Reinventing Wall Street: Rogo's AI Revolution with Gabriel Stengel

Mike Mignano, Partner at Lightspeed, introduces this week's episode featuring Gabriel Stengel, founder and CEO of Rogo. Rogo is a revolutionary AI platform that transforms the grueling work traditionally done by junior investment bankers - analyzing earnings, benchmarking companies, and building pitch decks - completing these tasks in seconds rather than the sleepless weeks they typically require.

Gabriel brings unique credibility to this space as a former analyst at Lazard, where he witnessed firsthand the inefficiencies plaguing the finance industry. Together with his co-founders, he built Rogo to address these pain points, and the platform has already gained traction with the world's top banks and hedge funds, attracting significant investor interest.

The conversation explores Gabriel's journey from investment banking to entrepreneurship, the inception story of Rogo, and his vision for AI's role in the future of banking and finance.

Timestamp: [0:02-0:55]

Gabriel and his co-founder John's journey began at Princeton University, where they collaborated on their senior thesis - a joint project between computer science and economics that created an AI chatbot for financial econometrics. While the technology was primitive by today's standards, using semantic parsing and context-free grammars, it sparked their excitement about natural language interfaces for financial analysis.

They attempted to commercialize this early concept but couldn't find a viable path forward. This led both founders into traditional finance careers - Gabriel at Lazard as an investment banker, and John at Barclays then JP Morgan. Gabriel's unique position at Lazard gave him exposure to both investment banking teams and data science initiatives, providing crucial insights into how financial institutions approach productivity challenges.

The company that would become Rogo was technically founded in 2019 as an LLC, but the actual business launch happened in late 2021, making it about three and a half years old at the time of this interview.

Timestamp: [0:55-2:46]

The original Princeton project focused on econometrics - statistical methods used in economics research, such as running regressions, auto-regressions, or finding instrumental variables to help economics postgraduates with their research work. However, today's Rogo bears little resemblance to that early academic tool.

The modern platform serves investors and investment bankers with company research, due diligence, and unique investment thesis development. While it still analyzes structured data, the focus has shifted from economic datasets to company-specific information. For example, users might request analysis like running regressions of revenue multiples on margin expansion at large healthcare companies to understand valuation drivers, or tracking share price returns year-to-date for specific company baskets.

This evolution reflects the founders' journey from academic research to real-world financial applications, shaped by their direct experience in investment banking roles.

Timestamp: [2:46-4:07]

Gabriel's experience at Lazard provided the crucial insights that shaped Rogo's development. As a banking analyst, he performed the exact types of work that Rogo now automates: building financial models for potential acquisitions, creating PowerPoint presentations, conducting industry research, and assembling PIBs (Public Information Books).

PIBs represent a particularly labor-intensive aspect of investment banking. When a senior banker needs to quickly understand a company, they request a PIB - a comprehensive 200-page PDF containing the last two 10-Ks, recent 10-Qs, initiating broker research coverage, recent analyst reports, and relevant news articles. This information packet must be compiled within 24 hours to help the managing director get smart on the company.

Despite the demanding nature of the work, Gabriel emphasizes that he genuinely enjoyed investment banking for the access it provided to interesting executives and companies, and the insights into M&A thinking and capital allocation strategies.

Timestamp: [4:07-5:49]

While the obvious application of AI in investment banking is automating mundane tasks like PIB creation and benchmarking, Gabriel's vision for Rogo extends far beyond simple automation. The core thesis has always been about empowering people to do things they couldn't do before, not just making existing work faster.

This philosophy traces back to their original Princeton thesis, which aimed to give economics students the power of Python programming through natural language interfaces. Similarly, Rogo seeks to democratize advanced financial analysis capabilities that were previously only available to those with dedicated teams of data scientists or junior analysts.

This dual approach - handling routine work while enabling entirely new analytical capabilities - represents Rogo's differentiated positioning in the market. Rather than simply replacing human work, the platform aims to augment human capabilities and unlock new possibilities for financial analysis and insight generation.

