[Student IDEAS] by Karen Taubenberger & Michelle Diaz - Master in Management at ESSEC Business School
Venture capital is flooding into AI, driving rapid innovation - but at what cost? This article explores the promises and pitfalls of the AI funding boom, and why smart execution matters more than ever.
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Consider the array of companies that power your daily life; a quick scroll through your Instagram feed in the morning, taking an Uber to work, messaging your friend on messenger or WhatsApp, ordering lunch on Deliveroo, scheduling a call over Zoom, sending money overseas via Wise. The list seems endless. All these companies, at one point or another (some even multiple times), have received outside funding—they went beyond bootstrapping.
The supply of capital as a catalyst for growth is indisputable. But as it is true that capital can create growth, it is equally true that said capital could result not only in loss, but in active destruction as well. Consider the fall of the multi-billion dollar crypto exchange company, FTX led by Sam Bankman-Fried. The company amassed millions in investment and was valued at billions of dollars in its last funding round in 20221. At its core, the fall of FTX revolved around extremely human issues like the voiding of accountability, transparency, and trust2. The crucial and relevant part of the FTX crash is the fact that the technology (the blockchain) on which the business was built is not inherently bad per se. Execution was the issue.
Similarly, AI’s foundational technology is neutral, but the current investment frenzy risks repeating execution failures driven by unchecked capital influx. Indeed, startups that receive a heavy influx of capital get a shot at building companies whose services become an integral part of society. And since there are instances where that shot not only fails but becomes problematic enough to result in something like fraud, diligence is imperative. Today, that diligence must be put towards AI ventures, given the increased flow of capital into the space.
According to PitchBook data, Funding for AI and ML startups accounted for 35.7% of all VC global deal value, totaling 131.5 billion3. In North America, every second dollar invested went into AI startups. While some of these companies may justify their high valuations and shape the future, others may simply be riding the AI wave. In recent years, the joke has circulated that the easiest way to secure VC funding is to mention AI integration in your pitch deck, and it seems there’s some truth to it.
The questions are not only whether and when the bubble bursts, but also if this hype has other costs arising from inefficient capital allocation. Are we putting too many eggs in one basket and overlooking other drivers of innovation? Are startups that fail to check the "AI box" finding it harder to raise funds? What is the real cost of AI? After all, the infamous list of companies that opened this article and that govern so much of our lives are characterised not only by the strength of capital that powered them–but also by the distinct diversity of the innovation behind them.
The two most common types of startup funding are equity and debt. Equity refers to the exchange of ownership stakes for capital, while debt often refers to high interest loans as investments in startups are risky. Other sources of funding often include grants, subsidies, crowdfunding, institutional, and academic funding. Debt and these other sources often cannot compete with the check size and terms of specialized equity funding for early-stage companies, known as venture capital (VC). This is a type of private equity that provides capital and support for ventures that have potential for long-term and exponential growth. VC firms are typically formed as limited partnerships, where the limited partners (LPs) invest in the fund (general partner)4.
VC stood as a powerful growth catalyst in the wake of the 2008 financial crisis, between 2010 to 2020. The decade saw the rise of sector-specific funds like cloud computing. The “unicorn factory” phenomenon emerged in the 2010s, birthing ventures that achieved billion-dollar valuations even without going public. Amongst the most notable ones are Uber, Airbnb, Snapchat, SpaceX, and Stripe. Academics theorized that key factors for the phenomena were technological enablers, regulatory arbitrage, and investor herding or fear-of-missing-out (FOMO)5.
VC has funded and supported a large proportion of the most successful companies known today–to say that it matters which types of startups VCs back is an understatement. Moreover, going back further in time to the dotcom bubble of 1995 to 2001 further emphasises this truth than simply looking at the “unicorn factory” decade. VC had a frenzy in the tech startups of that era, dubbed as the dotcom bubble. A great deal of funds were allocated to internet companies–a reality that proved problematic as academics found that a prevalent practice at the time was a pouring of capital into startups, with a large percentage earmarked for advertising and marketing, which then gave way to higher valuations, which again led to overinvestment, inevitably contributing to the creation of a bubble6.
Jump to today. The investment world is awash in AI. The questions looms–are we hurtling toward another overinvestment frenzy? A hard look at the staggering $110 billion poured into AI ventures in 2024 uncovers hard truths7:
The flow of capital into AI is not evenly distributed. With $80 billion, AI VC investment in the U.S. is more than six times higher than in Europe. In fact, 44% of all U.S. VC investment was directed toward AI, a significant jump from just 14% in previous years9. This concentration is even more pronounced in the Bay Area, which alone accounted for more than half of these AI-related investments. In contrast, AI and machine learning represented only a quarter of the deal flow in Europe. Even within the continent, there are considerable differences between hubs, with the UK leading the charge in European AI investments.
A closer examination of the flow of AI funding reveals that the wave was largely driven by a series of mega-deals, with over $40 billion allocated to just a handful of companies. These funds are becoming increasingly concentrated among a small group of industry leaders. In 2024, investments in companies like OpenAI ($6.6 billion), Anthropic ($4 billion), and xAI ($6 billion) nearly matched the total amount invested in AI the previous year10. A large share of investments went into foundational layer startups. Foundational models, which are trained on vast amounts of data and serve as the backbone for all AI applications, require significant capital to develop.
