This essay is part of a 3-part series:
The AI revolution now taking place is poised to change the software industry in fundamental ways, and likely, to shrink the number of people employed in it. Historically, our industry has consisted of smart people painstakingly telling computers how to do specific tasks in the arcane languages that computers understand. We call this process writing software. AI is upending that model.
Already, generative AI tools have substantially increased the productivity of programmers, allowing for more niche or lower value software to be written than previously would have been justifiable economically. But even bigger changes are afoot.
We’re moving to a future where AI can conduct arbitrary information-processing tasks based on natural-language instructions from any reasonably intelligent human who understands the problem they’re trying to solve. Manually instructing the computers exactly what to do will no longer be necessary. Essentially, the aims of the no-code movement are coming into focus, though in a different way than might have been anticipated a few years ago — general-purpose AI models are making hand-coded Lego-style no-code tooling obsolete for many use cases.
What this means is that there are likely to be a lot fewer programmers soon than there used to be. There may, however, be an increasing number of people doing work along the lines of what product managers and salespeople currently do — understanding and solving customer problems, but with the details of the solutions being realized by the computers themselves instead of by teams of programmers.
This parallels the trajectory experienced by many other revolutionary industries, like railroads — after drawing in millions of people in an initial boom, it’s not uncommon for an industry to settle into a relatively stable long-term economic position while relying on fewer and fewer workers to sustain its position.
If the software industry experiences the same dynamic due to more and more of its work being done by AI, this development will also have significant implications for the startup ecosystem. The ability of smart, often young, people with little or no money to start businesses that grow to huge revenues in a decade or less has only really ever been possible in the software industry, which in turn has only existed in its current form for about 50 years. That era is likely to be coming to an end in the near future.
This is because a software startup is an organization in which a small group of smart people create new value in the market by building and selling new software — that is, by manually telling computers how to solve a specific human problem. As building software manually becomes less and less necessary, software startups are going to either change in fundamental ways or go extinct entirely. This extinction event, if it happens, will also take the venture capital industry down with the startup ecosystem that feeds it.
Of course, how long it takes until AI technology advances to the point where manual software engineering is as antiquated as programming in machine code is today remains to be seen — it could be next year; it could be 50 years from now. But it’s almost certainly coming this century, and even until the full extinction event is complete, the nature of what software is and what it does is going to be constantly changing.
This leaves software entrepreneurs, myself included, in a precarious position now. There is more value to be created with software right now than at any point in recent memory, perhaps ever. Trillions of dollars worth of business processes can now be automated, creating huge efficiencies, and trillions more will likely be possible in the next few years. Building and selling software to meet those needs still requires teams of highly-skilled people manually writing code, so there’s a bonanza taking shape in the startup ecosystem — many entrepreneurs are racking up eye-watering revenues with AI products this year, and I expect that to grow substantially in the near future.
But many of those startups are likely to be made obsolete almost as quickly as they came into existence. If AI technology advances to the point that a business need can be solved with little to no work on top of general-purpose AI models, startups’ products painstakingly built with manual effort on top of earlier generations of AI technology will quickly lose their value, and those companies will quickly bleed revenue and wither if they haven’t accumulated other advantages in the meantime.
Of course, it’s entirely possible that some companies founded in the current AI wave will build more durable assets than quickly-obsolete technology, like owning valuable data or setting themselves up as hard-to-circumvent intermediaries between other companies. But many others are likely to earn huge revenues for a few years by building and selling extremely valuable software, then see those revenues quickly dwindle to zero as their products become trivial to replace due to further advancements in AI.
I believe it’s wise for today’s AI entrepreneurs to take this possibility into account. In particular, the possibility of huge revenues up front, followed by a rapid decline, introduces a new dynamic into the cost/benefit calculus of fundraising. In the past, fundraising was usually necessary to get a software product off the ground, because of the significant amount of up-front engineering work normally required to build something that could be sold for meaningful revenue. However, once that revenue was achieved, it was generally durable, because differentiated and valuable business software does not usually become obsolete overnight. Thus, VC funding has been an appropriate source of capital for software companies, which have required risky up-front investment but which have then generated large and durable revenue streams, and commensurate liquidity events, if successful — providing payoffs down the road for both investors and founders.
This dynamic may be flipped for many of today’s AI companies. Many previously infeasible or impossible to solve high-value needs in business software are now suddenly solvable in weeks to months with a good team, thanks to recent advancements in AI, and those solutions can quickly generate hundreds of thousands or even millions of dollars in revenue. But that revenue is likely to only last for a few years until the underlying product becomes trivial to replace, unless the company selling the product creates a long-term competitive advantage in ways that are more resilient to rapid advancements in AI.
Unfortunately, that dynamic presents significant risks for founders taking venture capital. A founding team able to generate tens or hundreds of millions of dollars in revenue over 5-10 years with a product that then vanishes in a puff of smoke will see very different outcomes depending on whether or not it takes venture capital. Without external investment, those revenues, net of costs, will be the founders’ to keep. With external investment, the founders will be able to take a relatively meager salary, but will then be expected to plow those revenues into future growth, which may not be available if the product direction is obsolete. In this way, raising even small amounts of venture capital can turn a multimillion-dollar outcome into a zero for the founders involved.
My conclusion is that for teams building in today’s AI landscape, it may be wise to forego fundraising entirely, at least at first, and instead to focus on generating as much revenue as possible, as early as possible. In many cases, it may be possible to get to millions of dollars in revenues with a small, self-funded team, then swing for the fences and attempt to create a huge and durable company from there. But if that proves not to be possible, at least the founders of such a company will have a sizable, if temporary, profit stream to hedge the risk of a dead end.
And for the software founders involved, being able to guarantee personal financial security may be more important now than ever, as our ability to generate economic value is likely to decline or even vanish in the decades to come as AI becomes better at building software than we are. Now is the time to gather our rosebuds while we may — and to reduce the risk of a zero outcome if possible.
Alex Kolchinski 