 |
LLMS ARE ALREADY DONE: Who Will Win - and Lose - in the Next Phase of Machine Learning By Berit Anderson Why Read: Analysts are questioning the validity of future claims about the widespread financial gains set to accrue from the saturation of LLMs across business verticals. This week's Global Report dives into the strategy and spending across cloud and AI modeling companies - Alphabet, Amazon, Anthropic, Meta, Microsoft, and Open AI - and compares them with the LLM uptake road ahead. - BBA ________ The single-most important decision in evaluating a business is pricing power. If you've got the power to raise prices without losing business to a competitor, you've got a very good business. And if you have to have a prayer session before raising the price by a tenth of a cent, then you've got a terrible business. I've been in both, and I know the difference. - Warren Buffett
Statements in this release that are "forward-looking statements" are based on current expectations and assumptions that are subject to risks and uncertainties. Actual results could differ materially because of factors such as:
- Microsoft's 2024 Q4 Earnings Release for the period ending 6/30/24 GS Head of Global Equity Research Jim Covello goes a step further, arguing that to earn an adequate return on the ~$1tn estimated cost of developing and running AI technology, it must be able to solve complex problems, which, he says, it isn't built to do. He points out that truly life-changing inventions like the internet enabled low-cost solutions to disrupt high-cost solutions even in its infancy, unlike costly AI tech today. And he's skeptical that AI's costs will ever decline enough to make automating a large share of tasks affordable given the high starting point as well as the complexity of building critical inputs - like GPU chips - which may prevent competition. He's also doubtful that AI will boost the valuation of companies that use the tech, as any efficiency gains would likely be competed away, and the path to actually boosting revenues is unclear, in his view. And he questions whether models trained on historical data will ever be able to replicate humans' most valuable capabilities. [. . .] GS senior multi-asset strategist Christian Mueller-Glissmann finds that only the most favorable AI scenario, in which AI significantly boosts trend growth and corporate profitability without raising inflation, would result in above-average long-term S&P 500 returns, making AI's ability to deliver on its oft-touted potential even more crucial. - Goldman Sachs Global AI Research Newsletter, "Gen AI: Too Much Spend, Too Little Benefit" (6/25/24) Despite being a technical innovation, the thing LLMs seem most likely to generate these days is intense feelings among those building and using them. Three major philosophical camps have emerged:
Which philosophy you personally subscribe to doesn't really matter. We are all bound up with the economic outcomes of the first camp, which benefits from the propellant thrust of a great power competition and the associated fervor of resources and capital that accompanies it. It is that future that will be built, regardless of whether it lives up to those rosy predictions or crashes and burns, sending markets into turmoil and accelerating the Earth's downward spin into global climate collapse. As frequent FiRe speaker and legendary economist Bill Janeway has explained, every bubble has its own kind of cycle. Cash floods the market, many companies are formed, many go out of business, huge carnage takes place, and (in best outcomes) something good comes out the other end. Today, we are going to explore the future parts of that equation to help anticipate where the chips (no pun intended) will fall. Who will fail, who will succeed, and what will be left in their wake? Within that reality, there are two economic questions that demand a deeper look: 1. Are tech companies over-leveraged on AI infrastructure? 2. When the LLM bubble bursts, who will be left standing?
|