While the past year was a boom for large, later-stage deals, seed funding actually declined compared to the year before. This drop in early-stage funding poses real challenges for new entrants. With investors doubling down on bigger, more established players, it’s becoming harder for emerging AI startups to secure the capital they need to develop and scale. At the same time, raising capital has gotten tougher for venture funds themselves. Rising interest rates and economic uncertainty have made limited partners (LPs) more selective, dragging out fundraising cycles and shrinking fund sizes. This tightening of capital flows is making it even harder for early-stage startups to find backers, further cementing the dominance of a small group of well-funded companies.
Historical precedents, such as the dotcom bubble, show that rapid capital concentration in emerging technologies often precedes corrections. AI’s reliance on foundational models–costly to develop and prone to valuation inflation–mirrors this pattern.
Take the mega-deals around U.S.-based startups focused on foundational layers. The valuation premiums for AI startups are especially ballooning at the later stages. So, are these high concentrations and overvaluations of a few mega-deals a reason to worry about a bubble? Perhaps. The dotcom bubble showed us that being a first mover does not guarantee dominion. The real winners in that space were often those who came after, with more refined offerings. Like how Google followed the first wave of internet companies. We might be seeing a similar pattern emerge in AI today.
Right now, AI is all about the enablers: the foundational models and infrastructure. But as the technology matures, the spotlight will inevitably shift toward its applications and how these models solve real-world problems. And that shift might come sooner than we expect. With emerging players like DeepSeek claiming to develop models for just $6 million, it’s clear that costs are coming down faster than anticipated. This means that cost efficiency and profitability are likely to become a focal point for AI startups as they try to raise funds.
The VC model is all about finding the next unicorn: a startup with the potential for 20x, even 100x growth. And with that in mind, it is easy to get distracted by shiny new things, promising this exponential growth potential. In 2024, AI investments surged by 62%, but overall startup funding declined by 14%11. Whether that split is justified is another matter entirely. Indeed, regardless of whether all this investment in AI is or will be worth it, one thing is abundantly clear: not all AI startups are actual AI startups (AI washing). Moreover, we must remain mindful not only of unbalanced capital allocation, but of other arising costs as well. A survey by Venture ESG found that only 19% of investors consider themselves well-versed in the ESG risks posed by AI. With the huge capital flows fueling AI’s growth, it is critical to consider its carbon footprint, its heavy energy consumption, and the potential for bias and discrimination in algorithms.
Foundational models are essential for the progression of AI. However, it is imperative to remember that innovation which fails to balance technological growth with responsibility often spells disaster. The downfall of FTX makes it clear that being blinded by hype can tempt us to overlook fundamental redflags. In cases where startups are pushed to deliver rapid results to justify sky-high valuations, the scope of innovation risks becoming narrower, favoring trends over transformative ideas.
Worse still, pressure can foster an environment where accountability and ethics take a backseat to growth at all costs, opening the door to wrongdoing. After all, pressure does not simply stifle creativity–it tempts shortcuts, breeds recklessness, and invites malfeasance. As we navigate the current AI boom, where billions flow and expectations soar, let us ensure that poor execution does not forfeit the promise of AI. Otherwise, the potential of innovation might very well be overshadowed by unchecked ambition. A cautionary tale FTX has already written.
[1] Dey, A. and et al., The Rise and Fall of FTX, HBS Case Collection, September 2023, Available at: https://www.hbs.edu/faculty/Pages/item.aspx?num=64748
[2] Shroff N, Reavis C. Sam Bankman-Fried's FTX. MIT Sloan School of Management Case Study, January 2024. Published online January 17, 2024.
[3] Robbins, J., AI startups grabbed a third of global VC dollars in 2024, Pitchbook. Published online January 9, 2025. Available at: https://pitchbook.com/news/articles/ai-startups-grabbed-a-third-of-global-vc-dollars-in-2024
[4] Ganti, A. et al., Venture Capitalists: Who Are They and What Do They Do?, Investopedia. Published online June 10, 2024. Available at: https://www.investopedia.com/terms/v/venturecapitalist.asp
[5] Davydova, D. et al., Why Do Startups Become Unicorns Instead of Going Public, National Bureau of Economic Research. Published October 2022. Available at: https://www.nber.org/papers/w30604
[6] Delossantos, C. From Hype to Bust: Investigating the Underlying Factors of the Dot-Com Bubble and Developing Regression Models for Future Market Predictions. Open Journal of Business and Management. Published online September 2023. Available at: https://www.scirp.org/journal/paperinformation?paperid=127628
[7] Lunden, I. AI investments surged 62% to $110B in 2024 while startup funding overall declined 12%. Techcrunch. Published online February 11, 2025. Available at: Techcrunch
[8] Opening moves in global AI. Dealroom.com. Published online February, 2025. Available at: https://dealroom.co/uploaded/2025/02/Dealroom-AI-Summit-2025.pdf?x63517
[9] Grabow, J., Massive AI deal supercharges VC results in Q1 2025. EY. Published online 23 April, 2025. Available at: https://www.ey.com/en_us/insights/growth/venture-capital-investment-trends
[10] PitchBook. AI & ML VC Trends. Published online 27 February, 2025. Available at: https://files.pitchbook.com/website/files/pdf/Q4_2024_AI_ML_VC_Trends_Preview.pdf
[11] AI investments surged 62% to $110B in 2024 while startup funding overall declined 12%, February 2025. Available at: https://shorturl.at/uK4DU
[12] Venture ESG LP. Pushing Forward Responsible Investing Practices Of VC Limited Partners with a Deep Dive on Responsible AI and Data. Published online February, 2025. Available at: https://www.ventureesg.com/wp-content/uploads/2025/02/VentureESG-Pushing-Forward-%E2%80%94-LP-White-Paper-Feb-2025.pdf