⚪️ Wall Street Once Banned the Internet. AI in Similar Spot

Hey, it's Matt. Welcome back to AI Street—I cut through the hype to break down what's really happening in AI + finance, with analysis you can use.

PERSPECTIVES

When Wall Street Banned the Internet

Imagine you aren’t allowed to use the internet at work.

That was the reality on Wall Street in the early 1990s, Pete Harris, a seasoned technology expert and former journalist, tells me.

"Companies would say, 'We're very interested in this internet thing, but we would never, ever use the public internet.'"

AI, Pete says, is following the same path the internet once did—an enormous change initially met with a lot of resistance.

Pete has spent four decades bridging the worlds of technology and finance. In 1988, he launched Dealing with Technology, a monthly newsletter on tech and trading, which Waters Information Services (now WatersTechnology) acquired in 1990. He joined Waters post-acquisition, rising to Editor-in-Chief and then President. After Waters was sold in 1999, he left the following year to found Lighthouse Partners, Inc., his consulting firm that remains active today.

Just as financial firms hesitated to embrace transformative technologies like cloud computing, many are now slow to fully adopt AI tools.

"They said the same thing with cloud technology," Pete recalls.  “Firms were like, ‘We’ll build a private cloud, sure, but we’ll never rely on public clouds.’ But now? Many financial companies would be lost without it.”

No one truly “knew” the internet’s vast potential in its early days, but the world adapted—and Pete sees the same shift happening with AI.

"These technologies do something, but they don't work great yet. They should, and they will work great at some point in the future," Pete says. “There'll be setbacks along the way, because there always are, but it's definitely gonna happen."

NEWS

AI May Cut 200K+ Finance Jobs in 5 Years: Bloomberg

Global banks will cut as many as 200,000 jobs in the next three to five years as artificial intelligence encroaches on tasks currently carried out by human workers, according to Bloomberg Intelligence. (Bloomberg) $

• These stories always get a lot of attention since folks are worried they may lose their jobs, but the pace of adoption is slow and is more likely to lead to retraining anyway, per the Fed.

  • Honestly, who wants to do “routine, repetitive tasks” anyway?

Treasury Report on AI in Financial Services

I missed this one during the holidays:

The Treasury Department published its report on AI in financial services following its request for information from market participants over the summer. Treasury received 103 responses from consumer advocacy groups, trade associations, and financial services firms, including eight of the top 10 banks, on how they’re employing generative AI.

While traditional AI (like machine learning) is already widely used across financial services for functions like credit underwriting and fraud detection, financial firms are taking a more cautious approach with newer Generative AI technologies, particularly for customer-facing applications, according to the report.

Recommendations Noted by Respondents

• Harmonize Definitions: Create consistent AI model terminology to improve interagency coordination.

• Clarify Data Standards: Provide explicit rules for privacy, security, and quality of datasets used in AI.

• Enhance Consumer Protections: Mitigate risks of AI-driven discrimination or harm.

• Ensure Uniform Compliance: Update guidance on existing consumer protection laws and AI-specific compliance measures.

• Strengthen Federal Standards: Prevent regulatory gaps and clarify supervisory expectations, reducing state-by-state inconsistencies.

• Promote Collaboration: Support domestic and international partnerships to share practices, maintain consistent standards, and monitor concentration risk.

Treasury plans to work with the National Institute of Standards and Technology (NIST) on AI risk profiles for finance, review consumer protection laws, launch an AI information sharing forum, and develop programs to help smaller firms access AI capabilities.

Ex-Trader's AI Model Spurs New Fund

• Vulpes Investment Management is launching a new hedge fund that uses AI to scan vast amounts of public data to identify risky companies, focusing on red flags like high leverage and potential fraud in Asia-Pacific markets.

