Anthropic has a $100 million plan to help enterprises get more from AI. No, it doesn’t involve agents. It involves people. Lots of people.
How retro.
On October 2, the company announced Claude Frontier Academy, with a goal of training 10,000 frontier deployed engineers by the end of 2027. Participants work through a simulated deployment, including use-case selection, security review, and handover, before undertaking a 12-week residency on a real project at their own company.
For a business selling increasingly capable models, it’s telling that Anthropic recognizes some of the thorniest problems in AI don’t involve AI at all. This, to me, is indicative of real progress, because the heart of any technological shift is always people.
Which people? Well, that’s where this idea becomes particularly interesting. As critical as developers are to the success of AI in any particular organization, we need those developers working alongside the people who understand the business, with authority to turn that understanding into systems that other employees can use. That’s the key to AI success.
Old advice for a new assignment
It has always been thus, however much we try to pretend that this time it’s different. Back in 2013, I quoted Gartner analyst Svetlana Sicular discoursing on the rise of big data technologies like Hadoop: “Organizations already have people who know their own data better than mystical data scientists.” She continued, “Learning Hadoop is easier than learning the company’s business.” Data becomes useful when paired with industry expertise—from experts who understand which data actually matters.
This topic is perpetually relevant. I covered the argument in 2023 and again in 2025. We’ve always needed people who can translate business requirements into software.
AI expands the kinds of knowledge that software can work with. Language models extend that work to material that often sat outside our applications: research notes, meeting transcripts, internal debates, and explanations of why a decision was made. An employee can ask questions across that material without someone first designing a report for every possible question. That opens up useful work that would have been difficult or expensive to automate before.
It also creates new ways to be wrong. A model may find a convincing explanation that was superseded six months ago, confuse a proposal with an approved policy, or combine two departments’ incompatible definitions of a customer. The people who understand those distinctions need to help engineers make them explicit, then build examples that reveal when the system gets them wrong. Companies that figure this out stand to benefit disproportionately; hence, PwC’s April survey of 1,217 senior executives found that companies reporting the strongest AI performance were twice as likely to redesign workflows around AI.
Teach the system what’s good
Bridgewater, the world’s largest hedge fund, has done some interesting work here that substantiates this theory. At AI Engineer NYC on October 13, two representatives of Bridgewater described Vault, its repository and access layer for external and internal unstructured information. Vault isn’t designed to be a dumping ground; instead, the company has shaped the way it stores data to make it useful for analysts.
I attended the talk and watched as Sam Green demonstrated a question about AI infrastructure investment and the Korean won. Left on its own, a web-connected AI agent relied on July material to answer a question posted in October. Hedge funds depend on up-to-the-second information to figure out optimal trades, making this initial attempt a failure. Connected to the human-influenced Vault, however, it searched October 1 through October 8 and drew on seven documents rather than one or two.
Knowing which period matters is part of understanding the investment question; making the right material available consistently is an engineering problem. You need both. A plausible answer assembled from stale research can be worse than an obvious failure because it gives the investor little reason to investigate further. The team also collected roughly 200 questions and manually identified useful documents, including misleading matches. Bridgewater’s Aaron Linsky reported that subsequent improvements lifted the share of relevant documents among the top 10 search results from about 60% to between 90% and 95% on that evaluation.
This method is useful well beyond investing. Someone has to distinguish a document that merely sounds relevant from one that actually helps answer the question. Engineers can then use those judgments to measure whether a change improves the system. Business expertise becomes something the team can test against, rather than advice delivered after the application has shipped. It’s not a case of human intelligence or artificial intelligence. You need both.
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