Felipe Sinisterra and Dave Wang, two ex-bankers, are booked out for the next two months teaching financial institutions to actually use the AI tools they have already bought.
Felipe Sinisterra and Dave Wang, two former investment bankers, are charging banks and investment funds up to $25,000 a day to teach senior staff how to use the AI tools their employers have already paid for, according to a Bloomberg feature published this week.
The two are fully booked for the next two months, the report said. Clients to date include T. Rowe Price, Citigroup and Bank of America.
The premise of their work is, on its face, embarrassing for the rest of the industry. Global banks have spent the past two years pouring billions into AI infrastructure, model licences and internal tooling, on the explicit thesis that generative AI will reshape financial workflows.
What Sinisterra and Wang appear to be selling is not new technology but the working knowledge of how to use what is already installed. In demonstrations, the pair show senior bankers how to use commercial models such as Anthropic’s Claude, OpenAI’s ChatGPT and Google’s Gemini for tasks the bank’s own staff have not yet figured out, including, in one Bloomberg-described session, analysis of a video pitch from a startup founder using Gemini’s video-understanding mode.
The credibility behind the price tag is the founders’ own pre-consultant careers. Sinisterra was at Goldman Sachs and Bank of America before leading fintech investments at SoftBank, where he deployed $2bn and incubated several AI ventures.
Wang was at Morgan Stanley and led crypto for SoftBank Latin America; he now sits on the Harvard Data Science Initiative’s advisory board. Sinisterra runs the training business under the banner of Wall Street Prompt.
The shape of demand is itself the story. Banks are not, on the evidence of the booking calendar, struggling with model access. They are struggling with what the McKinsey-flavoured AI strategy decks of 2023 and 2024 left out, which is the granular ground-level work of fitting probabilistic tools to a profession built on deterministic outputs.
Earnings interpretation, market-analysis prompting, due-diligence synthesis and pitch-deck review are all areas where, on Sinisterra’s and Wang’s account, most analyst desks are operating at a small fraction of what the underlying tools can do.
The price point is doing a particular kind of signalling work. A $25,000 day rate roughly matches what a single managing director at a large US investment bank generates in fees in a quarter; it signals, to procurement, that the cost is too small to bother negotiating.
It also outpaces what big-four consulting firms charge for comparable training engagements, which is consistent with the broader shift toward smaller, faster, ex-practitioner consultancies pulling work out of the McKinsey-Bain-BCG envelope on AI-specific mandates.
The longer-term question is whether the trainer category compresses. Anthropic itself has been actively pushing into financial services since early 2026, including a Moody’s data partnership and full Microsoft 365 integration. As model vendors move closer to delivering plug-and-play financial workflows, the value of a bespoke prompting tutorial naturally declines.
Sinisterra and Wang have so far stayed ahead by emphasising live, novel use cases that the vendor documentation does not yet cover. How long that gap stays open is a different question.
For now, the two-month waitlist is what banks are paying for. The actual training, as several recent client testimonials note, can be replicated by a moderately curious analyst with a corporate ChatGPT licence and a weekend.
