Business Technology Roundtable

In June, I wrote about why AI training had moved inside the enterprise : the open web has run dry of the cognitive density frontier models now need, and the next phase of the Applied-AI race would be won on access to human tacit knowledge, not public data. I said the leading tech vendors had already restructured to capture it. What I didn't have yet was a single, undeniable proof point of a professional services firm making the same bet with its own money. Now I do. Kirkland & Ellis, the world's highest-grossing law firm, is spending roughly $500 million of its own revenue over the next three to four years building proprietary AI systems it will own outright. A meaningful piece of that is a co-built platform with Palantir called the Fund Formation Engine, designed to run private-equity fund documentation, side-letter drafting, and obligation tracking across Kirkland's fund-formation practice. Kirkland didn't pick Palantir for its model. It picked Palantir for its on...

The public internet, long treated as an inexhaustible resource for training large language models, has run dry. Not in terms of raw volume, but in terms of the cognitive density that frontier AI now requires. Research I published through GeoActive Group's Applied-AI Initiative confirms what a growing number of senior researchers have quietly acknowledged: the next phase of the AI race is being won or lost on access to human tacit knowledge, and the leading tech vendors have already restructured their organizations to capture it. This is not an incremental refinement to existing AI training methodology. It is a wholesale reorientation of how the most resource-intensive companies in the world are deploying their most valuable internal asset: the unwritten reasoning of their best people. The Structural Bottleneck Driving This Shift Three converging constraints have forced this strategic pivot. First, models trained on generic web content have hit a reasoning ceiling. They perform ade...