Financial
Last month, OpenAI’s shift to a for-profit public-benefit corporation lifted Microsoft’s valuation past $4T. CEO Sam Altman continues to travel the world for funding deals, and October was a busy month for him. Technology contrarian Ed Zitron calculates OpenAI’s cash needs over the next 12 months to be $400B, but Fed Chairman Powell dispels the connection between AI funding boom and Dotcom crash: “I won’t go into particular names, but they actually have earnings.” (Fortune).
Criticism abounded as OpenAI’s CFO opened a can of worms by suggesting government guarantees for data centers, only for Altman to walk these claims back. Critics of OpenAI are raising alarms.
Why does this matter?
If you have a retirement account, you’ll likely care about a potential stock market correction or crash. Aside from that, the financing of AI data centers has tentacles into other companies and industries (Oracle, Google, Nvidia, Meta, Microsoft, power companies, etc.), so downturns and bankruptcies would likely lead to market disruption. For higher education, there are considerations about AI model pricing, and stock market fluctuations can affect giving to non-profit organizations.
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AI & Productivity
Amazon & UPS announced layoffs at the end of last month. From Amazon SVP, Beth Galetti: “This generation of AI is the most transformative technology we’ve seen since the Internet, and it’s enabling companies to innovate much faster than ever before (in existing market segments and altogether new ones).” Analyst Gil Luria suggests “companies appear to be making the cuts partly to hold their overall profit margins steady while they spend tens of billions of dollars on A.I. infrastructure like data centers. Cutting back on employees is a way to convince shareholders.”
But Luria also notes: “[w]e do think that at some point A.I. tools will allow us to enhance productivity to a point that we’re going to need less labor, but we’re not there yet, not in any significant way.” But another way of thinking about AI & productivity is not merely task augmentation but as something that enables creativity. From developer Aaron Boodman:
“Claude doesn’t make me much faster on the work that I am an expert on. Maybe 15-20% depending on the day. It’s the work that I don’t know how to do and would have to research.
Or the grunge work I don’t even want to do. On this it is hard to even put a number on.
Many of the projects I do with Claude day to day I just wouldn’t have done at all pre-Claude. Infinity% improvement in productivity on those.“
(Emphasis mine)
Why does this matter?
As I mentioned in an earlier post, the potential of a J curve for AI productivity gains is one that some economists suggest. Although productivity gains aren’t yet visible, there is growing anecdotal data to suggest structural changes in work, particularly in visual and technical fields.
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AI & Higher Education
Wharton Human-AI Research reported that many enterprises have incorporated AI tools into employees’ daily work and are no longer exploratory in nature.
Higher ed, meanwhile, is not using AI to the same degree. Only 2% of Student Success Leaders say their institutions are very effective at using AI. Their measure is subjective, but the picture is suggestive that AI adoption in higher education is slower than in industry (for good or for ill). Higher ed Leaders are exploring governance and policy, a task likely to be difficult for wrangling fast-moving AI technological advancements.
What does this matter?
Universities continue to explore using AI, but at a pace slower than industry. There are opportunities for universities to participate in both the conversations framing the use of AI and the practical use of the tools.
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