For the past 6–9 months, the consensus has been that vertical AI applications are dead — or are about to be. As the frontier labs develop more capable models, as they go after verticals like coding, financial services and healthcare, and as they build out large teams of forward deployment engineers and Applied AI staffs, and expand their extensive connector ecosystem, many worry that Anthropic and OpenAI are going to eat the lunch of most — if not all — vertical AI application startups.

I agree with part of that. Many thin wrappers will die.

But for the most part, I disagree. And I disagree with huge respect for Anthropic and OpenAI — where many of my friends work, and I believe both will be enormously successful companies.

Because there were so many debates and uncertainties with this question, we made zero investment for the first four months of this year — which felt very uncomfortable at times. But believing in the discipline for investment deployment, we chose to take the time to clarify our thinking rather than rushing towards writing another check.

That meant having private 1:1 chats with insiders working at the frontier labs, observing closely what our portfolio companies were seeing from their customers, and talking with many founders building vertical AI applications. From that, I’ve formed some thoughts below.

These conversations have sharpened our thinking on where we think the best opportunities are in vertical AI in financial and professional services, what we look for in the startups that we back and partner with, and directly resulted in our latest two investments — Narrative (AI for compliance) and Bilrost (AI for commercial real estate underwriting).

I don’t have all the answers, and some of what I put out here could be proven wrong — especially as the AI landscape changes so rapidly, month to month.

But I’ve always believed it’s important to think independently and have a point of view (POV), especially at times that feel uncertain and in a world that’s becoming increasingly consensus-driven. So my intention here is to share our current POV and welcome any thoughts, perspectives, and debate from others, as we keep refining our thinking.

So where are the opportunities we see in vertical AI for financial and professional services? Where is the light?

Below are the six things we look for, including specific examples from startups we are lucky to partner with.

1/ The moat is the full embedded workflow, not automating a single task.

2/ The proprietary layer is the orchestration, not the model.

3/ The action layer is commoditizing; the judgment layer is not.

4/ The best opportunities are the ones the frontier labs won’t prioritize.

5/ The internal data and unspoken rules compound inside the vertical product.

6/ Mid-market can be a more attractive customer base.

#1. The moat is the full embedded workflow, not automating a single task.

The companies I’m most excited about aren’t selling a chat interface on top of a model. They’re rebuilding entire workflows from the ground up.

Bilrost builds AI for commercial real estate underwriting. While early customers spent the past year trying to incorporate ChatGPT or Claude into their workflow themselves, they’ve all abandoned the workaround once they saw Bilrost had already captured the full underwriting workflow: dozens of document types per loan, cross-validation of leases against rent rolls against bank statements, the firm’s own risk and origination guidelines applied to each decision, and direct integration with email, Drive, APIs, and borrower portals.

Because Bilrost is embedded across the entire workflow — intake, classification, extraction, cross-validation, decision-ready output — it has already produced an outcome that’s rare this early: its first customer has 3x’ed its underwriting capacity. In a world where most AI tools still deliver modest efficiency gains, deep workflow embedding has proven its ability to drive material revenue uplift.

Solving the last mile of the day-to-day workflow is what drives adoption and creates stickiness. While the frontier lab optimizes for the average case across many domains, a vertical company optimizes for the long tail of one.

#2. The proprietary layer is the orchestration, not the model.

Accrual (an Elsa Capital portfolio company) builds AI tools for accountants, and this past tax season showed what a vertical company focused entirely on the most mundane, mechanical parts of an accountant’s job can do. It processed more than 1.4 million pages across over 10,000 clients at many of the largest accounting firms in the country. 99.7% of worksheet edits in the platform were drafted by AI; across hundreds of thousands of edits, humans stepped in fewer than 1,000 times. A year ago, every one of those pages would have needed a human to read, extract, and incorporate into a return — and the firms using Accrual closed out meaningfully ahead of where they were last year.

So why won’t Anthropic or OpenAI just do this? Accrual’s founder and CEO, Cosmin Nicolaescu, explained: “the labs will absolutely build tools where the model itself solves the problem, but accounting workflows involve dozens of integrations, compliance rules, edge cases, and process logic accumulated over years — hard to replace with a single model interface.” Ask yourself: are you comfortable letting Claude or ChatGPT fully handle your taxes?

What makes the largest accounting firms trust Accrual is its proprietary layer — the expert instructions and tax-specific orchestration sitting on top of the foundation models. The model is an input, swapped for whatever’s best this month. The defensible asset is that instruction layer, refined against real returns season after season. The model improving is good for them: it upgrades a component they don’t have to build, while the part that’s theirs keeps compounding.

