David Brooks: “I’d say that a guiding principle of the emerging AI age is this: When intelligence is plentiful, volition is valuable. The people who are going to make a difference are not the ones who seek relaxation and passively use AI to work less. They are the ones who will seek improvement and actively wrestle with AI to develop their own mental capabilities and accomplish more. In other words, what will differentiate people is not how smart they are but their relationship to mental effort.”
NYTimes: “The number of students admitted to Ph.D. programs this fall dropped 15 percent from the previous year, according to data from over 50 top [US] research universities, raising fears that the nation’s capacity to produce new science could be diminished. The decline is driven, in part, by a chaotic and unpredictable federal funding environment under the Trump administration, as federal cuts are promised and then reversed, and budgets remain unclear. A reduction in doctoral students could mean fewer scholars at universities to teach and mentor undergraduates. Higher education leaders also worry that, if the declines continue, there will be fewer researchers to power a rapidly evolving scientific work force.”
SaaStr: “Salesforce, the largest pure-play software company in the world, runs about $41 billion in revenue. Anthropic’s run-rate passed it in April. Adobe is around $25 billion. Intuit about $19 billion. ServiceNow about $14 billion. Workday about $9.5 billion. Anthropic is already larger than every one of them, three years removed from its first dollar of revenue. On its current trajectory the run-rate is tracking toward $70 to $90 billion by December. At that level there is exactly one public software company still ahead of it: Microsoft, whose software and cloud business runs around $300 billion.”
Uncover Alpha: “Today, essentially everyone uses the state-of-the-art (SOTA) model for everything. You want to summarize an email? SOTA model. Classify a support ticket? SOTA model. Extract three fields from an invoice? SOTA model. We do this for one simple reason: the frontier models have only just crossed the threshold of being broadly truly impactful for knowledge work, and when something has only just started working, you reach for the best version of it you can find. You don’t optimize cost on a capability you weren’t sure you had last quarter. But I believe this is a transitional behavior, not a stable equilibrium… I believe that for the overwhelming majority of economically valuable knowledge work, the correct model is not the SOTA model. It’s the cheapest model that clears the task’s quality bar. And as pilots move into full production (which is the stage we are in today) — where you’re suddenly paying for millions or billions of tokens a day instead of running a demo — intelligence-per-dollar becomes the only metric that survives contact with a CFO.”
