Gabe Stengel - Building Investing Superintelligence - [Invest Like the Best, EP.492]
Gabe Stengel, founder of Rogo, discusses building AI-powered investment intelligence tools for financial professionals. He explains how rapidly improving AI models are enabling automation of complex financial workflows, the challenges of enterprise sales in finance, and his vision for transforming capital markets through AI infrastructure.
Summary
Gabe Stengel, CEO of Rogo, sits down to discuss the evolution of AI-powered tools for investment professionals and financial services. He traces the development of Rogo through multiple failed attempts before GPT-3, culminating in current capabilities enabled by O1 Pro and Opus 4.5 models. The conversation covers several major themes:
Product Evolution and Capability: Stengel describes distinct eras of AI capability in financial applications. Early attempts in high school and college failed completely. When GPT-3 launched, basic demos became possible but nothing worked reliably. The real breakthrough came with O1 Pro, which provided enough reliability for search-like tasks (calculating financial metrics reliably). Opus 4.5 represented the inflection point where models became capable of most junior investment professional and banker work with proper instructions and context.
Current User Base and Use Cases: Rogo's early adopters are primarily dealmakers and transaction professionals at large banks rather than public equity hedge funds. Users employ the tool to prepare data rooms, unpack complex information, coordinate calls with third parties, and execute full deal lifecycles. The product integrates into behind-the-scenes systems including CRMs, portfolio monitoring systems, and LP communication infrastructure—not just as a chatbot interface but as deep infrastructure plumbing.
Business Model and Competitive Positioning: Rogo operates as traditional enterprise software with per-seat pricing, requiring human sales teams to land deals with large financial institutions. This contrasts sharply with token-consumption models used by companies like Anthropic and OpenAI. Stengel argues this limitation is actually an advantage because it allows Rogo to build much deeper plumbing—compliance systems, data rooms, audit trails, MNPI handling—that labs have no incentive to build. He envisions Rogo eventually becoming infrastructure for private markets transactions, similar to how Bloomberg created an exchange for information and transactions.
Data, Models, and Infrastructure: The company has evolved from a "Rube Goldberg contraption" of 60 model calls to a cleaner architecture prioritizing the most performant and cost-efficient models for specific tasks. Critical infrastructure includes compliance and regulatory plumbing around MNPI (material non-public information), secure data rooms for transactions, and full audit trails showing how AI decisions were made. Stengel emphasizes that infrastructure and harness matter more than raw model intelligence—how you present and deploy models determines their effectiveness.
Accuracy and Auditability: Rather than chasing perfect accuracy, Rogo prioritizes auditability and transparency. Users need to understand where data comes from and how conclusions were reached, even if occasional errors occur. As AI moves from copilot (information gathering) to autopilot (autonomous execution), auditability becomes critical for regulatory compliance and debugging agent decisions.
Organizational Talent and Scaling: The company has hired heavily from investment banks and finance firms (Goldman, Citi, Jefferies, Apollo, Blackstone, etc.). Rather than pure engineering talent, Stengel values former founders with product intuition and domain expertise who can navigate ambiguity. He emphasizes that enablement and training is the bottleneck for rapid scaling—making employees productive quickly requires sophisticated internal AI tools and knowledge management (called "Shrek" internally) that synthesizes all company conversations and institutional knowledge.
Future of Capital Markets: Stengel articulates a vision where capital markets become dramatically more efficient and accessible. He draws parallels to how mortgages moved from human intermediaries to online platforms (Rocket Mortgage), predicting similar transformation in M&A, capital raising, and asset pricing. The timeline compression could be dramatic—JP Morgan's current 5-month deal analysis processes could compress to 5 minutes. This expanded efficiency and liquidity should accelerate innovation and economic growth globally.
Innovator's Dilemma for Finance: Unlike most industries that have seen major disruptions, investment firms and banks haven't faced a forcing function to reinvent in decades. AI represents the first major innovator's dilemma moment for financial services, creating opportunity for AI-native competitors. Established firms must completely rethink operations rather than incrementally improve.
Founder Experience and Challenges: Stengel openly discusses emotional lows, particularly raising Series A when 40 top-tier investors passed ("being broken up with by 40 girlfriends"). He traces why investors missed the opportunity: underestimating TAM in finance, poor product early on, and insufficient track record of executing through obstacles. Eventually, founders who understood exponential AI improvement and demonstrated perseverance attracted capital.