#3. The action layer is commoditizing. The judgment layer is not.

Reading a document, extracting a field, drafting a response — these outputs get better and cheaper every quarter as the models improve. What doesn’t commoditize is the unwritten judgment that drives decisions inside a regulated workflow. Most consequential decisions in a bank aren’t rules-driven; they live in the heads of a few experienced people. A frontier model can tell you what the regulation says. It can’t tell you how this firm weighs a marginal complaint against a top-tier partner relationship, or which exceptions the senior underwriter has been quietly making for fifteen years.

Narrative, which builds AI-powered compliance and risk management for financial services firms, solves this gap. One layer does the general actions — read, extract, summarize — and commoditizes as the model providers improve. The other encodes what isn’t written down: the institutional judgment that makes the output actually valuable.

Because Narrative encodes that judgment, it frees its customers from the “judgment constraint” that has historically bottlenecked how fast financial services firms can grow. That’s why, even as a seed-stage company, it has signed seven-figure contracts with bank customers.

Along a similar line, I recently heard Winston Weinberg, founder and CEO of Harvey — the legal AI startup now with over $200M ARR — explain their moat. Customers pay for two things: the work product and the judgment. “The first will get commoditized and the latter will not,” he said.

The successful vertical AI startups bundle the product and the judgment, and that bundle is the value.

#4. The best opportunities are ones the frontier labs won’t prioritize.

The durable companies build in markets too niche, too regulated, or too operationally complex for the labs to care about. Not niche as in small — commercial lending and compliance are massive. Niche as in the workflow demands deep domain knowledge, proprietary context, and years of relationship-building. These markets have been underserved so long that when a founder finally shows up and cares, the reaction is visceral. One founder told us a billionaire customer flew to San Francisco just to meet them after seeing the demo. That pull doesn’t happen in well-served markets.

In addition, what we’ve heard from friends who’ve worked at the frontier labs is that winning a vertical takes more than a well-designed product. To sell into these verticals, the labs would most likely need to stand up a deep, expert sales team aimed squarely at that buyer. That organizational lift — not the product — is why so many verticals stay open to startups whose entire GTM motion is dedicated to one kind of customer.

#5. The internal data and unspoken rules compound inside the vertical product.

The frontier labs train on external, public data. But ~90% of all data lives inside a company, and on top of that there’s an enormous body of unspoken rules, institutional judgment, and firm-specific preferences that were never written down. None of that is on the open internet for a model to learn. It only accrues to a vertical product that embeds deeply enough in the workflow to see it, capture it, and act on it.

So every workflow execution makes the system understand that customer better — its quirks, its exceptions, the things no one wrote down. This isn’t model weights getting smarter for everyone; it’s a decisioning engine getting dialed in on one institution, and it can’t be shortcut. Narrative sharpens on a firm’s risk profile with every run. Bilrost tunes nightly against each customer’s historical loans. Accrual built a loop that reconciles every draft prepared in the platform against the final filed return — reviewed and corrected by human accountants — and ships fixes before the next season. The work doesn’t reset each year; every returning client hands the system a richer starting point.

A model release doesn’t replicate that, because the most valuable asset — a customer’s own data and the unspoken rules around it, compounding over time — isn’t in the model. It’s in the vertical product that owns the end-to-end workflow.

#6. Mid-market can be a more attractive customer base.

Plenty of great vertical companies win in the enterprise. But for many verticals, mid-market can be the more attractive base: underserved, faster to close, and far less contested. The Fortune 100 is precisely where Anthropic, OpenAI, and the best-funded startups all concentrate their attention; the mid-market sits well outside that spotlight. And the economics aren’t a step down. Solve a real pain point and the ACV can run $500K to seven figures even for a pre-seed or seed-stage startup — signed much faster than a Fortune 100 procurement cycle, where the buyer also has a frontier lab’s engineers on speed dial.

There are countless unsexy, unloved verticals and mid-size customers whose names I’d never heard before this work — starved of attention from AI startups, and glad to pay seven figures to one that cares enough to solve their problem.

Final thoughts

Application software was never defensible on its own. The defensibility was never going to come from the technology itself. It comes from the orchestration, the judgment layer, the compounding data inside each account, the customers the labs ignore, and solving the last mile and edge cases of the day-to-day workflow.

