding. And are you who you say you are? Then we go to the new era kya Know your agent. Remember I gave you a moment ago the example where I'm going to interact with you through a third party bank. We're each going to have agents, one or more operating for us. How do I know that your agent is who it says it is? So this is a whole new level of identity management that needs to be addressed. Trulio is in the lead among companies helping build that whole extra layer. Then there have to be a whole nother set of data and I'll give you another example. There's an amazing company called Telen AI. They are with the hairy audacious goal, the bhag the big hairy audacious goal of one click audit.
- [15:38]
C
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- [18:34]
A
So imagine that financial audit.
- [18:36]
B
Financial audit. Financial data is very well structured, so it's labeled well. So it's very suitable for AI interpretation and manipulation. Manipulation in a good sense. And the future is being written right now. For example, they just had an announcement that Citroen Cooperman, a top 20 auditing firm, just purchased, I think by Blackstone for about $3 billion, which has hundreds or maybe thousands of private company accounts that they work on. And they do audits every year. They are auditing processes one by one by one. There are probably a hundred different processes in a typical audit, something like that. And just one of them, quality control, already has an agent that talent has developed and is being put into production. It's already saving citron cooperman, one firm out of hundreds or thousands around the world, 10% of the spending. So 1.2 million out of the 12 that they spend on quality control is already being saved. Next month, in June, they're going to add a second notch to it that will save 25% of spending. So three out of the 12 is being. That's just in a couple months. This is happening very fast. So you say that many companies are not using it. I beg to differ. They're not using it a lot that you hear about on posters or billboards or maybe other podcasts. But I'm letting you in on the secret. It's happening extremely fast. And just. I'll give you the B2C example. Look at the adoption of ChatGPT. Fastest product adoption ever, I think, in human history. Right? It went to like 100 million users.
- [20:11]
A
Yeah, it was literally on the graph. It's up and up and up.
- [20:15]
B
Really. So we're living in revolutionary times in a very good sense. You talked about revolution. In the bad sense, I'm talking about making humans more productive. And I'll go back to again, historical analogy, because it's hard to always understand what's happening in the present tense. But Our mothers, our grandmothers, our great grandmothers used to have wash by hand before the washing machine came around. Think how much time that liberated for people, mostly women to do better, more creative, higher productive things. So talent is automating the auditing process. Auditing is limited but the data that it will be surfaced from this is unlimited. It's kind of that back to the infinite innovation loop. If you're an M and a banker and you have a client that wants to do an M and A deal in the middle of the year, say what are we in May? So May 21st you want to do the deal. Well usually audits are done at the end of the year. So you'd have to do a whole special audit for May with the audit ability that Talon is doing. That's it enables daily reconciliation of all the numbers. They flow in from the Darius systems of record. They are tallied, it's all labeled, it's all structured. We can have you have a daily audit. Now with that daily audit the management, the corporate over overseers are seeing data they would never see except maybe quarterly or annually. So they can get information earlier and they can make decisions faster. It's also going to do something very interesting for the world of credit. And this is where Truly you and Telan, two companies in the portfolio will be able to cooperate. Telon is developing this set of data from all the accounting firm's clients. Most of them are private companies. They don't expose their data. This is not going to be in the open Source World of LLMs. This is Mr. Smith's construction company private financial information. He doesn't share that with the big LLMs. So Talon will create this organized daily reconciled audit ability and information database data lake if you will on an opt in basis this construction company can share with peers and on a blinded basis put their information out in return for seeing what the benchmarks are from competitors. So I will see if I'm spending too much on marketing, if I'm paying too much for plywood in that example. This has never been possible before. How will Truly you use it? Trulio is the identity management company up in Canada. They are going to use that to create the world's first global credit bureau for small businesses. Public companies have to disclose their financials so that's noble for banks and everybody else. Private companies don't and counterparty risk is a big issue especially in international trade. So Talon and Trilio together are going to create this unique irreplaceable set of data private company financials and they're going to make it available for counterparty risk analysis and management lending. It'll help increase volume of trade in the world. Think because you'll be able to say, I want to do business with this guy because he's legit, he's solid, et cetera, et cetera. And so it should unlock some credit around the world, make more business available. So I'm super excited.
