The AI + Wall Street Year Ahead

 Welcome to the final AI Street edition of the year! I hope these Thursday briefings have been valuable to you. The next edition will be out on Thursday, January 2. Wishing you a happy holiday season!

ADOPTION
Wall Street’s AI Road Ahead

Despite all the AI hype, adoption on Wall Street has been slower than expected, as I’ve noted in this newsletter. When I started AI Street over the summer, I thought I was late to the AI conversation—now I realize I was early.

Generative AI, like ChatGPT, is still in its infancy. While traditional AI has been around for years (think spam filters), large language models (LLMs) are barely two years old. Their rapid rise to mainstream attention stems from their accessibility, enabling anyone to interact with AI through simple text inputs.

Here’s how I see AI evolving on Wall Street in 2025:

The Benchmark Challenge

Enterprise adoption faces a benchmarking challenge. How do you measure the quality of subjective AI outputs such as summaries? Without clear standards, decision-makers struggle to identify the best applications of AI.

The industry is responding:

• Dhagash Mehta, BlackRock’s head of Applied AI Research, recently published a thorough paper on benchmarking, called “How to Choose a Threshold for an Evaluation Metric for Large Language Models”

• James Corcoran, head of AI and Analytics at the Strategic Technology Analysis Center, told me this week that the benchmarking company is looking to assess new AI + finance platforms in 2025.

• Aiera, which provides AI-driven event streaming and analytics for institutional investors, recently published benchmark results for 28 models on finance tasks.

From Agents to Task Automation

Generative AI is often equated with chatbots, as ChatGPT introduced many to the technology. But this is starting to change.

If you follow AI news, you’ve likely heard of AI agents—tools that can handle multistep tasks like booking flights. But the term “agents” is misleading. They don’t really make decisions. I prefer to think of them as Taskbots—tools designed to complete specific, well-defined jobs.

Integrating Taskbots into Wall Street workflows makes sense.

For instance, Geoff Clauss, Chief Revenue Officer at Boosted AI, told me this week how their generative AI platform automated much of a weekly status report for dozens of analysts at a major bank, saving five hours per report each week.

While the timing is uncertain, it seems inevitable that AI will take over much of the repetitive, grunt work handled by financial analysts.

INTERVIEW
Tucker Balch on Scaling Investment Analysis with AI

As a kid, Tucker Balch dreamed of becoming an astronaut. Inspired by NASA’s finest, he plotted a course: become a military pilot, earn a PhD, and ultimately reach space.

After eight years flying F-15s and earning a doctorate in computer science, he secured an interview at NASA—but a minor health issue kept him grounded.

That setback became a launchpad. 

Tucker went on to navigate an unusual career as a robotics researcher, professor, and Wall Street AI innovator, authoring over 100 peer-reviewed papers and earning more than 15,000 citations.

Balch holds multiple AI and financial technology patents and co-founded Lucena Research (now Neuravest) a firm specializing in AI-driven investment solutions. 

Most recently, in 2019, he moved from Georgia Tech to join JPMorgan, where he helped his former postdoctoral advisor Manuela Veloso expand the bank's AI team from four to 110 members, cementing its position as a leader in financial AI.

This summer, Balch returned to academia, joining the Business School at Emory University.

I met Tucker at the International Conference on AI in Finance, the peer-reviewed conference he founded four years ago, which is a great event. 

Our conversation explored how AI is transforming investment analysis, from processing vast amounts of data to unlocking insights from alternative sources.

I spoke with Tucker for this week’s Five Minutes With Interview.

This interview has been edited for length and clarity. 

How do you see AI changing investment analysis? 

It's still the case that for most analysis tasks people are better than AI, but people are much slower than AI. So there's the capability to scale this up. And, you can do an 80 percent quality job on a thousand companies, as opposed to a 99 percent job on 10 companies. 

If you can have a fairly, decently informed opinion on thousands of stocks you can turn that into a robust investing strategy.

What's more important - the AI algorithm or the data?

The key question is, what is your data? In my experience, the effectiveness and quality of AI in investing strategies is less about the particular algorithm you're using and more about the quality or uniqueness of your data.

Large language models change that landscape in the following sense. When I talk about data, I generically mean numerical data. In other words, some sort of data that you can very easily feed into some sort of trading algorithm. What large language models do is they enable you to turn language into numbers.

Wherever you're getting your written language about a topic, you need to have something that is predictive and perhaps unique, but LLMs allow you to leverage different sources. For instance, if you can listen to the news in Vietnam, translate it in real time, and identify relevant information for specific stocks, you greatly expand your data sources.

People can do that but it takes time and, you’ve got to pay people to be listening and translating and what AI does is to enable a broader net. It opens up a lot more sources of data, more accessible that weren't accessible before. 

Read the full interview here

Q&A
Recent Interviews with Leading AI + Finance Experts

• 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.

• PhD candidate Alex Kim on executive tone in earnings calls.

• MDOTM’s Peter Zangari, PhD, on AI For Portfolio Management.

ICYMI: AI STREET MARKETS EDITION

Starting in January, I'm launching a Sunday edition featuring hands-on tips for using AI in market analysis. Keep an eye out for an opt-in invitation in the New Year.

Please take a moment to answer the poll below:

CENTRAL BANKING
AI Shows Promise in Decoding Fed Policy: Fed Study

A new Fed study shows generative AI effectively classifies topics like inflation, labor markets, and financial stability from FOMC meeting minutes. Tested on over a decade of data, top-performing models achieved high accuracy, especially with advanced chunking techniques.

“We were especially struck by how well the models performed despite the fact that we employed them off-the-shelf with no domain-specific fine-tuning,” the Fed researchers wrote.

Key Takeaways:

• Model Performance: New AI models like GPT-4o demonstrated impressive accuracy, particularly in identifying complex financial discussions.

• Research Insights: Analysis revealed shifts in focus over time, such as a growing emphasis on inflation after the pandemic.

• Future Applications: The findings suggest potential for AI to enhance public understanding of monetary policy.

Related: ”Urgent Need” For Central Banks to Adapt to AI: BIS

REGULATION
House Releases AI Report, Skips Specific Legislation

The House AI task force released a 200+ page report this week on how Congress can boost the technology’s development and safeguard against its risks without proposing specific legislation. The report covered broad AI use cases. I’ve highlighted key financial takeaways:

• Big firms are racing ahead with AI (fraud detection, underwriting, customer service) while small firms struggle with costs

• Main concerns: data quality, bias, security risks

• Recommendations: Build regulatory expertise, consider "sandboxes" for testing, ensure rules don't crush smaller players

MORE AI + FINANCE NEWS
Klarna Used an AI-generated CEO to Share Financials. Will More Companies Follow?

Ahead of Klarna’s IPO, CEO Sebastian Siemiatkowski announced some of the company’s financials — sort of. (Inc.)

Blockchain and AI: The Dynamic Duo Shaking Up Treasury Teams

The two technologies that are rapidly evolving, and in blockchain’s sense maturing, to meet the growing demands of finance teams and treasury functions across industries. (PYMNTS)

Recent Trends in the Demand for AI Skills: Fed Study

Demand for AI skills is spreading across the US labor market, and while it’s too early to determine if AI will revolutionize work, early evidence suggests it is starting to impact a growing number of jobs. (Atlanta Fed)