The AI Entrepreneur

Machine intelligence is the last invention that humanity will ever need to make.

- Nick Bostrom

Introduction

These days, definitions of AGI are a dime a dozen. “Human-level intelligent machines,” “a general problem solving algorithm,” “a recursively self-improving system”—these are a few of the catch-phrases you will hear. Sam Altman recently made headlines saying that superintelligence may be just a few thousand days out. But with shifting goalposts and fanatic discourse at every turn, it remains unclear what that even means, or what superintelligent AI should actually look like. What is really in store for us?

I’d like to share with you a vision of the future that I’ve had recently. In this vision is something I call: the AI Entrepreneur. 

The AI Entrepreneur is an AI system that is capable of founding and building a successful company. It can brainstorm ideas and create a business plan. It can oversee hiring, tech, sales, customer service, and marketing. It can handle finances, legal and PR concerns. And most importantly, it can ship product. In short, it’s an AI that can do all the essential tasks of an entrepreneur.

Can we build it?

Will it be difficult to create the AI Entrepreneur? Certainly. But with everything we know today about deep learning, it’s much more tractable than other problems frequently associated with AGI. And here’s why:

The most effective uses of AI involve modeling a distribution of labeled or human-generated data on a massive scale. While much of the current controversy in AI revolves around whether large models “truly” reason, generalize, or exhibit intelligence, it turns out that for a large number of practical use cases, it doesn’t really matter. In the end, interpolating a sufficiently complex training set and patching up any edge cases works wonders for real world problems. The bitter lesson for staunch skeptics, doomsday critics, and AI gospel-preachers is that simple imitation rules.

That’s good news for the AI Entrepreneur: it doesn’t need entirely novel behaviors to emerge from some magical algorithm, it can simply emulate entrepreneurs who came before. After all, why reinvent the wheel? Humans have been doing entrepreneurship for nearly 20,000 years, and although the specifics morph and evolve, the basic principles change little. While it’s true that innovation often plays a crucial role, one can certainly identify formulas for success. There is a finite (albeit large) set of observations and actions an entrepreneur needs to take to succeed, and, in principle, these can be learned by example. Put simply, entrepreneurship is possible to imitate.

That starkly contrasts with many other problems commonly viewed as “end-game” for AGI. Take “doing science” for example. Researchers frequently say things like “we’ll know we have superintelligence when it discovers novel physics,” and anything less isn’t really superintelligence. But such end-goals extend beyond what is currently possible with deep learning. Consider: if Einstein spent his lifetime meticulously studying the behaviors, thought patterns, and habits of Newton (rather than the fundamental nature of reality), would that have led him to relativity? Of course not. Relativity requires going beyond Newtonian physics. This example is a bit contrived, but novel discoveries mean changing paradigms; revolutions; regime shifts. And playing by the old rules doesn’t work in a new regime.

A similar point could be made for the adjacent end-goal of having an AI “do science” in machine learning research; and taken to its extreme, having a recursively self-improving AI. In silicon valley and beyond, a ridiculous amount of hysteria surrounds this concept. But it’s at least clear that scaling up prediction algorithms on a fixed human-generated dataset isn’t enough to reach this end-goal, even when the compute bill enters the billions. We don’t yet know how to build algorithms that handle regime shifts or approach singularities.

We do know, however, that deep learning today works for problems that can be learned by example. Once framed in this way, there are really just two core challenges to create the AI Entrepreneur: 1) collecting mass-scale high quality data from entrepreneurs: decisions, organizational structures, processes, reports, communications, and so on. Enough to fully demonstrate how to be a founder, and enough to prevent overfitting. And 2) devising a simple, scalable architecture conducive to these data types.

Should we build it?

Once created, the AI Entrepreneur is effectively a money printer. Except it’s better, because while generating returns, it actually adds value to the world. At the end of the day, successful entrepreneurship means satisfying customers, which means fulfilling the needs of real people. Moreover, the AI Entrepreneur can offer insight to human entrepreneurs on how to improve, the same way chess professionals improve their game using an AI engine. The AI Entrepreneur can do a lot of good.

