Why the AI Trade is Over
The AI trade is facing a brutal reality check. As semiconductors are selling-off, investors are confronting a harsh reality: AI development and deployment costs are outweighing its actual financial returns.
These are the three biggest problems facing the AI industry today:
1. No moat: Competition from open-source models
As I detailed in my May 26 letter, AI Snake Oil-Part 3: The Rise of Open-Source Models, the largest AI firms face a massive structural threat: there's no moat.
The technical lead once held by companies like OpenAI and Anthropic is evaporating. For most everyday business tasks, low-cost models are gaining market share.
The shift is being fueled by a wave of highly capable, open-source models that can match the expensive giants on core benchmarks, rapidly capturing developer usage on major platforms.
Even for security-conscious organizations like banks and governments, trusted domestic alternatives like Cohere and Reflection are providing secure, efficient models at a fraction of the price.
By turning AI software into a relatively cheap commodity, these alternatives are stripping elite tech giants of their pricing power.
2. The AI "tax" on businesses
AI has become a "tax" on businesses. Companies are finding out that adding AI to their workflow is expensive: the productivity boost doesn't justify the cost.
LLMs are prone to sloppy output, hallucinations and unreliability so businesses can't automate roles away or eliminate human oversight. Layering expensive AI subscription fees onto their budgets without seeing real measurable ROI is causing firms to look for ways to save money.
Businesses are now rationing how much their employees use AI and switching to cheaper alternatives.
3. No viable path to profitability
The economics of scale, which can be highly profitable for software firms, don't apply to LLMs. Generative AI behaves more like an expensive utility. The raw computing power required to train and run these models completely swallows their subscription revenue.
Tech giants like Microsoft, Google, Meta, and Amazon are spending hundreds of billions of dollars building massive data centers and buying expensive computer chips. However, the money they are making from AI products isn't growing nearly as fast as their spending.
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Market Forces: A deeper dive...
Alex Kantrowitz discusses Apple's lawsuit against OpenAI which alleges the stealing of trade secrets through Apple employees ahead of OpenAI's highly anticipated IPO.
Institutional risk analyst, Chris Whalen, explains that while U.S. banks have posted strong earnings, their massive loans to private credit funds are a major risk being ignored by investors and regulators.
Goldman Sachs equity analyst, Rich Privorotsky, says the market needs to see ROI from AI firms and concentration risk presents a problem for both stocks and the economy.
Ed Zitron joins Isaac Pound on The Tech Report to discuss why the data-center owners are running for the exits.
Jeff Gundlach “The Bond King” and analyst Felix Zulauf agree that the world is transitioning from a U.S.-centric unipolar world order to a multipolar world order shaping an unfamiliar landscape for investors.
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Off the beaten path...
Jack Schwager, author of Market Wizards, joined Matt Zeigler of Excess Returns to discuss his new book which shares the stories of 17 of the world's best traders, including a college dropout who turned a $40,000 loan into $500 million.
The logistics of the Tour de France are incredible; moving 4,500 people, 2,000 vehicles, and an entire nomadic village every day, across 2,000 miles in just over three weeks.
British runner Josh Kerr broke the (1999) world record for mile time on Saturday in London by nearly half a second.
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