Artificial intelligence (AI) companies are under pressure to deliver on promises of achieving artificial general intelligence (AGI) soon, in order to bridge the gap between investment and profitability in the sector.
Despite ambitions, there remains no scientific evidence proving the feasibility of AGI—machines capable of human-level or superior cognitive abilities.
A burgeoning market
Industry analysts describe the current AI market as predominantly speculative. OpenAI stands out as one of the few highly lucrative companies in generative AI, with a significant revenue lead (approximately $3.4 billion, according to The Information) over its closest competitors.
This disparity translates into a substantial deficit of around $600 billion, as estimated by recent analysis from Sequoia Capital.
Source:
Sequoia Capital
It’s worth noting that Sequoia’s projections are based on Nvidia GPU utilization, suggesting the figures may slightly understate global industry expenditures.
The analysis underscores the imperative for AI firms to generate over half a trillion dollars in revenue to justify current investment levels—a target expected to escalate annually.
Where are the products?
Despite the surge in investor and corporate interest propelling generative AI technology to record highs, including Nvidia’s brief stint as the world’s most valuable company by market capitalization, many analysts question when tangible AI products or services will sustain this growth.
As of now, generative AI has yet to demonstrate a definitive use case capable of driving exponential profit growth for stakeholders. While ChatGPT serves as a flagship product, there’s scant evidence it will soon dominate the mainstream market.
In essence, reaching the $600 billion revenue threshold will likely take decades if OpenAI’s commanding profit margin continues to dominate. Generative AI has yet to match machine learning’s proven value proposition, despite escalating investments from venture capitalists, governments, and corporations.
This trend hints at a potential future where the AI market faces a critical juncture—either delivering AGI or facing severe setbacks for companies like OpenAI and Anthropic, whose viability hinges on successful bets in human-level reasoning capabilities.
On the downside, companies central to the generative AI sector may soon confront revenue pressures. Failure to validate Nvidia’s near-$3 trillion valuation promptly could exacerbate the industry’s $600 billion shortfall to irreversible levels.
Conversely, achieving AGI would eliminate the notion of a point of no return. Nvidia, pivotal in this context, prepares to launch its new Blackwell-based chipset (dubbed “B100”) for training generative AI. The B100 promises up to 2.5 times better performance than Nvidia’s current H100 at a mere 25% increase in cost.
Experts suggest that achieving AGI becomes more plausible with hardware enhancements offering 150% greater power and efficiency than previous models.
Further developments:
Softbank, which suffered a 99% loss during the dotcom bubble burst, now focuses entirely on AI