A daily fee of $25k, two former fund managers conquer Wall Street with AI financial training

Two former SoftBank fund managers founded the AI training company Wall Street Prompt, which charges financial institutions $25,000 per day for training, with a waiting list now reaching two months.
(Background: Anthropic report: The AI dominance race in 2028—if the US doesn't maintain its computational advantage, it may be overtaken by China)
(Additional context: UC Berkeley study on "AI Brain Fog": 14% of office workers are driven crazy by agents and automation, with 40% considering quitting)

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• AI Anxiety in the Financial Industry
• Training itself is a business
• Singapore's Leading Indicator

Charging $25,000 a day, with a two-month wait. This is the price set by two thirty-something former fund managers, teaching Wall Street bankers how to use AI tools. According to Bloomberg, Felipe Sinisterra and Dave Wang co-founded Wall Street Prompt in July 2025, both previously held investment roles at SoftBank Latin America Fund.

Their courses do not discuss model principles, only how to directly embed AI into the daily work of financial analysts—scanning earnings call transcripts, comparing executives' body language with their spoken words, translating management statements into financial forecast figures.

AI Anxiety in the Financial Industry

Large banks are advancing their AI strategies in two simultaneous directions: one side cutting traditional roles, the other investing heavily to train remaining staff to use AI.

In Q1 2026, Citigroup, Wells Fargo, and Bank of America laid off over 5,000 employees combined, yet all three posted record profits for the quarter. Standard Chartered announced plans to cut thousands of support roles over the next four years.

JPMorgan Chase’s 2026 tech budget reached $19.8 billion, with CEO Jamie Dimon explicitly stating that they will hire more AI specialists and fewer traditional bankers. Analysts estimate that the total layoffs across major banks over the next three to five years could reach 200k.

Igor Sydorenko, CEO of Neurons Lab, directly pointed out: with high-skilled employees paired with AI tools, work output can be 10 to 20 times higher than before. He said, “They will no longer need any junior financial analysts or assistants—they can do it all themselves.” This judgment is making bankers from Singapore to New York uneasy.

In 2022, when ChatGPT was first released, most major banks blocked the tool internally due to security concerns. Less than four years later, JPMorgan has deployed the LLM Suite—an enterprise-wide large language model toolkit for nearly all employees; Goldman Sachs is collaborating with Anthropic to develop autonomous AI agent systems, and has joined a joint venture led by Anthropic, with Blackstone and H&F investing $1.5 billion; Bank of America reports that productivity has increased by 20% to 25% among its 18,000 developers after adopting AI.

Training itself is a business

Bloomberg observed that Wall Street Prompt’s clients include T. Rowe Price, Citigroup, and Bank of America, but the company is bound by confidentiality agreements and refuses to confirm publicly.

T. Rowe Price has arranged for two staff members to receive training for its investment professionals; Citigroup and Bank of America hold courses for external fund clients, with the banks covering the costs. Each session has about 20 to 30 participants, and nearly all clients have renewed their contracts. Currently, a fund managing over $50 billion is finalizing a renewal.

The backgrounds of Sinisterra and Wang explain why financial institutions are willing to pay this price. Wang previously worked at BCG and Morgan Stanley, then led cryptocurrency investments at SoftBank before founding a digital asset fund, 99 Capital; Sinisterra graduated from university, worked as a software engineer at Facebook, then moved to Goldman Sachs and Bank of America, and in 2019 joined SoftBank as FinTech head, helping deploy over $1.5 billion in investments.

They are not just selling technical skills but an intuitive understanding of financial scenarios that allows investment firms to “recognize” the value.

The two are also planning online live courses, priced around $1,500, targeting financial professionals who feel resource-constrained and cannot wait for institutional invitations. This is an attempt to bring the “elite customized courses” to retail clients, building a second revenue stream outside their traditional high-ticket business.

Singapore’s Leading Indicator

Bloomberg mentioned that Asia is leading the world in embedding AI into finance, especially Singapore. According to the IMF’s AI readiness index, Singapore ranks first among 174 countries; a 2026 survey by financial software company Finastra shows that 64% of financial institutions in Singapore have deployed AI in core operations. Sinisterra and Wang are currently considering relocating to Singapore to serve the local market directly.

The case of hedge fund analyst Justin Tang illustrates the demand in this market. He spent three years self-learning AI at Regal Funds Management with little success; after meeting Wang and Sinisterra last year, a company analysis that once took hours now takes 90 seconds for a first draft. This time saving is precisely what Wall Street Prompt’s paying clients want.

Another dimension of evidence from Singapore: Duncan, 55, was laid off by a bank outsourcing process and unemployed for nine months. He rebuilt his AI skills through courses at Nanyang Technological University and eventually found a job at a local bank. His story shows that AI training is not only an offensive tool but also a threshold for maintaining employment eligibility.

The $25,000 daily training fee is essentially a liquidity premium that financial institutions are willing to pay to “shorten the learning curve.” Sinisterra’s straightforward comment: “What people are really paying for is transformation, not just prompts or templates.” As AI shifts from a competitive advantage to a baseline for employment, those who can accelerate this learning curve will hold a business with the least demand over time.