Wall Street is now paying an arm and a leg per day for artificial intelligence training, an amount that would once have bought a small trading desk, but now buys roughly eight hours of being told how to use ChatGPT without accidentally turning it into a compliance incident, as Citigroup, Bank of America and T. Rowe Price race to retool their workforces for an AI era where the biggest risk is no longer missing earnings season, but discovering your competitor figured out the prompt first.
Major U.S financial institutions are paying as much as $25,000 per day for specialist artificial intelligence training as Wall Street accelerates efforts to embed AI across trading, research and risk operations.
The courses are run by Felipe Sinisterra and Dave Wang through their firm Wall Street Prompt, which provides tailored training for investment professionals on how to use generative AI tools such as OpenAI’s ChatGPT and Google’s Gemini for tasks including market analysis, earnings interpretation and investment decision-making.
The pair are both former SoftBank fund managers, having previously worked within the Japanese investment group’s venture and technology investing ecosystem before leaving to focus on AI training and advisory work for financial firms.
Their client list includes Citigroup, Bank of America and asset manager T. Rowe Price, according to people familiar with the matter, reflecting the extent to which major banks and investment houses are now formalising AI capability-building at senior and junior levels.
The sessions have become so sought after that they are reportedly fully booked for months ahead, with banks competing for limited availability as internal pressure grows to avoid falling behind in AI adoption.
What $25,000 per day actually means
At face value, the pricing places the training in the ultra-premium consultancy category. But when broken down over time, the scale becomes clearer.
A single one-week engagement (five working days) would cost a bank $125,000. Over a typical four-week month, the cost rises to approximately $500,000 per client engagement.
If the training were hypothetically extended across a full year of continuous work, the cost would reach about $6.5 million per year, assuming a standard 260 working days. On a calendar-year basis of 365 days, the figure increases to roughly $9.1 million annually.
In practice, these programmes are not delivered continuously. Instead, they are structured as short, intensive interventions, often lasting days or weeks, suggesting that banks are paying for concentrated bursts of capability transfer rather than ongoing instruction.
Why banks are paying premium rates
The willingness to pay such sums reflects how rapidly artificial intelligence has shifted from experimental tool to core infrastructure within finance.
Large banks are increasingly deploying AI systems across equity and credit research workflows, compliance and surveillance systems, risk modelling and scenario analysis, client communication and reporting tools.
The commercial logic is that even marginal efficiency gains at scale can translate into significant cost savings and productivity improvements across thousands of employees.
Industry transformation and labour pressure
The rise of such training programmes comes amid broader restructuring across the banking sector, where firms are simultaneously investing in AI systems while reducing certain back-office roles.
Industry executives have increasingly framed AI not as a supplementary tool but as a structural shift in how financial work is performed, particularly in entry-level analytical roles.
In that context, high-cost training is being positioned as a way to “rebuild” internal capability quickly rather than rely solely on hiring or external consultants.
The result is a growing market for elite-level AI educators who sit at the intersection of finance and machine learning—offering not just technical instruction, but domain-specific reinterpretation of how banking work is done in an AI-driven environment.
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