AI will automate pieces of the work. The real, more exciting opportunity is redesigning how the work gets done — rebuilding the entire workflow from the ground up. The frontier labs supply the first half. The founders who know the work, and dedicate their lives to a single vertical, own the second.

That’s where we believe the light is in vertical AI applications.

If you are building in the space, or know a great founder who is, we’d love to hear from you. You can reach out here (if you’re a founder pitching) or email us directly.

You can find key takeaways below and the full recording of the event at the end.

Financial Services Moved Faster Than Anyone Expected

Everyone assumed tech companies would lead AI adoption. They were wrong.

Three industries surprised the OpenAI team by becoming early, aggressive adopters: #1 financial services, #2 healthcare, and #3 manufacturing & industrials. Financial services weren’t just first—they moved with unexpected speed and conviction.

Why?

Content-heavy workflows made them perfect for AI from day one. Investment memos, research reports, document analysis—these weren’t nice-to-haves, they were immediate use cases with clear ROI.

The economics were obvious. Offsetting even 10-20% of a banker’s or research analyst’s time delivers massive returns when you’re paying $200K+ in comp.

Client demand created competitive pressure. Wealth management clients started asking “How are you using AI to optimize my portfolio?” Firms that couldn’t answer lost business.

Enterprises Were Ready Before the Product Was

Aliisa’s first meeting at OpenAI was with Morgan Stanley in June 2022. At the time, OpenAI had zero enterprise certifications, no compliance framework, and was selling a $60,000 “AI innovation license” that literally gave you one hour of conversation about AI.

Morgan Stanley signed anyway. They built a wealth manager chatbot using retrieval technology before ChatGPT even existed.

Carlyle showed even stronger conviction. Before ChatGPT Enterprise had pricing figured out, they told OpenAI: “Just send me a contract. I’ll sign it.”

When the OpenAI team asked what Carlyle was building, they showed deal diligence automation, Black-Scholes analysis, and sophisticated quantitative use cases. “It blew our minds,” Aliisa recalls. “We weren’t even thinking about ChatGPT as that sophisticated.”

Insight for founders: your customers will often see use cases you never imagined. Even the largest enterprises in the world are ready to design and build the product together with you in your earliest days. 

The $20M Enterprise Budget That Can’t Ship

Here’s the uncomfortable truth: even enterprises with $20M AI budgets and direct Slack access to OpenAI engineers struggle to implement meaningful AI solutions.

The gap is real. Most companies drastically underestimate how hard it is to work with APIs. They lack developers who understand model strengths and weaknesses, can manage latency, run evals, and implement retrieval systems. Their procurement processes are built for buying seats of Salesforce, not for iterating on AI applications.

CIOs have massive budgets to spend, genuine risk tolerance, and big dreams. What’s missing? Service providers who can actually fill that gap.

Insight for founders: this is your opening. Enterprises will access AI primarily through application layers, not by building in-house. They need you to be that missing service provider. And unlike previous technology waves where startups had to “land and expand” from SMBs, enterprises are ready to be your first customer.

Two Waves of Disruption Ahead

During Aliisa’s time at OpenAI, the company’s own accounting team cut a 4-day Excel tax calculation process down to 10 seconds using ChatGPT, with verified accuracy. The impact was so profound that the employee who built it left to help other companies automate accounting full-time.

This is exactly the kind of transformation happening across financial services right now. We see two distinct waves:

Wave 1: Vertical AI displaces incumbent solutions. Startups are building AI copilots that simply do the job better than 40-year-old tools like Bloomberg Terminal, FactSet, or manual Excel processes. Better efficiency, better accuracy, 10x better user experience.

Wave 2: Complete workflow re-imagination. This is where it gets most interesting. Instead of just making the old process faster, founders will rethink the entire flow. Imagine one natural language interface managing all your taxes, investments, 401(k)s, and financial documents in a single place. Not automation—reinvention. The opportunity spans loan origination, insurance underwriting, portfolio management, risk analytics, compliance, legal doc review and much more.

Advice for Founders: Be Audacious

Aliisa’s advice to founders building in this space:

“My biggest advice is just be audacious and dream big, and don’t feel limited by the current constraints of the environment that you’re in. We’re going to see things change so dramatically over the next few years.

Instead of replacing an existing flow, rethink the entire flow, rethink the entire way that users might interact with or deal with the problem that you’re out to solve.

Lastly, just be audacious. Be bold. Make big bets, and don’t be afraid to sell to financial services because they’re along for the ride with you.”

