We spend extraordinary amounts of money and computing power searching for opportunities in financial markets. Some of the smartest engineers, mathematicians, and researchers in the world spend their careers building systems designed to identify small advantages, test them, and deploy capital.
Much of the broader economy operates with nothing resembling that level of systematic investigation.
Prices vary wildly. Distribution is inefficient. Entire industries run on outdated processes. New technologies create opportunities that nobody has seriously explored.
I've been wondering what happens when we point that same research machinery at everything else.
Quant firms have developed an entire discipline around research, backtesting, capital allocation, and managing strategies that eventually stop working. What if we applied that discipline to discovering and creating businesses?
That would mean finding inefficiencies in existing markets and exploring an enormous, continuously changing space of possible products, services, transactions, and industries.
Some opportunities are waiting to be discovered. Others don't exist until someone experiments with creating them.
Historically, investigating these possibilities has required considerable human effort. Research takes time, validation costs money, and commercial execution often means assembling an entire organization.
AI is beginning to change those economics.
Making economic discovery computational
Imagine a research organization investigating thousands of commercial opportunities simultaneously. Its agents study industries, examine pricing and supply chains, research emerging markets, and develop hypotheses about where economic value might be captured or created.
Finding interesting ideas isn't particularly difficult. Figuring out which ones are worth pursuing, and then actually doing something about them, is another matter.
I'd organize this research through directed acyclic graphs (DAGs), where some experiments can run in parallel while others depend on evidence from earlier work. Agent swarms explore competing approaches, investigate difficult problems, and challenge assumptions. Findings from one experiment might open entirely new branches of research elsewhere.
Consider the invention of Post-it Notes.
In 1968, 3M researcher Spencer Silver was trying to develop a stronger adhesive. Instead, he produced one that stuck lightly and could be removed easily. For years, he struggled to find a compelling use for it.
Another 3M researcher, Art Fry, later remembered Silver's adhesive while trying to keep bookmarks from falling out of his church hymnal. The connection eventually became Post-it Notes. Even then, commercial success wasn't immediate. An initial launch in four cities produced mixed results, and 3M had to experiment with getting the product directly into customers' hands before it found broader demand.
What interests me is the connection between two problems that originally had nothing to do with one another. The knowledge was already there. It took years, another person's experience, and further experimentation to turn it into a business.
Imagine a research system that retains findings like Silver's and continually looks for applications in other industries. One swarm investigates possible uses, another studies customer problems, and others evaluate production costs and distribution. A promising connection can become a new branch of experiments instead of waiting for someone to stumble across it.
The system might simulate the economics, develop a prototype, contact suppliers, or attempt to secure actual customers. Some assumptions can be tested against historical data. Others require experiments in the real world.
Much of this execution is already possible with today's tools. Agents can write and deploy software, operate browsers, send emails, make calls, interact with APIs, and coordinate workflows. We still have reliability and oversight problems to solve, but we don't need to wait for some future agent-to-agent economy to conduct meaningful commercial experiments.
Depending on the opportunity, very little human involvement may be required. Others will depend on specialized expertise, physical infrastructure, or existing operators.
A disciplined evaluation process would begin with relatively inexpensive research. Promising results justify progressively more expensive experiments. At each stage, the system evaluates the evidence and decides whether to allocate additional compute, commit capital, revise the hypothesis, or move on.
The goal is to investigate and act on far more commercial opportunities than conventional organizations can reasonably explore.
The economics of experimentation
Reducing the cost of research is useful. Reducing the combined cost of discovering, validating, and executing an opportunity changes which opportunities are worth pursuing in the first place.
Consider a simplified model for the expected value of an opportunity:
Here, is the probability of commercial success, is the net value if successful before research and deployment costs, and and represent the costs of research and expected deployment.
A real model would need to account for unsuccessful outcomes, capital constraints, timing, and risk. This is enough to illustrate the idea.
Suppose an opportunity has a 1% chance of producing $100,000 in economic value. Its expected payoff before research and deployment costs is $1,000.
If it costs $10,000 to investigate and attempt, the expected value is negative $9,000.
If computation and automation reduce those costs to $200, the expected value becomes positive $800.
The numbers are hypothetical. We can't assume cheaper experiments are equally reliable, or that we can accurately estimate the probability of success in the first place.
Lowering the cost of experimentation doesn't just make entrepreneurship faster. It makes previously uneconomical opportunities worth investigating.
An opportunity that couldn't justify hiring developers, building a team, or committing substantial capital might justify a relatively inexpensive computational experiment.
Some discoveries could produce modest profits from temporary market inefficiencies. Others might reveal opportunities for entirely new products or businesses.
Most hypotheses probably won't survive validation. That's fine if the organization can recognize that quickly and cheaply.
What matters is whether systematic experimentation discovers more economic value than it costs to investigate and exploit.