Leadership and Culture: As CEO, Stengel maintains intense focus on AI adoption metrics internally, publicly ranking departments by AI tool usage to drive culture change. He describes his leadership approach as combining his mother's irrational confidence in his abilities with his father's rigorous standards for excellence. He emphasizes the need to put on confidence even when deeply scared, while being transparent about concerns and building very specific roadmaps to $100B outcomes.
Competing with Labs: Stengel argues the path to sustainability is building perpendicular capabilities that labs won't—domain-specific workflows, compliance systems, transaction infrastructure, data connectivity. Finance generates sufficient TAM ($5-10B+ per niche) that building deep vertical stacks represents sound strategy rather than competing head-to-head on model capability.
About this episode
Gabe Stengel is the co-founder and CEO of Rogo, the AI platform for finance. He believes the best investors will spend the next several years reinventing their firms around AI, and Rogo is trying to build the infrastructure that allows them to do it. We discuss how Rogo evolved alongside the frontier models, why the last mile and the harness around the models matter so much, what happens when every portfolio manager can deploy thousands of agents against a problem, and how AI could transform the way capital is raised, assets are priced, and deals get done. We also cover which investing skills become more valuable as AI improves, the move from seat-based to outcome-based pricing, Rogo’s internal company brain called Shrek, the 40 investor rejections Gabe received before his Series A, why building in applied AI requires extraordinary aggression, and what it takes to become a black hole for talent and capital. Please enjoy my conversation with Gabe Stengel. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at colossus.com/subscribe. ----- Ramp’s mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, Vanta continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to vanta.com/invest. ----- WorkOS is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit ridgeline.ai. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:01:24) The Model Eras of Rogo (00:04:15) Why the Last Mile Mattered (00:06:14) Who’s Using Rogo Right Now (00:10:55) Which Investor Skills Still Matter (00:12:23) Inside Rogo's Data Stack (00:14:33) Competing With the Frontier Labs (00:16:57) Why the Harness Matters Most (00:18:05) What Makes a Vertical AI Winner (00:22:00) How Firms Buy AI Software (00:23:43) From Seats to Outcome Pricing (00:31:19) Auditability Beats Accuracy (00:32:55) Scaling Enterprise Sales Fast (00:34:48) Meet Shrek, Rogo's Company Brain (00:35:49) The Pitch to Great Talent (00:39:37) What Chewing Glass Feels Like (00:42:18) Forty Investor Rejections (00:47:41) Finance's Innovator's Dilemma (00:50:22) Questions Every Firm Should Ask (00:53:09) What Remains Most Uncertain (00:56:46) Becoming a Black Hole for Talent (00:57:56) The Kindest Thing
Key Insights
- Rogo went through two failed attempts before launch, and current success is enabled by exponential improvements in underlying models—the business model and team were similar but models now provide sufficient reliability for real financial tasks.
- The company discovered that enterprise sales limitations in finance are actually competitive advantages because they require building compliance plumbing, data infrastructure, and audit systems that AI labs have no economic incentive to build.
- Most junior investment professional work (memo writing, DDQs, analysis) is now within AI capability with proper instruction and context, but autopilot execution requires unprecedented levels of auditability and regulatory adherence.
- Auditability matters more than raw accuracy in financial AI—users need to understand how conclusions were reached and trace all inputs, especially for regulatory discovery and agent debugging.
- The current bottleneck for enterprise AI scaling is employee enablement and knowledge management, not raw engineering capacity, which is why Rogo built internal systems to synthesize company conversations into accessible institutional knowledge.
- Investors initially rejected Rogo in Series A not because of the idea but due to underestimating finance TAM, poor product-market fit at the time, and lack of founder track record of navigating obstacles—success required demonstrating perseverance through rejection.
- The entire 5-month M&A deal analysis process at large banks could compress to 5 minutes with AI, creating an innovator's dilemma for firms that haven't faced forcing function to reinvent in decades.
- Former founders make better technical hires than pure engineers because they provide product intuition, operational judgment, and ability to navigate ambiguity, which matters more than raw coding ability in applied AI.
- Private markets remain more attractive than public equities for AI applications because most infrastructure is still manually human-coordinated rather than automated, creating more leverage for AI to create value.
- Leadership requires maintaining confidence in $100B+ outcomes while simultaneously being paranoid about the thousand ways the business could collapse, channeling insecurity into preparation rather than paralysis.
- The future of capital markets likely mirrors mortgage industry transformation—capital raising, M&A, and asset pricing will move from human intermediaries to digital platforms accessible to small businesses and entrepreneurs currently excluded.
- Models are becoming capable of work that requires less prescriptive routing as they improve, shifting engineering effort from complex orchestration toward building proper data tools, compliance systems, and integration infrastructure.
Topics
Transcript
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