Timestamp: [5:49-7:26]

• Rogo originated from a Princeton University senior thesis project that created an AI chatbot for financial econometrics, demonstrating early vision for natural language interfaces in finance
• The platform has evolved from academic econometrics to practical investment banking applications, focusing on company research, due diligence, and investment thesis development
• Gabriel's experience at Lazard provided crucial insights into investment banking inefficiencies, particularly around labor-intensive tasks like creating PIBs (Public Information Books)
• The company's vision goes beyond simple automation to empowering users with entirely new analytical capabilities previously requiring dedicated teams of specialists
• Traditional investment banking processes like PIB creation (200-page research packets compiled in 24 hours) represent clear opportunities for AI transformation
• The founders' combination of technical expertise and direct finance industry experience provides unique credibility and market understanding

Timestamp: [0:02-7:26]

People:

• Gabriel Stengel - Founder and CEO of Rogo, former investment banking analyst at Lazard
• John - Co-founder of Rogo, Gabriel's Princeton classmate and collaborator on senior thesis
• Mike Mignano - Partner at Lightspeed, host of Generative Now podcast

Companies/Products:

• Rogo - AI platform for investment banking and financial analysis
• Lazard - Investment bank where Gabriel worked as an analyst
• Barclays - Investment bank where John worked
• JP Morgan - Investment bank where John later worked
• Data Dog - Example company mentioned in PIB discussion
• ChatGPT - Referenced as comparison for modern information gathering

Concepts:

• PIB (Public Information Book) - Comprehensive research packet compiled for senior bankers
• Econometrics - Statistical methods used in economics research
• 10-K and 10-Q - SEC filing documents for public companies
• Natural Language Interface - Technology allowing users to interact with systems using everyday language

Timestamp: [0:02-7:26]

Rogo functions essentially as a specialized version of deep research capabilities, but specifically designed for financial professionals. While general AI tools like ChatGPT can handle basic information gathering and research queries, Rogo provides access to the comprehensive data ecosystem that financial institutions rely on.

Unlike general AI assistants that only access web information and basic code interpreters, Rogo integrates with banks' internal systems including CRM platforms, SharePoint repositories, and precedent transaction analyses. Additionally, it connects to essential financial data sources like Excel, SEC filings, FactSet, Cap IQ, and Pitchbook - the specialized tools that form the backbone of Wall Street analysis.

This integration transforms Rogo from a general-purpose AI into a specialized Wall Street analyst, trained specifically for the unique workflows and requirements of financial professionals.

Timestamp: [7:32-8:32]

Gabriel identifies three distinct elements that differentiate Rogo and create defensible intellectual property. The first pillar is content and data access - Rogo works with providers like S&P Global Cap IQ to ensure mutual customers can query professional-grade financial databases through the platform. This represents a complex business data licensing challenge that creates barriers to entry.

The second pillar focuses on tool integration capabilities. Even the most advanced language models struggle with practical financial work without access to specialized tools. While an LLM might build financial models in Python, these aren't auditable or formatted to professional standards. Similarly, creating PowerPoint presentations requires specific formatting and output specifications that general models can't match.

The third and most crucial pillar is specialized reasoning and model quality. Rogo employs post-training and reinforcement fine-tuning techniques to teach models the specific workflows, formatting standards, and analytical approaches that bankers and investors use daily. This domain expertise can't be replicated simply by having access to powerful base models.

Timestamp: [8:32-10:22]

Rogo's approach involves building upon existing foundation models like GPT-4 or Gemini 2.5 Pro through fine-tuning and distillation techniques, rather than training models from scratch. The company focuses on post-training methods, particularly reinforcement learning, to teach models the specific skills required for financial analysis.

The training process addresses real-world workflows that financial professionals encounter daily. For example, when a Goldman Sachs analyst needs to find precedent transactions for the Microsoft-Activision deal, they would typically navigate to Edgar (the SEC's filing database), search for specific documents like DEF14A forms, and manually scan through them using control-F searches.

Rather than relying on inefficient browser automation, Rogo creates specialized tools for filing searches with ticker filtering, document type selection, and date range capabilities. The company then develops specialized models for different tool categories - some optimized for document querying with appropriate keywords, others for Excel formatting - ensuring lower latency and higher accuracy than general-purpose approaches.