Compute becomes a capital allocation problem
As more research, engineering, and commercial execution can be purchased through computation, compute itself becomes an increasingly direct input into economic production.
A firm could investigate an emerging market, develop several competing products, test different distribution strategies, or allocate additional resources to an operation already generating revenue.
All of those activities compete for the same resources. Where is the next dollar most likely to create value: investigating an uncertain hypothesis, validating something promising, or scaling a business that's already working?
Some problems might benefit from hundreds of agents exploring competing approaches. Others require customer feedback, supplier negotiations, or real transactions. No amount of additional computation will make a customer respond to an email faster.
The portfolio itself could look quite different from a conventional venture portfolio.
A pricing discrepancy might generate attractive returns for a few weeks before competitors eliminate the advantage. A specialized software business might be worth owning for decades.
The institution would need to decide which opportunities deserve more capital, which should be maintained, and when to exit.
Businesses aren't liquid financial instruments. Capital can be difficult to recover, and obligations to customers, employees, and suppliers can't simply be closed like a trading position.
The allocation process needs to account for these different kinds of assets, with their different risks, time horizons, and capital requirements.
The institution becomes the asset
I keep coming back to the possibility that the research itself could compound.
Imagine an experiment in customer acquisition reveals an underserved industry. Operating in that market produces information about its customers, pricing, and competitive dynamics. That knowledge exposes an opportunity in vertical software.
The software business, once operating, reveals inefficiencies in procurement or financing that weren't apparent during the original investigation. The original business generates cash flow while teaching the organization something about how the market works.
One discovery leads to another.
Successful operations can finance additional research. That research might uncover opportunities that wouldn't have been visible without the experience of operating the first business.
Even failed experiments can contribute useful information, provided the organization captures reliable evidence about what actually happened.
Over time, the firm could develop proprietary commercial intelligence, operational infrastructure, and relationships that improve subsequent research.
There may also be an advantage to investigating such a broad opportunity space. Research in one industry could uncover applications in another, particularly as the organization learns to recognize relationships between technologies, business models, and markets.
Information isn't automatically valuable because we collect it, and knowledge from one industry may not transfer to another. A compounding advantage depends on whether accumulated experience actually improves the firm's ability to discover and evaluate the next opportunity.
If it does, the most valuable asset might not be any individual business the system creates.
It might be the research institution itself.
Where this might lead
Agent-to-agent commerce could eventually reduce some of these costs further.
As identity, communication, and payment infrastructure mature, autonomous businesses may be able to discover suppliers, negotiate routine transactions, purchase services, and coordinate directly with other autonomous systems.
That could make certain markets more accessible and create entirely new ones. It could also eliminate existing inefficiencies as pricing and distribution become more competitive.
Agents can already participate in meaningful commercial activity through tools built for people, so the thesis can be tested before this infrastructure matures. A more machine-native economy would change the opportunities available to the research institution.
The question worth testing
There are good reasons this might not work.
Financial markets provide structured data and rapid feedback. The broader economy is messier. Customer relationships, distribution, regulation, and physical execution can dominate commercial outcomes.
Perhaps discovering opportunities isn't the real bottleneck. Maybe the hard part has always been exploiting them, and even with cheaper research and software, the economics won't improve nearly as much as we expect.
There's also a real danger of generating enormous numbers of convincing but commercially useless hypotheses. Quantitative finance has spent decades dealing with overfitting and strategies that look extraordinary in a backtest but fail when exposed to actual markets. An autonomous research organization would face many of the same problems.
The central question is whether it can consistently discover and capitalize on opportunities at a lower total cost than conventional methods.
Can it produce superior risk-adjusted economic returns? Does additional compute improve those results? Does accumulated research make the institution better at subsequent discoveries?
What interests me is whether the discovery and creation of economic value can become a continuous process that scales with computation. The institution I'm imagining would investigate opportunities across the economy, validate them through real experiments, and allocate capital according to what the evidence supports.
Some discoveries might produce short-lived commercial strategies. Others could become businesses worth owning for decades. Successful investments generate capital, while research and operating experience inform whatever comes next. The individual opportunities come and go. The institution retains what it learns.
I don't know whether the economics will work at scale. Many of the technical pieces already exist, and the remaining questions are ones we can begin answering through real commercial experiments.
I think there's a new kind of economic institution worth building here. One whose primary business is the systematic discovery and creation of economic value.
Further reading
- 3M. The history of Post-it Notes.
- Stenger, G. & Dewey, R. Financial Times, on the convergence of quantitative trading and AI laboratories.
- Lu, C., et al. (2024). The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery.
- Bailey, D. H., et al. (2017). The Probability of Backtest Overfitting.
- Hayek, F. A. (1945). The Use of Knowledge in Society. American Economic Review, 35(4), 519–530.