AI trainers for Wall Street banks have quietly become the most sought-after consultants in finance, with some duos pulling in $25,000 a day to teach seasoned bankers and hedge fund analysts how to actually use artificial intelligence. While global financial institutions have poured billions into AI infrastructure, many remain stuck when it comes to integrating these tools into daily workflows—and that gap has created a golden opportunity for a new breed of tech-savvy ex-bankers.

Meet the Duo Cashing In

At the center of this booming niche are Dave Wang and Felipe Sinisterra, two former finance professionals who turned their understanding of both banking and AI into a thriving business. Wang, 31, is a Harvard graduate who spent his early career grinding through mergers and acquisitions deals at Morgan Stanley in New York. Sinisterra previously worked in the fintech division at Goldman Sachs, where he crossed paths with Wang during their later stint together at SoftBank.

Rather than chasing the traditional banking ladder, both decided to step off the well-trodden path and build something tailored to the moment. Today, their company specializes in helping bankers, traders, and hedge fund employees harness large language models effectively—earning the pair a reported $25,000 per day combined.

The Real Skill Now: Teaching the Prompt

For years, “prompt engineering” was the buzzword everyone wanted on their resume. That phase is rapidly evolving. Today’s financial firms aren’t just looking for people who can craft prompts—they need professionals who can teach others to do it well.

Jane Street, for instance, has been advertising a $300,000 position for a “machine learning educator” whose job is to train internal staff. This represents a natural progression from the earlier prompt engineering era. Companies have moved beyond hiring AI whisperers; they now want AI instructors who can scale that knowledge across entire teams.

Inside Their Training Toolkit

During a recent session in New York with a venture capital firm, Wang showcased how Google’s Gemini model could be deployed to dissect founder pitch videos. Using a custom web application that draws on behavioral analysis techniques associated with FBI investigators, he demonstrated how transcripts can be cross-referenced with visual cues—body language, facial expressions, micro-pauses—to flag potential warning signs in entrepreneurs seeking funding.

This is just one piece of what Wang and Sinisterra offer. Their broader toolkit includes:

• Prompts that direct Claude to assign numerical scores to management commentary, enabling sharper sentiment analysis
• Frameworks for evaluating founder pitch videos, where subtle gestures like winces, folded arms, or hesitations can be interpreted for hidden meaning
• Custom workflows that automate research and analytical tasks typically performed by junior staff

Other Ex-Bankers Riding the AI Wave

Wang and Sinisterra are far from alone. A growing list of former finance professionals are pivoting into AI-related ventures. Taron Arshakian, formerly of Deutsche Bank, is building AI benchmarks. Nicholas Lin, who previously worked at Morgan Stanley, has joined Anthropic, where he’s working on automating tasks that once kept armies of junior bankers busy late into the night.

Every market cycle creates its own lucrative side door, and in 2026, AI is unmistakably where the smart money—and the smart talent—is flowing.

Why Senior Bankers Refuse to Retire

While younger professionals chase the AI gold rush, a different story is unfolding at the top of the banking pyramid. Many senior managing directors who could comfortably retire are choosing to keep working—and the reason often comes down to family financial support.

According to the Financial Times, today’s wealthy bankers feel obligated to support not just their adult children but also grandchildren. One London banker’s wife described the steady stream of “wearing” texts from her adult son asking for financial help with his growing family. Another retired banker has already gifted £250,000 (roughly $337,000) to each of his grandchildren.

The reasoning, he explained, is straightforward: with skyrocketing property prices and shrinking access to stable, lifelong careers, younger generations simply can’t make it without family backing. If that kind of generational wealth transfer isn’t an option, working into your 60s and 70s may be the only path.

Other Notable Items From the Finance World

A few additional stories making the rounds:

• Some London bankers who bought UK government bonds maturing in 2061 as a long-term play are now bailing out, dumping £50,000 holdings as the trade sours.
• AI-resistant roles in finance include senior fund managers, financial advisers focused on long-term client relationships, and risk managers whose work depends on nuanced judgment. Junior analysts handling data processing and basic modeling, however, face the biggest threat.
• Elon Musk has been granted 1.3 billion shares with eye-watering vesting conditions—potentially worth $750 billion, but only if he successfully establishes a Martian colony of one million inhabitants.
• Quantum computers may eventually be capable of cracking Bitcoin’s cryptographic security, raising long-term concerns for the crypto industry.
• Prediction market trading desks at hedge funds and proprietary trading firms are now offering salaries around $260,000.

Tips for Banking Interns

For interns hoping to stand out, one piece of advice circulating on social media suggests overcommunicating relentlessly. Phrases like “Will do,” “Sounds good,” and “On it” should be used constantly to signal that messages have been received and tasks are underway. Quick email responses remain non-negotiable.

The Bigger Picture

The rise of AI trainers for Wall Street banks reflects a broader shift happening across finance. Institutions are no longer asking whether to adopt AI—they’re scrambling to figure out how to actually use it productively. In this environment, professionals who understand both the language of banking and the language of machines are commanding premium rates.

Wang and Sinisterra’s $25,000-a-day fee may sound extraordinary, but in a world where banks have committed billions to AI transformation, the cost of teaching a roomful of senior staff how to extract real value from these tools is a bargain. For ambitious analysts watching from their cubicles, the message is clear: the next big career opportunity may not be in dealmaking at all.