Most AI organizations cannot be foundations focused on improving the world. OpenAI was one of the few that started as a non-profit, but even it pivoted to a for-profit model—proof that investors have a limited appetite for ventures without a clear path to returns.
As a result, AI startups need a “come to Jesus” moment: they must scrutinize the long-term economics of their business models and figure out how they’ll contend with big tech incumbents rolling out their own AI products and services.
Ultimately, the gap between startups and large companies in AI has less to do with who builds the better AI model, and more to do with who can profitably bring that model to market faster and get network effects.
Even if two companies build the same AI offerings, big tech can outspend, outscale, and out-distribute them—having a clearer path to profitability where most startups are hemorrhaging cash. Still, speed and specialized innovation allow smaller players to stand out—if they can manage costs and quickly find their niche.
As an example, despite Google being the company that introduced the Transformer architecture in 2017—paving the way for breakthroughs like GPT-3 and ChatGPT—many had written them off in the race against OpenAI. However, it’s too early to make that call, as the economics of building sustainable AI products and services still heavily favor tech giants.
Given how important data, distribution, and infrastructure are in profitably building and running AI products and services, AI may be one of the technologies where big tech has an insurmountable advantage over startups. I think about this unique dynamic daily as we make investment decisions: where the sheer speed and innovation, which is typically enough to compete, may not be enough for startups to muscle their way into building a real competitor to the big tech companies.