Timestamp: [10:22-12:04]

When asked about competition from major AI companies like OpenAI, Anthropic, and xAI who may want access to similar financial data sources, Gabriel expresses confidence in Rogo's positioning. He argues that improvements in foundation models actually benefit Rogo, as the company can quickly integrate next-generation intelligence into their specialized system.

The key differentiator lies in focused specialization. While major AI companies concentrate on generic consumer applications, competing with Microsoft Office, and coding assistance, Rogo dedicates all its resources to financial tools and workflows. This focused approach creates substantial product complexity and workflow intricacy that generalist companies underestimate.

Gabriel anticipates that by the time major tech companies turn serious attention to finance, Rogo will have accumulated substantial user data, evaluation frameworks, and workflow understanding that maintains their competitive edge. Finance represents a large enough market to eventually attract attention from big tech, but the domain's complexity provides a defensive moat during the critical early years.

Timestamp: [12:04-13:48]

• Rogo functions as specialized "deep research" for finance, integrating internal bank data with premium financial data sources that general AI tools cannot access
• The platform's competitive advantage rests on three pillars: exclusive data licensing, specialized tool integration, and domain-specific model training
• Unlike general LLMs, Rogo's models are fine-tuned through post-training and reinforcement learning to master financial workflows and formatting standards
• The company builds custom tools and specialized models for different financial tasks, optimizing for lower latency and higher accuracy than browser-based automation
• Rogo's focused specialization in finance creates defensible complexity that generalist AI companies underestimate, providing competitive protection during critical growth phases
• Foundation model improvements benefit Rogo's capabilities while the company maintains advantage through accumulated financial domain expertise and user data

Timestamp: [7:32-13:48]

Companies/Products:

• S&P Global Cap IQ - Financial data provider that Rogo integrates with for mutual customers
• FactSet - Professional financial data and analytics platform
• Pitchbook - Private market intelligence platform
• Edgar - SEC's electronic filing database system
• Goldman Sachs - Investment bank used as example for analyst workflows
• Microsoft-Activision - Major acquisition deal referenced for precedent transaction analysis
• OpenAI, Anthropic, xAI - Major AI companies positioned as potential competitors

Technical Concepts:

• Post-training - Machine learning technique for specializing pre-trained models
• Reinforcement Fine-tuning - Method for teaching models specific workflows and behaviors
• Model Distillation - Process of creating smaller, specialized models from larger foundation models
• DEF14A - SEC proxy statement filing type containing transaction details
• Three-statement Model - Financial modeling approach covering income statement, balance sheet, and cash flow

Financial Tools/Systems:

• CRM - Customer relationship management systems used by banks
• SharePoint - Microsoft collaboration platform for document management
• Excel - Spreadsheet software essential for financial modeling

Timestamp: [7:32-13:48]

Gabriel strongly believes that value increasingly resides in the application layer rather than at the model level. He uses his personal experience with ChatGPT as an example - despite potentially inferior models, he finds it difficult to switch due to the personalized experience built through conversation history and context understanding.

The application layer creates sticky advantages through personalization, workflow optimization, and user data accumulation. In contrast, the model layer offers limited sustainable differentiation, with no provider maintaining more than a six-month technical lead. Model competition focuses primarily on cost efficiency, service reliability, and latency rather than fundamental capability gaps.

This philosophy reinforces Rogo's strategy of specializing deeply in financial workflows rather than competing on foundational AI capabilities, creating defensible value through domain expertise and user experience optimization.

Timestamp: [13:54-15:26]

Rogo began development before ChatGPT's public release, when only GPT-3 was available. While technologically aware observers recognized the profound implications of these early models, the target market remained deeply skeptical about AI's potential impact on financial services.

Banks, hedge funds, and private equity firms consistently dismissed Gabriel's pitches, viewing the technology as unproven or irrelevant to their operations. This skepticism created a challenging environment where market education became as important as product development.

The company spent approximately 18-24 months learning what the market actually wanted while simultaneously educating potential customers about AI's capabilities. This period involved determining specific use cases, understanding relative priorities, developing effective sales pitches, and identifying the right decision-makers within target organizations.

Timestamp: [15:26-16:48]

The initial customer development process proved extremely challenging, with Rogo lacking meaningful customer adoption until late 2023. Gabriel describes the desperate founder mentality of doing "a whole song and dance" just to get someone to use the platform, only to have the product fail after 25 minutes of usage.