We couldn’t agree more. The enterprises are ready. The budgets exist. The question isn’t whether this disruption will happen — it’s who will build it.

At Elsa Capital, we’re excited about backing the founders audacious enough to rethink the entire financial and professional services flow, not just automate the existing one.

Event Recording

You can watch the full conversation here: View recording

If you’re building vertical AI for financial or professional services, we’d love to hear from you. Reach out here or directly at sarah@elsacap.com.

Follow us on LinkedIn or subscribe to our Substack to stay updated on future events.

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Given the overwhelming positive response to this session, we're planning another founder office hour with Ryan in July, and with other operators and experts in future. If you’d like to join our future office hours to learn from the best in class operators, sign up here and we will notify you of our future events. We look forward to supporting you on the founder journey!

In addition, we want to thank the Anthropic team for providing API credits and access to higher rate limits through the Elsa Capital x Anthropic Startup Program.

Below we are sharing some key takeaways from this session.

ATL vs BTL: Know Your Altitude

One of the most critical frameworks: "Is this an ATL or BTL buyer?"

• ATL (Above The Line) buyers are often C-level executives, heads of innovation/AI focused on increasing revenue and decreasing costs

• BTL (Below The Line) buyers are often direct users, i.e. operators dealing with tactical problems

Make sure you give the right messaging (ATL vs. BTL) to the right stakeholders in the sales process.

The key trap: salespeople often nail the BTL value proposition, then when they get access to ATL executives, they deliver the same BTL presentation. The deal loses energy and momentum dies.

The AI-Native Sales Reality – what traditional B2B SaaS sales wisdom are now outdated?

We're in a unique moment where "buyers are learning how to buy, sellers are learning how to sell—everyone is trying to get educated at the same time."

Ryan talks about a few specific areas where traditional B2B SaaS sales wisdom may be outdated when selling AI-native products.

• Feature-first demos are outdated → Show outcomes and ROI from AI insights instead

• Controlling the data narrative is outdated → Address AI trust and data privacy concerns upfront

• Annual contracts as standard is outdated → Consider usage-based pricing and shorter proof-of-value periods

The Golden Rule of ICP: Bad Customers Are 10x Worse Than No Customers for Early Startups

"Signing a bad customer is 10x worse than not signing them," Ryan said. This isn't just about support headaches—bad customers drain engineering resources, create negative case studies, and prevent you from learning what drives real value.

The key is understanding not just who your customer is, but how they buy. Ryan breaks buyers into three personas:

• Songbird: Agreeable, says "that's great" to everything

• Owl: Analytical, asks endless who/what/where questions

• Rhino: Direct, results-focused

Your entire sales approach must adapt to their buying style.

Hiring: Leverage Networks + What to Look For in Your First GTM Hire

For early stage startups, 90% of successful sales hiring happens through networks and referrals.

In addition, don't just hire traditional sales backgrounds. At Stripe, some of the best sales performers were founders, MBAs, ex-consultants, etc. Hire people who can think strategically.

For early-stage startups, the profile for your first GTM hire should prioritize:

• Ideal profile: Startup experience, comfortable with ambiguity, builder mentality

• Red flags: Only worked with mature processes, needs extensive support systems

• Interview focus: Problem-solving ability, grit, and coachability over pure experience

• Compensation structure: Base + commission with accelerators for overachievement

Now is the Land Grab Time

"Now is land grab time." Don't let annual contracts prevent customers from trying your product. Give them 2-month trials. Get market share now—contract optimization comes later.

The outdated approach of forcing annual commitments upfront can cost founders deals in this fast-moving market with AI products.

The Power of Trumpeting

Use your investors strategically for ATL touchpoints. Multi-thread early in the sales process.

Make noise at the C-level early. The C-level executive who signs off on the budget shouldn't hear from you for the first time when you're trying to close a deal.

Become an Extension of Your Customer

Become the path of least resistance for your customers in the sales process. Ask yourself: "What can I do to help them?" Sometimes that means putting together the ROI analysis for them.

Building Credibility the Effective Way

Events and booths are often dead money (especially for budget-conscious startups). Better approaches:

• Sponsor coffee carts at conferences—everyone's holding a cup with your logo

• Speaking engagements beat booths every time

• Social proof is extremely important—obsess over getting the right logos early

Given the overwhelming positive response to this session, we're planning another founder office hour with Ryan in July (and with other operators in future). If you’d like to join our future office hours to learn from the best in class operators, sign up here and we will notify our next events. We look forward to supporting you on the founder journey!

