How AI Became the New Dot-Com Bubble

In 2025, 64% of all venture capital in the US has been allocated to artificial intelligence startups, with investment hitting $50 billion in the second quarter, accounting for nearly half of all VC funding. Companies like OpenAI were valued at $300 billion without being profitable or publicly traded. This massive influx of capital, coupled with the lack of immediate revenue generation from 70% of these funded AI startups, has raised alarms among experts, suggesting that the current AI boom might be echoing the dot-com bubble of the early 2000s.

The dot-com bubble, an economic phenomenon from the mid-1990s to early 2000s, saw a surge of investment in web-based companies driven by the mass adoption of the internet. The NASDAQ composite rose nearly 400% before crashing 78%, wiping out most gains. Venture capitalists often invested without considering robust business models, leading to many bankruptcies. Today, the world is experiencing a similar technological boom with AI, particularly since the public launch of Chat GPT in late 2022, triggering a comparable surge in investor attention and concerns about sustainability.

Global funding for AI startups escalated from $18 billion in 2014 to $119 billion in 2021, with generative AI accounting for nearly 30% of the total US investment by 2023. In the first half of 2025, AI investments constituted 64.1% of all venture capital transactions in the US, sparking warnings and comparisons to the dot-com bubble. This enthusiasm has fueled an unprecedented wave of investment, exemplified by OpenAI's $300 billion valuation in April 2025 while still a private entity. Anthropic, a direct competitor, is seeking to raise an additional $5 billion despite its products not yet generating revenue.

The four largest venture capital deals in the US during the first quarter of 2025 were all AI-related, totaling $26.6 billion. This significant concentration of capital is highly reminiscent of the dot-com bubble frenzy, when funds were indiscriminately poured into companies without finished products, driven solely by the allure of emerging technology. This pattern raises concerns about the speculative nature of current AI investments.

Big tech companies have responded to this euphoria with an intense arms race to acquire top talent and infrastructure. Salaries for AI engineers have skyrocketed, with some compensation packages reaching $100 million, particularly in Silicon Valley. This competitive hiring environment is further fueled by significant investments in AI infrastructure, with Google, Microsoft, and Meta projecting a combined spending of $45 billion by 2026, a figure that doubles previous estimates. Alphabet, for instance, plans to allocate over $85 billion annually to its AI projects.

The period between the mid-1990s and early 2000s, known as the dot-com bubble, saw a surge of investment in burgeoning web-based companies, mirroring the current AI boom. During this time, the NASDAQ composite experienced a nearly 400% rise before collapsing by 78%, effectively erasing all gains. Venture capitalists and speculators poured money into companies without robust business models, leading to numerous bankruptcies as funding was exhausted without generating profits. This historical parallel highlights the risks of speculative investment in emerging technologies.

Since the public debut of Chat GPT in late 2022, investor attention has surged dramatically, leading to a significant increase in global funding for AI startups. This rapid escalation has drawn comparisons to the dot-com bubble, particularly given the high valuations of companies that are not yet profitable. For example, OpenAI was valued at $300 billion in April 2025 despite not being publicly traded. Similarly, its direct competitor, Anthropic, is seeking to raise an additional $5 billion even though its products currently generate no revenue, further highlighting the speculative nature of these investments.

The four largest venture capital deals in the US during the first quarter of 2025 were exclusively AI-related, collectively totaling $26.6 billion. This intense concentration of capital is reminiscent of the dot-com bubble, where funds flowed into companies lacking finished products, driven solely by the allure of emerging technology rather than proven business models. Amazon is reportedly considering a multi-billion dollar investment to deepen its ties with Anthropic.

Big tech companies have responded to the AI euphoria with an aggressive race to acquire top talent and infrastructure. This has led to an explosion in AI engineer salaries, with compensation packages reaching as high as $100 million, particularly in Silicon Valley. This talent acquisition frenzy underscores the high demand and perceived value of AI expertise in the current market.

OpenAI CEO Sam Altman stated that Meta is trying to poach his top engineers, offering what he called "$100 million sign-on bonuses." This exemplifies the fierce competition for talent in the AI sector.

