If there is one point on which nearly all venture capitalists seem to find rare unanimity when it comes to financing artificial intelligence startups, it is that investing in this rapidly advancing sector demands an entirely distinct methodology—one that diverges markedly from the playbooks that guided previous waves of technological transformation. The rise of AI, with its volatile pace, immense infrastructure demands, and transformative potential, does not fit neatly into traditional valuation models or growth trajectories that defined earlier booms in software or internet innovation.
During a lively discussion at TechCrunch Disrupt 2025, Aileen Lee, the founder and managing partner of Cowboy Ventures and a long-standing figure in the venture capital community, described the current investment climate with characteristic frankness. “It’s a funky time,” she remarked, capturing both the excitement and disorientation that dominate the market. Lee elaborated that the established logic of venture investing is undergoing a profound recalibration, citing examples of young AI enterprises that have rocketed from zero to one hundred million dollars in annual revenue within the span of a single year—a level of acceleration that defies conventional growth expectations and challenges every assumption about scaling.
However, Lee was quick to clarify that the frenzy surrounding rapid revenue generation is not, in itself, sufficient to attract serious Series A backing. Drawing on research undertaken by her firm, she emphasized that contemporary investors are now guided by what she metaphorically termed an “algorithm with different variables and different coefficients.” In other words, the criteria for evaluating AI startups have become far more multidimensional. Rather than fixating solely on topline growth, investors now weigh a range of qualitative and quantitative factors that together shape a company’s long-term defensibility and value. These include whether the startup is producing proprietary data assets, the durability of its competitive moat, the proven accomplishments and resilience of its founding team, as well as the technical sophistication underpinning its product architecture. As Lee succinctly put it, “Depending on what your company is, the output of the algorithmic formula is going to be different”—a recognition that no single template governs success in the AI era.
Echoing her sentiments yet adding his own perspective, Jon McNeill, co-founder and CEO of DVx Ventures, observed that even startups demonstrating impressive early traction—achieving, for example, five million dollars in revenue shortly after launch—often struggle to secure follow-on capital. He cautioned that “this game has changed, and it is changing dynamically,” highlighting the speed with which investor expectations continue to evolve. According to McNeill, the bar for Series A funding has risen sharply, to the point where early-stage startups are now subjected to the same rigorous scrutiny once reserved for more established, mature firms.
McNeill further explained that many investors have come to realize a paradox at the heart of contemporary startup scaling: those companies that ultimately break out and dominate markets are not necessarily the ones that possess the most advanced or elegant technology. “They have the best go-to-market,” he stated, emphasizing the increasing importance of distribution strategy, customer acquisition, and retention capabilities as decisive competitive advantages.
Steve Jang, founder and managing partner of Kindred Ventures, offered a differing point of view. While acknowledging the value of a strong sales and marketing execution—known in venture capital and startup circles as go-to-market or GTM—he argued that it is inaccurate to claim that exceptional GTM performance can fully compensate for technological mediocrity. “I don’t think it’s 100% true to say mediocre technology, great GTM wins and raises money and gets customers,” he said, asserting instead that success in this new environment demands a union of both elements: robust, differentiated technology and sharp, efficient market engagement.
McNeill later refined his stance, clarifying that his earlier comment was meant to emphasize how critical it has become for founders to think about their commercialization and distribution strategies from the very beginning of company formation. “Investors are getting much more sophisticated on the go-to-market than they have in the past,” he observed, suggesting a new level of analytical precision in how backers evaluate demand creation, sales efficiency, and user retention in AI businesses.
The ongoing debate about the relative importance of marketing versus technology resurfaced vividly later in the conference when Roy Lee, founder of the viral startup Cluely, recounted a cautionary tale. He explained that releasing a product that barely functions—no matter how much social media hype it enjoys—can backfire. His observation served as a reminder that while virality can generate sudden attention, sustained success ultimately rests on delivering a product that performs reliably and evolves quickly enough to outpace copycats.
Returning to the theme of relentless speed, Aileen Lee pointed out that AI startups today are under extraordinary pressure to ship updates and launch new features with unprecedented frequency. The competitiveness of this market now hinges on the velocity and quality of execution. “If you look at how much OpenAI and Anthropic are shipping, you’re going to have to figure out how to match how much you ship, how quickly and the quality of it,” she remarked, underlining that maintaining parity with the leading players demands not only technical prowess but also organizational endurance.
Despite these intense demands, all of the panelists converged on one essential conclusion: the AI industry remains in its earliest evolutionary stage. As Jang summarized, “There are no clear, outright winners, even in LLMs. There are competitors nipping at their heels.” That admission encapsulates both uncertainty and opportunity. For ambitious founders and investors alike, the field is still wide open, leaving room for both established corporations and emerging challengers to redefine leadership. In essence, the conversation painted a portrait of a sector that is simultaneously exhilarating and unstable—a “funky time” in which old rules are dissolving and new ones are being written in real time, governing the future of how intelligence itself is invested in, scaled, and ultimately monetized.
Sourse: https://techcrunch.com/2025/11/13/vcs-abandon-old-rules-for-a-funky-time-of-investing-in-ai-startups/
