The world of artificial intelligence is experiencing a boom like no other. Startups are sprouting up, raising funds at jaw-dropping valuations. Yet, despite the enthusiasm, a sobering truth looms: many of these ventures will fail. But as history has shown us, that’s not necessarily a bad thing.
The Bubble Inflates
AI startups are raising money at valuations that often defy logic. For instance:
CR, co-founded by former Salesforce co-CEO Brett Taylor, is seeking a $4+ billion valuation. Yet, reports suggest it generates minimal, if any, revenue.
Perplexity AI, an AI-powered search startup, recently sought $8-9 billion in valuation despite a reported annual revenue of only $50 million. This equates to a revenue multiple of 160x-180x.
Glean, a document search startup, raised $250 million in mid-2024, doubling its valuation to $4.5 billion. Yet, its annual revenue is just $55 million, putting its valuation multiple at 82x.
Poolside, an AI-powered software developer platform, raised $500 million at a $3 billion valuation.
These valuations far outpace their revenues, signaling an unsustainable trend reminiscent of past bubbles.
The Celebrity Effect
A significant factor driving these investments is the influence of “AI celebrities.” Investors often bet on the prestige and networks of high-profile founders rather than the viability of their products. Examples include:
Mira Murati, former CTO of OpenAI, raised significant funds for her AI startup based largely on her reputation.
World Labs, led by renowned Stanford professor Fei-Fei Li, secured $100 million at a $1 billion valuation.
Inflection AI, despite failing to gain traction with its chatbot competitor, managed to arrange a special deal with Microsoft to recover some value for investors.
This mirrors the streaming content boom of the 2010s, where major deals with celebrity producers like Shonda Rhimes and Ryan Murphy didn’t always deliver expected returns.
Economic Realities
Running AI-powered applications is expensive:
Training and Maintenance Costs: Large language models and AI apps require immense computational power, driving up costs as user bases grow.
Human Oversight: AI systems still rely on human input and monitoring, limiting cost-saving potential.
Competition: With numerous startups in the same field—like AI coding assistants (e.g., Poolside, TabNine, Magic, GitHub Copilot)—pricing pressure is fierce, eroding profit margins.
For instance, while OpenAI leads the market with $4-5 billion in annual revenue, its revenue multiple is just 42x—a stark contrast to the far higher multiples of smaller, less established companies.
Lessons from the Past
The hard disk drive boom of the late 1970s serves as a cautionary tale. Venture capital poured into 70 firms, many of which replicated each other’s work. When the market contracted in 1983-84, most of these companies collapsed. However, the industry eventually consolidated, leading to stronger, more innovative players.
Similarly, while an AI startup bust seems inevitable, the industry as a whole will likely emerge stronger. Companies with sustainable models will survive and thrive.
Moving Forward
For startups, prudent management is critical. Prioritizing lasting revenue streams, minimizing burn rates, and preparing for lean times can make all the difference. Employees, too, must remain adaptable, as many startups may not survive the bust.
Despite the looming challenges, the current wave of capital is fostering innovation. Funds flowing into AI applications like chatbots (Anthropic’s Claude), search tools (Perplexity AI), and coding assistants are testing the limits of what’s possible.
Hope for the Future
While many ventures will falter, those that endure could redefine industries and transform our lives. The AI boom, chaotic as it seems, is part of a cycle that often precedes revolutionary progress. Let’s embrace this exuberance, learn from past bubbles, and build a resilient future for AI.