Beyond the Hype: As AI Funding Soars, Smart Money Tightens Its Belt 

Despite record venture capital flowing into AI startups in 2025, a growing sense of caution is emerging among investors who see alarming parallels to the dot-com bubble, as they confront a market saturated with inflated valuations, unclear monetization strategies, and startups where “AI” is often a marketing gimmick rather than a core solution.

This skepticism is driving a strategic shift, with savvy VCs moving beyond the hype to tighten their filters, increasingly favoring foundational infrastructure companies and specialized application builders with defensible moats over flashy front-end startups, signaling a necessary market maturation that prioritizes sustainable business fundamentals over speculative frenzy.

Beyond the Hype: As AI Funding Soars, Smart Money Tightens Its Belt 

The venture capital world is caught in a paradox. On one hand, 2025 is shaping up to be a landmark year for artificial intelligence, with record-breaking sums of capital flowing into startups promising to reshape our world with algorithms. On the other, a growing chorus of seasoned investors is hitting the pause button, their spidey-senses tingling with a disconcerting sense of déjà vu. The AI gold rush is in full swing, but the prospectors are starting to worry that too many are selling shovels without a clear map to the gold. 

The central question is no longer if AI is transformative, but which AI companies are built to last. The market is buzzing with talk of inflated valuations, ambiguous monetization strategies, and a widening gap between technological potential and practical, profitable application. This isn’t a dismissal of the technology, but a necessary maturation—a market correction in mindset that separates the foundational architects from the flash-in-the-pan opportunists. 

The Dot-Com Echo: When Exuberance Outpaces Execution 

The parallels to the dot-com bubble of the late 1990s are too stark to ignore. Then, as now, a revolutionary technology (the internet) promised to upend every industry. A company needed only a “.com” in its name to attract feverish investment, regardless of its path to profitability. The result was a spectacular boom and bust that wiped out trillions in market value, but also laid the groundwork for the genuine tech titans of today. 

Today, “AI” is the new “.com.” It has become a ubiquitous, often superficial, badge slapped onto pitch decks to secure funding. As Somdutta Singh, founder and CEO of Assiduus Global, astutely points out, the term can mean “anything from basic automation to truly transformative use.” This blurring of lines is dangerous. It allows startups with a simple API wrapper around a large language model to command valuations previously reserved for companies with defensible intellectual property and scalable business models. 

The warning from Andrej Karpathy, a founding member of OpenAI, that Artificial General Intelligence (AGI) is still at least a decade away, is a crucial reality check. It punctures the hype balloon and forces a critical question: if the ultimate goal is distant, what tangible, near-term problem is a given startup solving today? Exaggerated claims not only set unrealistic expectations for investors but also risk a catastrophic blow to public and corporate trust in AI when they inevitably fall short. 

The Data Deluge: Record Funding Masks a Growing Cynicism 

The surface-level numbers tell a story of unbridled optimism. According to data from CB Insights, AI startups have cornered more than 50% of global venture capital investments in 2025. In India, the enthusiasm is palpable, with Tracxn data showing local AI startups raising $474.57 million so far this year, highlighted by massive fundraises like Uniphore’s $260 million Series F round. 

Yet, behind these headline-grabbing figures, a strategic shift is underway. The initial “spray and pray” approach is giving way to a more surgical, skeptical investment thesis. Venture capitalists are no longer satisfied with a slick demo; they are digging into the fundamentals. 

The new VC due diligence checklist for AI is brutally practical: 

• The Core Enabler Test: Is AI the fundamental engine of the company’s value proposition, or is it merely a marketing add-on? A company that uses an off-the-shelf model for minor efficiency gains is fundamentally different from one that has built a proprietary, data-driven moat. 

• The Monetization Litmus Test: Beyond the buzzwords, how does this company make money? Is there a clear, scalable business model with paying customers, or is the plan predicated on a future, hypothetical demand? 

• The Technical Talent Audit: Does the founding team possess the deep technical expertise to innovate and iterate, or are they business folks riding a trend? In a field evolving as fast as AI, a lack of top-tier technical talent is a fatal flaw. 

• The Infrastructure vs. Application Assessment: As Bhaskar Majumdar of Unicorn India Ventures notes, many are growing wary of “front-end AI startups” where the risk of commoditization is high. The smarter, albeit less glamorous, play may be in the foundational layers—the chips, data centers, and power generation needed to fuel the entire ecosystem. 

Where the Smart Money is Flowing: The Three Layers of AI Opportunity 

To understand where value is being created, it helps to view the AI landscape as a stack with distinct risk and reward profiles. 

1. The Foundation Layer (The Pickaxe Sellers): This is the infrastructure that powers everything else. It includes companies like Nvidia (chips), and the burgeoning sectors of AI data centers and micro power generation. As Majumdar states, this high power-intensive sector requires massive physical infrastructure. Investing here is a bet on the entire AI ecosystem’s growth, much like investing in railroad companies during the industrial revolution. The risk is lower that any single application will fail, as long as the overall demand for compute continues to soar. Unicorn India Ventures’ bets on Netrasemi and Kluisz are prime examples of this thesis in action.
2. The Agentic & Model Layer (The Toolmakers): This layer comprises companies building the core AI models and agentic frameworks that applications run on. While potentially high-reward, this is an extremely high-risk arena. Competing with well-funded behemoths like OpenAI, Anthropic, and Google is a daunting task. As Abhishek Prasad of Cornerstone Ventures warns, “Unless one has very deep pockets, investing in foundational models is risky. Even in the agentic layer, there’s a risk of commoditisation.” The winner-take-most dynamics in this layer make it a treacherous playground for all but the most specialized VCs.
3. The Application Layer (The Problem Solvers): This is where most of the hype and scrutiny currently reside. These are startups applying AI to solve specific, domain-specific problems in fields like healthcare, finance, logistics, and marketing. The key to success here is not having the most advanced AI, but having the best data and the deepest understanding of a particular industry.

Prasad’s firm, for instance, prefers “application-led models that build domain-specific moats.” A startup using AI to streamline drug discovery by analyzing genomic data has a more defensible position than one offering a generic AI content writer. The former builds a moat through exclusive data partnerships and specialized knowledge; the latter can be easily replicated. The “smarter play” is to back companies where AI is a powerful tool embedded in a deep, valuable solution, not the product itself. 

The Path Forward: Building with Authenticity in the Age of AI 

So, what does this period of correction mean for founders, investors, and the tech ecosystem at large? It signals a welcome return to business fundamentals. The era of “AI-washing” is reaching its expiration date. 

For founders, the mandate is clear: focus on authenticity over hype. As Somdutta Singh advises, the innovation reveals itself in “the quality of data, the robustness of models, and the depth of the problem being solved.” Founders must articulate not just what their AI does, but why it is uniquely qualified to solve a painful, valuable problem in a way that is sustainable and ethical. 

For investors, the task is one of discernment. It requires the wisdom to see through the buzzwords and the patience to back companies that are playing the long game. The goal is not just to spot the next unicorn, but to identify the next workhorse—the company that will use AI to build a durable, profitable, and meaningful business. 

The current tightening of filters is not a sign of AI’s failure, but a sign of its growing up. The dot-com bust was not the end of the internet; it was the painful, necessary process that cleared the way for Amazon, Google, and Netflix. Similarly, this moment of VC caution is weeding out the superficial, forcing a focus on substance, and ultimately strengthening the entire AI landscape. The mania may be intensifying, but the smartest players are ensuring that the foundation being laid today is solid enough to support the world of tomorrow.