Big thank you to Ryan for sharing his 10+ years of hard-earned sales wisdom with our founder community.

Despite advanced education, millions of finance professional spend most of their day on mundane, repetitive tasks. It’s not the long hours that are the problem—it’s what fills those hours: often low-value grunt work. Generative AI now creates the first real opportunity to change this reality.

I started Elsa Capital because I believe vertical GenAI applications in financial & professional services are going to create over $1 trillion in enterprise value. In addition to returns, this is also about creating a future where financial professionals find more meaning and joy in their work.

A future of both profit and purpose.

A Personal Note

I’ve spent the first half of my career in financial services and the second half in tech. Many assume I left finance because I hated it, seeking light in the exciting tech world. But that wasn’t the case.

I left Fidelity for Stripe because I was curious about the inner workings of building and operating durable, high-growth companies. Even in tech, I gravitated toward financial services—having worked at a payments innovator and built SaaS product for the CFO org. Finance remains my root, where my career began and where many of my close friends and connections remain.

Why start a VC firm? Because I believe we’re at an inflection point to see the largest transformation in financial & professional services in over 40 years—driven by Generative AI.

Financial Services: A Massive Sector Underserved by Technology

The financial and professional services sector represents ~25% of the global market cap through public companies alone—banks ($15.7T), insurance ($4.3T), investment firms ($5.5T), and professional services ($2.2T). Private partnerships like accounting firms, hedge funds, and law firms could add another $5T if they were public.

According to the Bureau of Economic Analysis, financial & professional services sectors contribute over 20% to U.S. GDP, double the Tech sector’s 9%.

Yet this massive sector has seen minimal technological innovation for 40 years:

• Microsoft Excel was launched in 1985

• Bloomberg Terminal was built in 1982

• FactSet dated back to 1978

Previous technology waves transformed other sectors dramatically:

• Internet transformed Retail and Media (e.g., Amazon, Google, Facebook)

• Mobile revolutionized Consumer in terms of how we transport, eat, live, and entertain (e.g., Uber, Doordash, Airbnb, TikTok)

• SaaS shifted value from on-premises software to cloud (e.g., Salesforce, ServiceNow)

Throughout all these technology waves, financial services remained largely unchanged, continuing to operate on technology founded over 40 years ago, back in the Computer era.

Why Now: Generative AI is Going to Fundamentally Transform Financial & Professional Services (in a Bigger Way than Computers)

About a year ago, generative AI emerged, giving us a glimpse of its potential to completely transform financial and professional services jobs, especially at junior levels and in roles containing large amounts of repetitive, mundane work.

For the first time in 40 years, financial services firms started to care deeply about technology—specifically GenAI. And that level of attention is not just bottom-up but comes directly from the C-suite across CEO, COO and CTO. Why?

1. Document-Heavy Workflows: Financial and professional services are filled with document/text-heavy processes that are ideal use cases for GenAI. One example: during my time in investment banking, I spent two months creating 200 company overview slides (”one-pagers”) for potential acquisition targets. Over 90% of that process involved manual copy-paste, extracting information from documents, synthesizing it, and creating standardized outputs—precisely what GenAI excels at.

2. Human Capital Intensity: Financial and professional services are fundamentally human-capital intensive. The largest cost item is compensation. Just three banks—JP Morgan, Bank of America, and Morgan Stanley—paid over $100B in compensation costs in 2024. For law firms, accounting firms, and consulting firms, ~75% of the cost structure is labor.

3. Competitive Pressure: Financial and professional services operate in highly competitive environments (unfortunately no monopoly or oligopoly) with relatively thin margins. Hence, there’s strong incentive to adopt AI for efficiency gains and real fear-of-missing-out (FOMO).

For a bank to improve profit, you can either bet on interest rates or market activities – which they can’t control – or focus on basis points (bps) of margin improvement, which they can control. For private firms and partnerships (hedge funds, law firms), there’s substantial incentive to reduce costs, so partners can pocket each additional dollar of profit in their own distributions. The founder of Millennium Management earned $3.8 billion in 2024, while Kirkland & Ellis partners collectively earned $5.3 billion in profit, with each equity partner averaging $9 million distribution. The incentives for adopting GenAI to reduce costs are evident.

Beyond the business case, there’s a human element that matters equally.

Creating a More Fulfilling Future of Work

For decades, jobs in financial & professional services have been filled with manual, mundane tasks—whether for accountants, lawyers, investment bankers, compliance officers, insurance brokers or underwriters.