- [23:31]
A
There's so many new paradigms going on with AI, the CFO of Lagora on and that to create all new metrics, they created new terminology, new lingo. One thing that's not changing is what makes businesses valuable and that is network effects. And one of the things I see the top AI companies doing is not only gathering the data, doing the processing and efficiency gains, it's building on these, these, building on these network effects and building moats.com pound we talk about the
- [24:02]
B
data flywheel that's enabled by agentic AI. Here's how it works. Say you're a startup company and you have some data that's proprietary. You mentioned radiology. Maybe it's that, maybe it's construction data, maybe it's mining data. The data flywheel. And the data flywheel starts with a company that has perhaps no data or it's got some proprietary data set. The first time a user interacts with that data, as small as it is, or if it's substantial, the data is increased because of the user interaction that gets fed back in, that creates new data that's fed back into the model. That's called reinforcement learning. The reinforcement learning improves the model, then the model delivers better results, and then there's more users and more data created and so on. It's a positive flywheel. So that gives a new meaning to the term first mover advantage. First mover advantage used to be talked about in network value or in brand recognition, but here it's in the fact that if you start making a data flywheel, your data flywheel is going to be earlier than the others and it's harder for them to catch up because your data is constantly improving.
- [25:12]
A
And everybody comes to the rational decision, who am I going to use? I'm going to use the person with the best model, that has the best data, they feed their data. And so it can compound.
- [25:21]
B
There you go. So that's why we're extremely excited, because it seems to apply to every domain we see.
- [25:27]
A
And that answers the question, how do these application companies compete or defend against these large LLMs?
- [25:35]
B
Yes, that does most people think, oh, that large LLMs are going to take over everything? No, they're going to become, I think a little bit more like aws, Azure and other cloud vendors. They're going to be a utility. The LLMs. They'll do a lot of things and they're great and I want to own a lot of the stocks and so on. But I believe that there's a lot of room for proprietary data set vertical experts because the future of agentic AI is about understanding process. And process can be very complicated and often process combined with a proprietary data set is a weird animal all of itself and it's not as easily exploited by a generic LLM model.
- [26:18]
A
In full disclosure, I'm early investor in Anthropic and I actually think that the most Pareto optimal strategy for them to do is to avoid going via verticals. Obviously I'm conflicted. I'm in other competing companies that are verticalized. But the reason for that is the number one risk for Anthropic and OpenAI. The number one risk is the government and the definite being defined in monopoly antitrust. And by the way, this is not a new phenomenon. Google's number one risk.
- [26:47]
B
Correct.
- [26:48]
A
Was, was that as well. There's a famous Peter Thiel ism which is the reason Google has so many different product lines is to confuse the regulators that they don't just dominate the one market which is Google search. I think something like at one point more than 95% I think it was even higher of their revenue came from search. But they had all these businesses. Why. And Peter Thiel's theory was it was just to confuse people that they were all these different companies so no one would go after them. Yeah, but the leader.
- [27:14]
B
We're not the leader in all our segments is what they say.
- [27:16]
A
Yeah, yeah, we're. We have strong competition of all our segments. Exactly. Obviously Dario and Sam Altman are, are, have huge ambitions going after all sorts of different sectors. Maybe in the very short term it's optimizing on revenue. In the long term it is not a wise strategy.
- [27:32]
B
Well, I won't try and get in their mind they're doing great as they're doing. I do think, and I think we agree that there's a lot of room for substantial companies to be built. The next Fortune 500 companies are being created right now and many of them will be in vertical domains where they are masters of complicated processes that are idiosyncratic. They're arcane knowledge sets and they often come associated with specific proprietary data. One more thing that is that there's A whole nother realm. Most people don't talk about it very much. And that is large quantitative models. These are not LLMs, these are not large language models. These are models about physics and models about mining and geology and protein folding and all kinds of other things. There are many different kinds of algorithms. Most people are just stuck on a certain kind. But there is room for, for folks with unique algorithms and as well as unique data sets to do very well in this bright future.
- [28:31]
A
Demis Asabas, who's the equivalent of the CEO of Google's AI, famously won the Nobel Prize for the alphafold model which basically figured out the folding of proteins. If you ask me what that means, that's beyond my pay grade, but was revolutionary in the science space. I had this very same conversation. Dr. Alex Wisner Gross. He believes that we're going to solve all of math and all of physics, he actually believes will happen the next two, three years. Wow. Most people think it's going to happen in the next five, ten years.
- [29:03]
B
Either way, it's a good deal from humanity.
- [29:06]
A
That's one of the reasons why when people try to predict even five years from now, it's kind of a silly exercise because you have to predict post the solving of physics, post the solving of math. And I'm not even sure Albert Einstein could do that today.
- [29:20]
B
Yeah, beyond, in many ways we're already beyond individual productivity or capabilities.