Google, Microsoft, and Meta are collectively projected to spend $45 billion on AI infrastructure by 2026, a figure that has doubled previous estimates and represents a 13% jump compared to the prior year. Alphabet, for instance, has announced it will allocate over $85 billion annually to its artificial intelligence projects. This enormous spending reflects the commitment of major tech players to dominate the AI landscape, but also raises questions about the sustainability and return on such massive investments.

The tech sector currently accounts for 34% of the S&P 500 index, a proportion even higher than during the peak of the dot-com bubble in 2000. Despite this market enthusiasm, most generative AI products have yet to demonstrate a truly large-scale revolutionary impact. While they serve as useful tools, their transformative potential remains debatable. The payoff from these technologies may be more uneven than the hype suggests, as many users report models providing inaccurate, shallow, or incorrect responses. In practice, generative AI has proven more effective in supporting repetitive tasks rather than disrupting entire processes, leading to a disconnect between futuristic promises and tangible results.

The value of many AI startups is based on speculative future revenue that has not yet materialized. Revenue projections are often highly uncertain. According to CB Insights data, over 70% of AI startups funded in 2023 and 2024 still do not generate operating profits. This lack of profitability, combined with a significant increase in talent and early-stage company acquisitions by large corporations (40% in 2025), raises doubts about the sustainability of the current speculative wave and the potential for long-term returns on investment.

The sustainability of the current speculative wave in AI is being questioned due to uncertain returns on investment and doubts about actual productivity gains. While tech giants like Microsoft and Google are increasingly outsourcing coding to AI tools for productivity pushes, some new research indicates these tools may not be as helpful as anticipated. This discrepancy between perceived and actual benefits adds to the concerns about the long-term viability of many AI ventures.

Another troubling symptom is the disconnect between stock prices and economic fundamentals. Nvidia, a major AI chip supplier, experienced a nearly 17% loss in market value in a single day following the release of an open-source model offering similar results. Although Nvidia has benefited greatly from the AI boom, this event highlights the extreme volatility of current valuations, where future expectations can collapse swiftly with unexpected competitive breakthroughs. This mirrors the rapid shifts and crashes seen during the dot-com era, raising alarms about potential market instability.

Beyond individual company valuations, there is a significant risk of financial contagion. The excitement surrounding AI has pushed other speculative assets upward, including cryptocurrencies, echoing the asset correlation phenomenon observed during the dot-com bubble. Back then, the NASDAQ crash dragged down nearly 1,500 small tech companies and had a systemic impact on global markets.

Today, a negative event within the AI ecosystem could trigger a similar domino effect, given that many institutional portfolios and venture capital funds are heavily exposed to the sector. This interconnectedness means that a downturn in AI could have far-reaching consequences across various financial markets, potentially impacting overall economic stability.

Regulatory and social ecosystems could also significantly limit AI's future growth. Various entities, including the European Union and US authorities, are actively developing regulatory frameworks that may impose restrictions on data usage, privacy, or copyright. These regulations could create hurdles for AI development and deployment, impacting the scalability and profitability of AI solutions.

A clear example of this is the copyright infringement lawsuit filed by Disney and Universal against the generative image AI platform Midjourney. This case marks a legal turning point, as it is the first time major Hollywood studios have sued an AI company for copyright infringement. Such legal challenges could set precedents that reshape the landscape for AI companies, particularly those dealing with creative content.

Public skepticism also plays a role, with about 55% of Americans believing AI will be just one more technology among many, without a decisive impact. Restrictive regulation, combined with a potential decline in public and consumer enthusiasm, could trigger a sharp shift in market perception. This dual pressure from regulation and public sentiment poses a significant threat to the sustained growth and valuation of AI companies.

The enormous amount of money being invested in AI, with projections estimating up to $7 trillion by 2030, raises serious questions about the sustainability of such returns. Similar to the dot-com era, it is highly probable that many AI companies will not survive the current speculative cycle. Some will likely be acquired by larger entities, others will simply disappear, and only a select few will emerge as sustainable leaders in the long term, echoing the shakeout that followed the dot-com bubble burst.