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Blockchain And The 95% Problem: What Mortgage Brokers Got Right
On <a href="https://xpertsstudio.com/we-asked-chatgpt-and-grok-where-btc-trades-by-the-end-of-september/” title=”We Asked ChatGPT and Grok Where BTC Trades by the End of September”>September 1, three mortgage brokers stood on a stage at Detroit’s historic Fillmore Detroit and pitched working technology prototypes to the company that engineered them. I watched from the mezzanine, seated in one of the theater’s original seats and a part of a building that opened in 1925 as the State Theatre, that survived Detroit’s decades of ebb of decline and flow of reinvention, and was ultimately restored again as the Fillmore.
The theater was built by C. Howard Crane during Detroit’s great movie-palace era, when going to the movies was itself an experience. The physical setting had an early twentieth century charm but the energy was decidedly twenty-first century and much more than atmospheric. Now, nearly a century later, the same room was being used for a different kind of experience: mortgage brokers showing a technology company what they believed their industry needed next.
That occasion was “The Big Pitch”, a contest Rocket Pro launched in June with an unusual premise for enterprise AI innovation: rather than telling brokers what it had built for them, the company asked brokers what needed to be built. Then came the twist. After months of framing the competition around a single $100,000 winner chosen by broker vote, Rocket announced it would build all three tools, awarding $100,000 to Seth Hasan of West Capital Lending for Quick Counter, $60,000 to Andrew Haff of Barren Hill Mortgage for Right Track, and $40,000 to George Chevalier of Clearview Lending Solutions for Signal.
The 95% Problem
The announcement matters beyond mortgages because it lands in the middle of a defining tension in enterprise AI: enormous investment in technology has yet to translate consistently into measurable business results.
A widely cited report from MIT’s NANDA initiative, The GenAI Divide, found that roughly 95% of the enterprise AI initiatives it studied delivered no measurable profit-and-loss impact. The report examined 300 AI deployments, interviewed executives and surveyed employees. Its diagnosis pointed less to model quality than to the way that companies select problems to solve and how they integrate AI into actual workflows.
Lead author Aditya Challapally toldFortune that the small minority of winners succeed because they “pick one pain point, execute well, and partner smartly” with the people who actually use the tools. That pattern actually predates generative AI.
The Pattern Predates AI
In 2018, Maersk and IBM launched TradeLens, a blockchain platform designed to digitize global trade. The technology (or, solution) was built first and offered to an industry afterward. Maersk shut it down in 2022, acknowledging that while the platform itself wassary for commercial
Source: ca.finance.yahoo.com