GenAI and the New Economics of Analyst Work

This week we found Trista Kelley’s Financial Times article “AI is coming for (some) finance jobs” quite timely to what we are also seeing in the industry.

In the article, we learn that Omar Sayed used to run an analyst-heavy book at Millennium. At his new shop, Porchester Capital, he’s replaced most of that human horsepower with a retrieval-augmented generation (RAG) system powered by Claude and Gemini.

The result: 75% of a traditional analyst’s work is now automated — DCFs, LBO scenarios, CRM updates, document intake, idea vetting — and his personal productivity is up 4x.

It’s no surprise Sayed’s model works. Microsoft’s latest research puts Business & Financial Operations jobs in the “highly automatable” bucket, and we see the same trend as incumbents move fast:

• S&P Global paid $500M for Visible Alpha

• Bloomberg upgraded its MODL function to fend off rivals

• Anthropic is embedding S&P’s historical data directly into Claude

• Goldman Sachs is exploring AI agents for investment committees

The math is simple

As we’ve said before, AI in finance isn’t just about innovation — it’s about survival.

The industry is in a margin squeeze, and any shop that can produce alpha with fewer high-cost resources gains a structural edge. The math is simple:

• Lower headcount costs = lower break-even point

• Cheaper data processing = less dependency on $26k terminals

• Savings can be reinvested in client acquisition, technology, or retained as profit

Once early adopters start reporting higher margins to LPs, others will be forced to follow. The competitive loop will be relentless and the type of team members you hire will shift.

The human side becomes more valuable

As models take on the heavy lifting in analysis, the human edge moves to places AI can’t go (yet):

• Building trust in client relationships

• Navigating shareholder activism

• Crafting strategy in complex political or market environments

Sayed is already hiring for interpersonal skills over spreadsheet mastery. The best networker in the room might now be more valuable than the best modeler.

Internal data is the moat

GenAI’s value is only as strong as its access to the right data — and more critically, your internal data.

AI systems live or die by their retrieval layer and access to quality data. If your internal data is siloed, unstructured, or locked inside legacy systems, the AI cannot deliver. This is where many firms get stuck: they can’t match the giants’ in-house data architecture and are left at a disadvantage.

Competing without a $500M acquisition budget

That’s why we built Basis.

Basis is a platform for research analysts that seamlessly connects siloed, disparate data and apps within an organization. It creates a unified workspace, layers in AI intelligence, and launches agent workflows to automate repetitive research tasks.

The same foundation that lets Sayed’s RAG system hum is now available without the kind of budgets large firms are spending — like S&P Global’s $500M buy of Visible Alpha. Basis lets mid-sized and boutique firms operate leaner, faster, and with the same intelligence layer as the big players.

Most importantly, they can keep the workflows they already know while using the latest AI to grow their firms.

The bottom line: The margin crunch is here. The firms that survive will master two things: the human side of investing and the data infrastructure that lets AI do the rest. Basis is how you get both — without needing Millennium’s resources.

~Michael J. Prichard, Founder & CEO

TLDR: What you need to know about AI in Finance

• Billions Flow to New Hedge Funds Focused on AI-Related Bets

  Highlights the surge of capital into AI‑centric hedge funds, such as a fund by a former OpenAI researcher raising over $1.5B with 47 % YTD gains, and firms backed by prominent investors like Steve Cohen and the Collison brothers. 
  

• The fund manager of the future might just be a machine

  Discusses how asset‑management firms are building AI platforms, embedding data scientists, and leveraging AI to interpret satellites and earnings calls—potentially supplanting human decision‑makers in the look and cost of active management. 

• UBS leans into AI to modernize wealth management
  UBS Americas is focusing 60% of its AI work on productivity gains, using its “Red” assistant for onboarding, KYC, and client communication across 52,000 employees while streamlining legacy systems.

• AI labs court Wall Street’s top quants
  OpenAI, Anthropic, and Perplexity are recruiting Wall Street quants, whose data and optimization skills adapt well to AI with minimal retraining. Packages of $1.5M–$3M make the career shift tempting.

Prompt of the Week

💡 This prompt helps you quickly spot ripple effects from major corporate moves. Even if you’re not directly investing in the companies involved, understanding how their competitors, suppliers, and partners might be impacted can uncover overlooked opportunities or risks in your portfolio.

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