- [29:26]
A
There's a study out there that's something like 75% of all venture capital went to the top five firms. These extreme power laws when it comes to actually on the fundraising side. What do you think happens in the long run? Is this a new normal and is this even rational?
- [29:45]
B
It's true. And yet I see this vast flourishing of small firms, people under 50 million, under 100 million sized funds and I hear a lot of LPs looking for that. And you know what they're saying? They're saying we want to get close to the universities where this AI is being created. And the people that are closest to the universities are the people that just graduated and are starting these new funds. Maybe with very little experience, but they have the relationships. As much as technology is going to build our future, human relationships are crucial and should be maintained and cherished and cultivated. So we have this combination of sociobiology which you and I agree on. Very important. Human nature doesn't change very much. Relationships are very important. Human species developed over hundreds of thousands of years in tiny little close knit clans. Right. Family related, village related. We're not used to this massive urban sprawl and dense societies that we have today, 20 years ago or something, that the scales flipped from majority rural to majority urban. It's never going back. Many of our LPs use Addepar as their system of record. And for those of you who don't know, Addepar is a system of record for complicated portfolios, both public portfolios, stocks, bonds, things like that, as well as alternatives, private things like venture capital, private equity, real estate, and so on. So they just came out on, I think CNBC yesterday with a report that showed that 1.5 trillion AUM that Addepar was tracking. Only 2.8% of that was in venture capital, which surprised me. Double that was in private equity. I don't think that a lot of these small family offices are going to get into these big giant venture capital funds, except as through feeders and then maybe through the big banks. And I think that a lot of these new small funds are going to do very well and the savvy institutions and traumas are going to try and get into their next funds. So I want to give you in funds one, if I'm not in fund one, I want to be in fund two. If I can't be in fund two, then fund three. And that's the flip of what it was before. The old institutions used to say, we need to see that you have six funds under your belt before we're going to consider you. And now it seems to be flipping because the pace of change seems to be accelerating. Also, the large firms will be affected by the law of averages. Too much money generally gets more mediocre returns. There may be some returns to scale in that. I can put a big chunk in anthropic, as I hope you did. But on average, it seems to me that there's going to be innovation always around the edges.
- [32:23]
A
Well, David, I think we met originally in 2009. Okay. So I put together two.
- [32:29]
B
It was a very good year. It was a very good, very good year.
- [32:32]
A
I put together two founders of a company called iSocket, Zach Hassanin and John Ramey. I did what would later be understood as a unsyndicated pre seed round.
- [32:41]
B
Before pre seed was round, you were a fundless sponsor.
- [32:43]
A
It was my own money. I was a funded sponsor.
- [32:46]
B
That's good. That's good.
- [32:47]
A
And you helped with the company and you led the Series A. And we've kept up since then. So it's been great to stay in touch. And thanks so much for jumping on the podcast.
- [32:58]
B
Much appreciated. You've gone, you've done really well and I'm honored that you had me on. And I'll say something also about this. You talked about, you know, the world of AI and is it going to become making us more zero sum and, you know, sort of data driven and automatons of a sort. I think that you reached out to me partly because you knew me absolutely. Decade and a half ago and we built a nice friendship, mutual respect, and those relationships mean something. And you're a young guy and I'm going to tell the audience maybe who are many people who are even younger, most of you will live to a hundred years of age. Life is long. One of the most important things I ever Learned was in 9th grade, two futurists came to my high school, John and I think Mary Nesbitt. They used to write these books called Megatrends. They did megatrends of the 70s, megatrends of the 80s, megatrends of THE 90s. I don't know what happened to them after that. The thing that they told me that's lasted with me till now is they looked at us, the ninth graders in that back in the 70s, and they said, your parents. Talking about my parents generation graduated in a time post World War II when they generally would graduate from high school. Some of them went on to college and, and they would go to one company and work for a career for 40 years. Same company maybe increasing in responsibility, but more or less in the same line. And they would retire with a gold watch in 40 years. Your generation, they said, pointing to us, the kids, is going to have an entirely different career history. You're going to work in many different jobs in many different industries. Many of those industries don't even exist yet because we're in a fast changing world. And then he said, you're going to work in things called biotechnology, which we didn't even know what that meant here. Some of you will work in the space economy, some of you will work off of planet Earth, some of you will work in electronics and computers, which will do things that we can't even imagine. Now we're at that same kind of inflection point.
- [35:00]
A
So thank you, David. Appreciate you jumping on and looking forward to doing this again soon.
- [35:04]
B
Anytime.
- [35:05]
A
Thanks.