Yet, a fear may be rising that giving agentic, financial, and legal powers to an AI is a recipe for disaster. Once that Pandora’s box is opened, how can we keep it on a leash? What if the AI gets out of control? How can we ensure it stays aligned with human interests and values?

I argue that, in fact, putting an AI in charge of building a company actually gives society maximal control over it. In contrast to the current paradigm of having top labs cook up powerful intelligences behind closed doors, the AI Entrepreneur must answer to the public. Individuals can harness sophisticated economic and legal structures to control the AI’s behavior—structures that humans have developed over centuries by trial and error. Customers can vote with their wallets. Governments can enforce competition. If the company missteps, it can be sued. Banks can limit funds the AI Entrepreneur accesses. Bad behavior can cause reputational harm. The public can invest when confidence is high. Shareholders can jump ship if business goes south. And so on.

Moreover, it’s quite easy to start small. The AI Entrepreneur can begin with tiny companies that fill simple needs. Then correct any issues observed, and try something slightly larger. Developing step by step avoids catastrophic failures that may result from releasing a powerful AI at scale before it has gained our confidence. In addition, throughout this process, it’s quite certain the AI Entrepreneur will be under extreme scrutiny. The watchful eye of the public and news media will enforce a high degree of transparency, resulting in either public trust or skepticism. The first one to develop the AI Entrepreneur has the blessing and curse of enormous free publicity.

Lastly, recall that the AI Entrepreneur simply learns by imitating human entrepreneurs. Its abilities and faults are just the ones present in its training data. In the distant future, perhaps a singularity will come when machines improve their own code and intelligence explodes. This should maybe even concern us. But in the paradigm of deep learning, data trumps code. An AI Entrepreneur built using deep learning can only significantly improve by collecting more/better data from real people. Thus, humans ultimately set the pace. Here, the AI Entrepreneur’s inability to go beyond its training is a strength, not a weakness.

The road ahead

The largest gap between current approaches and the AI Entrepreneur is surely interfacing and interacting with the real world on a large scale. Text from the internet and RLHF can yield helpful conversational assistants one-on-one, but it likely requires more sophisticated techniques to interact with customers, investors, tech, marketing and more. It is perhaps possible to run a company with a text interface alone, but it is very far from ideal. A good AI Entrepreneur should seamlessly integrate video, audio, tool use, calendars, email, and more.

Another key problem to solve is how to give the AI Entrepreneur all of the relevant context for decision making. Modern techniques can extend textual contexts past 1 million tokens, but there needs to be a good way to embed all the relevant information about the company, relationships, the competitive environment, the political winds, customer wants, etc. Successful leaders often have an acute sense of broad trends coursing under the surface of society and an instinctual intuition for the environment they operate in. Deeply understanding this ability and replicating it may be one of the more difficult problems.

While these challenges are daunting, one cause for hope is that the introduction of the AI Entrepreneur actually changes the startup ecosystem. These very problems that the AI Entrepreneur faces, from the outside, actually look like business opportunities. Once the AI Entrepreneur controls capital, it becomes a potential customer for other companies. Catering to its needs, providing it services, and solving its problems all become viable market strategies for savvy startups.

Conclusion

I see a future with the AI Entrepreneur in it. I think arguing over whether this system is truly AGI or superintelligence amounts to wasted breath. Whatever we call it, I argue that it’s tractable to build, it’s beneficial to mankind, and it’s highly controllable using societal levers we are all familiar with. So, next time you catch yourself saying “here’s a crazy business idea” or “why isn’t there some startup that does X,” just imagine the AI Entrepreneur hearing you, then immediately springing to action.

- Cameron Witkowski

Acknowledgments

Thank you to Sean Peari, Kyrylo Kalashnikov, Aman Bhargava, and Alexander Detkov for reading and providing feedback on initial drafts of this post.