Why does Ed Zitron believe Anthropic and OpenAI shouldn't go public?
He says they’re 'horrifyingly unprofitable' with no measurable ROI, masking costs and risking a harmful market correction if allowed to IPO.
Video Summary
Zitron argues many AI companies lack measurable ROI and operate at massive losses, making public listings dangerous.
He contrasts AI spending with Amazon’s long-term CapEx story and says AI lacks a comparable path to profitable infrastructure.
Allowing Anthropic or OpenAI to IPO could mislead retail investors because their apparent revenues hide subsidized costs.
Nvidia and data-center contractors are the clear short-term winners, while many AI model companies may not survive a market correction.
He says they’re 'horrifyingly unprofitable' with no measurable ROI, masking costs and risking a harmful market correction if allowed to IPO.
Amazon’s heavy CapEx led to useful, profitable infrastructure over years; Zitron argues AI lacks a comparable long-term infrastructure story and clear returns.
He points to Nvidia (GPU suppliers) and construction/data-center firms as primary beneficiaries, not necessarily the AI model companies themselves.
Retail investors may be misled by subsidized pricing and engineered profitability, buying into companies that conceal large losses and limited paths to profit.
"When it comes to the actual businesses, you can't find anyone who can measure the ROI because you can't do it."
There's a widespread misconception that the increase in capital expenditures (CapEx) on AI technology directly correlates with successful business models. However, many AI companies, including Anthropic and OpenAI, are reported to be operating at significant losses without clear paths to profitability.
As organizations like Uber struggle to justify their AI spending based on measurable returns, a fundamental question arises: how can businesses assess the cost-effectiveness of AI tasks when the actual costs are obscured?
The rush towards adopting AI has led many companies to invest heavily without evaluating whether these investments yield tangible benefits, creating an environment described as one without clear returns on investment (ROI).
"Between 2003 and 2017, Amazon Web Services accounted for around $57 billion in capital expenditures."
The narrative around Amazon's growth contrasts sharply with the current state of AI companies. Amazon initially struggled with profitability but eventually established a profitable infrastructure that consumers valued.
Unlike Amazon, AI firms lack a similar foundational story, suggesting that they may not develop useful infrastructure that leads to profitability over time. For instance, the costs associated with AI data centers remain high with no clear path to offset those costs through returns.
Additionally, major investments in AI infrastructure may not result in future gains, as markets remain volatile. The expectation of substantial profits similar to those seen with Amazon's success is deemed unrealistic in the current AI landscape.
"OpenAI and Anthropic should not be allowed to go public. They are dangerous, unprofitable companies."
There's an underlying risk in allowing AI companies to enter the public market without clear profitability. This raises concerns about potential impacts on investors and market stability.
With significant investments pouring into AI, the expectation of continued growth may be misplaced, leading to speculation that could culminate in a market correction or bubble burst.
The irrational behavior of the current market may mask the precarious reality of AI investments, leading many to question how sustainable these dynamics can be, especially when foundational profitability is absent among key players.
"I think Nvidia is looking for the most aggressive haircut you've ever seen."
The current operational inefficiencies in AI companies, including a lack of completed data centers and stalled project execution, indicate a concerning trend for industry giants like Nvidia.
As AI technology rapidly evolves, companies must address the balance between investment in infrastructure and actual delivery of services and products. If foundational investments remain incomplete, there may be grave repercussions for growth and market confidence.
Projections suggest that if market conditions shift towards more realistic evaluations, many AI companies may face serious financial challenges, emphasizing the importance of strategic planning and execution in sustaining their market positions.
"I don't think these companies will survive."
Ed Zitron raises serious concerns regarding the viability of AI companies like OpenAI and Anthropic, arguing that they are "lossy companies" without a clear path to profitability.
He highlights the issue of these companies potentially becoming public entities despite their unprofitability, emphasizing the lack of scrutiny they face from investors and analysts.
Zitron warns that retail investors may be misled into supporting these companies, referring to them as "dangerous, unstable companies."
"No normal profitable company does things like this."
The discussion touches on financial practices, suggesting that the reported profitability of Anthropic might have resulted from engineered leaks or discounts, particularly related to their association with Elon Musk's SpaceX.
Zitron presents a stark contrast between typical profitable companies and the unpredictable business models of these AI firms, which he deems unsustainable.
He mentions the alarming non-GAAP loss for OpenAI of -122%, further illustrating their financial instability.
"This is a dangerous precedent, and it’s a dangerous thing for retail investors."
Zitron criticizes the lack of transparency regarding the true costs of AI services that are currently subsidized, placing retail investors at risk.
He explains that consumers are engaging with subscription-based AI services, often without understanding the extensive losses that companies are incurring to retain them as customers.
He points out a looming trend where businesses like Uber are implementing usage caps on AI tools after exceeding their budgets, a scenario likely to repeat in other large corporations.