• The firm is run by Steve Diggle, who made $3 billion during the 2007-2008 financial crisis. He’s now seeking to raise $250 million in the first quarter for this AI-augmented investment approach. (Bloomberg) $

AI STREET MARKETS EDITION

This Sunday I’m launching AI Street Markets Edition. I’ll be using the below list of AI tools, with more to follow, to break down what’s happening in markets and companies in the news.

• Aiera

• Bigdata.com

• DoTadda

• Finster

• Hudson Labs

• Marvin Labs

 Subscribe to See AI investment Tools in Action

FIVE MINUTES WITH…  

Jayeeta Putatunda, Lead Data Scientist and Director at Fitch Group Inc, has been at the forefront of NLP since 2015.

Spurred by a chance NLP course during her master’s in quantitative methods, she pivoted from Deloitte consulting to data science and has since built significant expertise in combining traditional statistical approaches with modern AI techniques.

In this Five Minutes Q&A, Jayeeta shares her insights on AI evaluation frameworks, the emerging potential of causal AI, and why bridging research and industry remains a key challenge in financial services.

Key Takeaways:

• Causal AI could help address hallucination problems by grounding outputs in validated relationships

• Financial services need specialized evaluation frameworks beyond generic AI metrics

• Regulators require explainable AI models with clear evidence chains

• Integration of traditional statistical models with new AI techniques is crucial for adoption

This interview has been edited for clarity and length.

TECHNICAL INSIGHTS

Matt: Could you explain causal AI in simpler terms and why it’s important in finance?

Jayeeta: Sure. Think of finance or any domain where you care about cause and effect rather than just correlation. Econometrics has always done that, trying to see if a 5% change in parameter A leads to some shift in parameters B or C. You can run simulations to see if that link holds, or if there’s just a loose correlation.

Now combine that with generative AI. If you feed the generative model validated causal maps or relationships, then when it produces a report, it’s less likely to invent false connections. If you’re summarizing how Apple’s green initiatives led to a reduction in carbon emissions, you can validate that 5% or 10% figure through your causal model. The generative model can then generate text that reflects those relationships, rather than guessing.

Matt: That addresses one of the big concerns: hallucinations. Is that part of why we need more than just raw LLMs?

Jayeeta: Exactly. Especially in regulated industries like finance or healthcare, 99% confidence isn’t good enough. People want to know how you arrived at your conclusion. If a generative model says, “Company X decreased its carbon footprint by 20%,” but in reality, it was only 5%, you have a serious problem.

So the question becomes, “How do I prove this output?” You can add a retrieval mechanism that references a trusted knowledge base. But you can also anchor it in a causal model that runs these simulations and says, “Yes, a 5% change here caused 10% change there.” If your final output strays from that validated relationship, the pipeline can flag it or correct it.

Read the Full Interview

IN CASE YOU MISSED IT  

Recent Five Minutes with Interviews

• Ex JPM’s Tucker Balch on Scaling Investment Analysis with AI.

• USC's Matthew Shaffer on using ChatGPT to estimate “core earnings.”

• Moody’s Sergio Gago on scaling AI at the enterprise level.

• Ravenpack | Bigdata.com’s Aakarsh Ramchandani on AI and NLPs.

QUOTES

NOTEWORTHY LINKS

Anthropic Eyes $60B Value in Funding Round

Anthropic is in advanced talks to raise $2 billion in a deal that would value it at $60 billion, more than triple its valuation from a year ago. (WSJ)

Korea Passes AI Basic Act

The AI Basic Act, which mandates efforts to ensure the reliability of AI, includes provisions such as watermarks on AI-generated content to combat issues like fake news, deepfakes, and violations of personal information and copyright. (Business Korea)

Samsung Adds Generative AI TV Lineup

The company’s AI-powered screens will be able to search online for information about an actor or product on screen, translate in real time and generate personalized background images. (Bloomberg) $

HK Start-Up Arbor Eyes 100K Users for AI Tool

Company aims to grow AI chatbot for finance professionals to 100,000 globally within three years. (South China Morning Post)