The Industrial Revolution provides a relevant analogy. Before the Industrial Revolution (beginning in the 1760s), 80-90% of people worked in agriculture, performing physically repetitive labor. Technological advancements reduced the need for manual farming labor, and agricultural employment dropped to 22% of the workforce by the 1840s. This shift freed people from physically intensive, repetitive jobs, allowing them to move to more enjoyable work in factories and later offices.

Today’s knowledge workers in financial & professional services are in some ways similar to pre-industrial agriculture workers – performing mundane, low-level tasks, just on a cognitive level rather than a physical one.

Our hope is to reinvent the future of financial & professional services work – taking away the grunt work (where AI agents could do better) to make them more meaningful, creative, and joyful.

The $1 Trillion Value Creation Opportunity

I am not a traditional venture capitalist, and I did not grow up in the TAM world. As such, I’ve triangulated the value creation potential from three different angles. While no market sizing is perfect, this analysis gives me conviction that we are looking at a trillion-dollar ballpark for value creation opportunity for AI in financial & professional services.

Approach #1: Bottom-Up Labor Cost Analysis

Financial and professional services are fundamentally human-capital intensive. And the exciting part of GenAI applications (or agents) is their potential to augment and eventually replace some of these roles.

In the U.S., there are 11.8 million financial and professional service (PFS) roles, with a weighted average base salary of $91,393 (source: Elsa Capital analysis, based on the Bureau of Labor Statistics employment data).

Adding in bonuses (assume 25% of base) and benefits (assume 25% of base), the total labor costs for FPS jobs in the U.S. is around $1.6 trillion. Conservatively estimating global costs at twice the U.S. figure brings us to $3.24 trillion globally.

If vertical AI applications capture only 10% of these labor costs (a conservative estimate given AI tools can already outperform human banking or hedge fund analysts in specific areas), that represents a $324 billion ARR opportunity.

Applying a 5x EV/Sales multiple (conservative relative to the 7x average for SaaS companies and the 10x for financial data companies), we arrive at $1.6 trillion in enterprise value creation.

Source: Elsa Capital analysis

Approach #2: Top-down Labor Costs Analysis

Another way to size the labor costs is take a top-down approach estimating labor costs as a percentage of revenue or market cap. For the world’s largest banks like JP Morgan (8%), Morgan Stanley (14%), and Bank of America (13%), labor costs represent on average 11% of market cap.

Extending this analysis across the entire financial services and insurance industry (given similar business models) and also adding in private firms like accounting and law firms, this approach yields total labor costs on financial & professional services of $3.4 trillion, aligning with Approach #1.

Approach #3: Market Comparable (Comps) Analysis

Today’s financial and professional data and software providers command $520 billion in enterprise value. These companies primarily aggregate structured, tabular data, representing less than 10% of what professionals in these fields actually do.

The remaining 90% of professional work involves processing texts, analyzing thousands of pages of financial, legal, or tax documents.

If addressing only the tabular data portion has already created over $500B in enterprise value, generative AI that automates intellectual processing of unstructured documents should reasonably create multiple times of that value, i.e. in trillions.

Final Note

We are just at day 1 of massive value creation in vertical AI for financial & professional services in the coming decades. Although there are still many challenges ahead—from AI-specific issues like hallucinations and the chasm between pilot and multi-year contracts, to general startup challenges —we are going to see enormous opportunities for value creation and investment returns.

We believe now and the next 20 years is the unique time to seize this opportunity. The technical capabilities have matured, the market is open (in fact there is strong market pull), and the ROI case is compelling for financial and professional services firms to adopt AI.

We are fortunate to already have the opportunity to partner with amazing startups building AI agents or co-pilots for specific financial and professional service roles. These include Portrait Analytics (AI for public market investors), Pearson Labs (AI for law firms), Revi (AI for M&A deal prospecting), Dealops (AI for in-house deal pricing), Bayesline (AI for financial and risk analytics), Yellowpad (AI for in-house counsels), and Lucite (AI for insurance brokers).

One piece of advice often given to startup founders is to solve a problem you’ve personally experienced. This has a twofold advantage – one is that founders already come with deep domain expertise and unique insight, and the other is that founders genuinely care about the problem (because the journey of entrepreneurship is so hard that emotional connection matters).

As the founder of an investment firm, I feel fortunate to have the opportunity to invest in products that the younger me would have loved to use, and tools that I want many of my financial and professional services friends and connections to adopt to find more joy and meaning in their work.