"Build it and they will come" was among the fundamental assumptions of dot-com era startups. The belief was that getting big fast, creating a large audience of users, was the foundation for monetization. So, many venture-backed firms essentially spent more money to acquire a user than the lifetime value of that user was reasonable to anticipate, if in fact entrepreneurs even had an idea what that number might be.
The goal was to build market share first and figure out sustainable economics later. So instead of revenue indicators, startups used non-financial metrics such as page views, unique visitors, and user growth, rather than revenue metrics.
But artificial intelligence represents a fundamentally different paradigm. Rather than hoping to monetize attention, AI tools aim to address concrete business problems with measurable returns from day one: productivity gains, cost savings, or revenue improvements.
To be sure, there is some element of “user engagement” that observers note, as market share matters in big emerging industries. But, for the most part, investment seems more pragmatic for AI, compared to the dot-com era.
That doesn’t eliminate the danger of overinvestment. But the focus on financial returns does ground AI investments in financial reality. Instead of “build it and they will come,” AI developers are much more grounded in "build it because they're already asking for it and willing to pay."
Also, the funding of AI firms is not led by venture capital, but by profitable, revenue-generating, profitable hyperscalers.
Also, where many dot-com companies were essentially trying to become digital magazines or shopping malls, competing for finite consumer attention. AI companies are building tools that enhance work. So the focus is on productivity rather than entertainment spending.
Key Dimension | Dot-Com Era Startups | AI Firms (Current Era) |
Core Offering | Web-based services (e-commerce, portals, online marketplaces, content) | AI products/services, AI-as-a-service, automation, predictive analytics, platform APIs |
Value Driver | Website traffic, user acquisition, and “eyeballs”; hope for future monetization | Data-driven solutions that improve efficiency, automate tasks, and deliver predictive/personalized value |
Monetization | Advertising, IPO capital, early focus on growth over profits | Subscriptions (SaaS, AIaaS), API usage, licensing, and value-based enterprise sales |
Profit Timeline | Delayed, speculative—often prioritizing fast scaling “at any cost” over profitability | Earlier monetization; many AI companies achieve profitability within 2-3 years of launch |
Main Users | Primarily B2C (consumers)—focus on internet adoption and convenience | Mix of B2B and B2C—solutions for enterprises (automation, analytics) and consumers (personalization) |
Technology Focus | Utilized the internet for delivery; basic web technologies and e-commerce platforms | Core focus on AI/ML, deep learning, neural networks, data pipelines, and process automation |
Data Dependence | Limited data collection/analysis; basic web analytics | Highly data-centric; model development, product improvement, and scaling depend on high-quality data |
Scaling | Slow initial scaling, limited by server costs, bandwidth, and infrastructure | Rapid scaling via cloud computing; platform and ecosystem approaches facilitate global reach |
Investment Focus | Aggressive, speculative VC inflows; little diligence; focus on market “potential” rather than fundamentals | Cautious, more rigorous VC due diligence (product, financials, market fit, defensibility) |
Entry Barriers | Comparatively low—basic website, small tech team | High—requires AI/ML expertise, large datasets, significant computing resources, regulatory compliance |
Sustainability | Many ventures failed after burning through capital; often lacked clear viable business models | More robust, industry-adaptable models, often with recurring revenue and adaptable offerings |
Market Focus | Novelty-driven; unproven digital business models targeting new online audiences | Problem-solving, industry-agnostic, targeting specific verticals to deliver tangible value] |
Some amount of overinvestment will likely happen. That tends to be the case for many new general-purpose technologies. Not all the efforts will succeed, but the assets will be rationalized, as was the case for railroads, for example.
Phase | Description of Overinvestment | Typical Examples (Historical) | Rationalization & Consolidation Phase | Results of Consolidation |
Technological Breakthrough | Emergence of new general-purpose technology (GPT); triggers widespread excitement and investment | Railroads (19th c.), Electricity, Telephony, Internet (1990s), AI (2020s) | Initial indications of value mismatch and inefficiencies | Early signs: Maintenance costs rise, market saturation |
Investment Bubble/Boom | Massive capital flows seeking profit, often outpacing initial real returns or productive deployment | “Canal mania,” “Railway mania,” Dot-com bubble, early AI startups | Asset values diverge from fundamentals | Overcapacity, fragmented assets, irrational valuations |
Overinvestment | Realization that not all investments are viable; bust phase, financial losses, bankruptcies, job cuts | Great Depression (1929), Dot-com bust (2000), other sector crashes | Rationalization push by firms, investors, and regulators | Asset sell-offs, bankruptcies, sector exits |
Rationalization, Reallocation | Surviving firms acquire distressed/undervalued assets; asset consolidation reduces duplication | Telecom mergers post-dot-com, Cloud provider consolidation, AI sector | Consolidation into larger, more viable entities | More efficient use of capital, streamlined offerings |
Deployment, Productivity Growth | Consolidated sector harnesses matured tech; rational investment leads to true productivity gains | Sustainable “deployment period” post-crash (see Perez, 2025) | Stable sector, less speculation, measured investment | Stable profits, innovation resumes sustainable growth |
The point is that AI might not be analogous to the “dot com bubble.” It might be more akin to the investment pattern for lots of general-purpose technologies, where some amount of overinvestment eventually happens, but the assets are rationalized over time.
So far, no single phrase captures the investment mindset for AI. If "build it and they will come" expressed the unbridled optimism of the dot-com era, it is not yet clear what will emerge for AI.
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