While the current AI-driven market feels similar to the late 90s, a key difference is the financial reality. Unlike many dot-com companies with no cash flow, today's tech giants like NVIDIA and Microsoft have massive, undeniable revenues and established customer bases, making valuations more defensible.

A company with over $9M ARR was initially ignored by investors because it didn't fit the typical early-stage YC profile. Once its revenue was revealed at Demo Day, it became the hottest deal, showing that non-traditional, more mature companies in YC can be overlooked champions.

Sam Altman dismisses concerns about OpenAI's massive compute commitments relative to current revenue. He frames it as a deliberate "forward bet" that revenue will continue its steep trajectory, fueled by new AI products. This is a high-risk, high-reward strategy banking on future monetization and market creation.

DFJ Growth passed on a pre-revenue LinkedIn at a $1B valuation because they lacked a clear revenue signal. This highlights a common VC pitfall: over-indexing on current financial metrics and under-valuing powerful network effects and analogous, proven business models from other tech giants.

Lin warns that much of today's AI revenue is 'experimental,' where customers test solutions without long-term commitment. He calls annualizing this pilot revenue 'a joke.' He advises founders to prioritize slower, high-quality, high-retention revenue over fast, low-quality growth that will eventually churn.

A unique dynamic in the AI era is that product-led traction can be so explosive that it surpasses a startup's capacity to hire. This creates a situation of forced capital efficiency where companies generate significant revenue before they can even build out large teams to spend it.

Contrary to common belief, the earliest AI startups often command higher relative valuations than established growth-stage AI companies, whose revenue multiples are becoming more rational and comparable to public market comps.

Current AI spending appears bubble-like, but it's not propping up unprofitable operations. Inference is already profitable. The immense cash burn is a deliberate, forward-looking investment in developing future, more powerful models, not a sign of a failing business model. This re-frames the financial risk.

Beyond outright fraud, startups often misrepresent financial health in subtle ways. Common examples include classifying trial revenue as ARR or recognizing contracts that have "out for convenience" clauses. These gray-area distinctions can drastically inflate a company's perceived stability and mislead investors.