In this article, we explore how generative AI can change the way venture builders work and how it affects the startup ecosystem's economics. As more people become proficient users of Generative AI tools like ChatGPT and GitHub CoPilot, the resulting efficiency gains are changing the traditional startup building and funding models. This shift not only enables startups to explore niche markets and specialized services but also makes venture-building more accessible.
Generative AI has the potential to significantly change the way we build startups. I am not talking about how it enables new product categories or new business models. I’m not even thinking about new features driven by ad-hoc personalization.
What I find most interesting is the way it changes the economics of financing a venture and building a winning portfolio. And how it will do this across all types of startup financing, from Bootstrapping to VC, to corporate venture building. The method? Dramatically increasing the efficiency of venture building by reducing errors and costs.
As it is advancing now, GenAi greatly lowers the cost of intelligence. However, it is not able to do our job or take over our decisions. It’s not General Artificial Intelligence. It doesn’t need to be.
These tools provide usable dimensions of intelligence, which promise a decrease in venture cost and an improvement in venture quality. Starting in 2024, we will see the full effect of this, as more and more venture builders become skilled at using the next generation of more powerful and steerable GenAI tools.
The key drivers of cost in early-stage startups are human time and errors. It comes with trying to solve the enormous complexity of trying something new and learning how to do it right. Generative AI tools can greatly reduce the cost of the learning process, making it more affordable to reach product market fit.
I see three key pathways for how generative AI will reduce the cost of getting to product-market fit. If we were to map the total efficiency potential of GenAI in startups today, we could easily expand our perspective to include growth marketing, employee onboarding and training, financial reports, and investor relations. But, I want to focus on the key challenge of learning and building a relevant product.
1. Accelerated product development
Generative AI accelerates the generation of new product ideas, and prototypes. What used to take months to build is shaping up to take weeks - or days. Lack of access to customer insights also often limits early-stage startups. GenAI is very good at suggesting adequate tests for new products and features, and can even simulate customer behavior. All of this dramatically speeds up the path to product-market fit.
2. Increased efficiency of engineering
Early-stage startups can easily spend 30% of their funds on software engineering. Allowing engineers pair-program with GitHub Copilot dramatically reduces errors while increasing speed. Generative AI can also be used to take over less complex tasks of engineers, like generating unit tests. This frees up resources to focus on the venture’s most important issues. Finally, GenAI can help with documenting code, making it more readable and easier to fix. First studies show that engineering teams already become 30% more efficient if they use generative AI the right way. And, this number is bound to go up quickly
3. Improved leadership decision-making.
Generative AI makes it much easier for leadership to analyze data and generate insights. Traditionally, data analysis is often too costly for early-stage startups, leaving room for high-risk guesswork or untested hypotheses. Using Generative AI, startup leaders can leverage simple language and common-sense approaches to gain deep insights from data right from the start, enabling them to make more informed decisions.
Generative AI has the potential to significantly reduce costs and errors in various processes. If it can lead to a 50% reduction in costs and a 50% decrease in errors in various processes, it could bring about a transformation in how providers of capital, such as venture capitalists (VCs) and corporate venture builders, operate. Generative AI could add new options to the traditional startup funding model.
The “Power Law” that sits at the base of today’s VC portfolio model, simply refers to the fact that almost all of the startups that a VC or corporate venture builder invests in, will fail. Therefore, the small number of survivors must grow rapidly to generate incredibly outsized profits, so that the entire fund/portfolio generates the expected returns. Therefore, startups seeking investment typically need to demonstrate large target markets and aim for exponential hypergrowth ('triple-triple-double-double-double') to secure funding.
What if it became much cheaper and easier to launch and stay alive as a startup? Would the “Power Law” still describe the best way for successful venture building?
As long as there are startups that begin a high-risk, high-reward journey, VC funding is required, and the Power Law applies. But, as GenAI enables founders to take a route of lower-risk, lower rewards, it creates options for a new category of capital which we might aptly term 'Somewhat Risky Capital.'
This type of funding could be reasonably channeled into ventures and business models that currently struggle to secure VC investment. They become more attractive only because they fail faster and cheaper, but also because the cost of getting to market will come down by an order of magnitude.
As the risk balance in these “Somewhat risky” investment portfolios moves away from the Power Law, this emerging tier of venture financing could empower startups that focus on smaller industries. It could help founders to build products for less attractive customers. Finally, it could enable founders to develop specialized services for local markets.
Among the major beneficiaries of this change should be corporate venture building initiatives. The efficiency generated by GenAI enables corporations to address use cases within corporate venture building that have traditionally been uninvestable.
Traditionally, corporations don't provide enough money for their venture building initiatives to have a portfolio that follows the Power Law principle. Consequently, corporate venturing often struggles to show positive ROI purely in financial terms. (It’s a different story regarding non-financial KPI, but that’s a much harder internal sell.)
When venture costs and failure rates decrease, corporate venture initiatives can go beyond aiming for success on their first try. A small portfolio could yield a handful of successful ventures.
Among all the beneficiaries, however, my personal favorite is the founders. Anything that makes starting a tech-driven business easier is great progress. I expect GenAI to empower a new generation of founders, especially in smaller and traditionally overlooked markets. Many of these founders might even look at bootstrapping as a very accessible and perfectly adequate option.
We can clearly see the potential of tools like ChatGPT & CoPilot to create efficiencies that reshape the venture-building ecosystem. But how much of that future is already here? There won’t be a single moment or single tool that drives this impact. Rather, we should expect a steady increase in the proficient use of intelligence-tools, as they mature into business-as-usual reliability and productivity.
How can you be sure this impact is really occurring? As someone who likes to see data, the burn rate of ventures should be a good place to look. If we expect teams to get really good at using the power of GenAI next year, then the industry surveys for 2024 should show that median burn rates are starting to shrink - even beyond what VCs have pushed for post-2021.
Generative AI will make it easier than ever to start and thrive in the startup ecosystem. It will create a new wave of founders, and enable new categories of ventures and innovation. It will also reshape commercial venture building and the organizations we build for it. In the coming years, Generative AI is poised to revolutionize the startup landscape, ushering in a new wave of founders and reshaping the very foundations of venture building. With its potential to reduce costs, mitigate risks, and empower a broader spectrum of entrepreneurs, it's clear that the future of innovation will be driven by intelligence-as-a-tool, making the startup ecosystem more inclusive, dynamic, and accessible, but also a lot less capital-intensive than ever before.
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