The early value proposition centered on speed and interface improvements: replacing clunky legacy tools like Bloomberg terminals and Edgar SEC filing databases with natural language queries that could return answers in five seconds instead of complex navigation procedures.

However, pitching a generic search or chatbot interface created an impossible development challenge. Users would inevitably test the one expected use case plus five unexpected ones, revealing gaps in functionality and causing the product to fail user expectations. This experience taught the team the importance of focused, specific use cases rather than broad, general-purpose tools.

Timestamp: [16:48-17:52]

Gabriel's first significant investor was Kevin Ryan from AlleyCorp, a prominent figure in New York City's tech ecosystem who founded companies like MongoDB, Gilt, and Business Insider, and served as CEO of DoubleClick. Ryan's investment decision wasn't primarily based on AI's transformative potential, but rather on founder-market fit and domain opportunity.

Ryan's investment thesis focused on Gabriel and John as technologically sophisticated founders who intimately understood investment banking operations. Having grown up in New York's financial ecosystem, Ryan recognized investment banking as a sector ripe for technological disruption.

The relationship developed organically through Gabriel's New York connections and a previous internship where Ryan spoke. Ryan maintained regular contact, sending check-in emails every six months asking about Gabriel's career plans and interests. When Gabriel developed the Rogo concept while at Lazard, Ryan became the natural first contact, leading to their seed funding round.

Timestamp: [17:52-19:09]

Rogo's path to product-market fit involved two critical pivots that transformed customer reception. The first was expanding beyond structured data analysis to include unstructured data processing. The original econometrics-focused product created charts and scatter plots from structured datasets, but failed to leverage LLMs' core strength in processing unstructured information.

The second breakthrough came from a fundamental change in sales approach and positioning. Gabriel recalls a transformative call with a large private equity firm where the product demonstration exceeded all previous performance, largely due to the new unstructured data capabilities.

When asked for pricing, Gabriel impulsively quoted $2 million annually - a number he pulled from thin air. The client's immediate acceptance ("Okay makes sense") provided crucial market validation. Gabriel immediately called his co-founder, recognizing that serious consideration of such pricing indicated they were building something genuinely valuable to enterprise customers.

Timestamp: [19:09-20:51]

The shift from structured to unstructured data processing provided both user value and significant development advantages for Rogo. While structured data analysis requires carefully designed databases with comprehensive documentation and data dictionaries, unstructured data processing needs only elastic search indices, intelligent keyword search prompts, and large context windows.

This architectural simplification dramatically reduced the time and cost required to build robust functionality. Instead of extensive database engineering and schema design, the team could focus on search optimization and prompt engineering to deliver immediate value to users.

This insight reflects a broader trend in AI startups where unstructured data processing capabilities allow teams to bypass traditional data engineering complexities, accelerating development cycles and reducing infrastructure requirements while delivering superior user experiences.

Timestamp: [20:51-21:47]

• Application layer value increasingly outweighs model provider advantages due to personalization, workflow optimization, and switching costs created by user data accumulation
• Pre-ChatGPT market education proved as challenging as product development, with financial institutions dismissing AI's potential impact for nearly two years
• Early customer acquisition required desperate founder tactics, with generic chatbot positioning creating impossible development expectations across unlimited use cases
• Founder-market fit and domain expertise often matter more to early investors than technology trends, as demonstrated by Kevin Ryan's investment thesis
• Product-market fit breakthroughs came from expanding beyond structured to unstructured data processing and fundamentally changing sales positioning rather than incremental improvements
• Unstructured data architecture provides significant development advantages over structured approaches, requiring simpler infrastructure while delivering superior user experiences

Timestamp: [13:54-21:47]

People:

• Kevin Ryan - Early investor and leader of AlleyCorp, described as "self-proclaimed godfather of New York City tech"
• Gabriel Stengel - Founder being interviewed, sharing his fundraising and product development journey
• John - Gabriel's co-founder, mentioned in investor context

Companies/Products:

• AlleyCorp - New York-based investment firm led by Kevin Ryan
• MongoDB - Database company co-founded by Kevin Ryan
• Gilt - E-commerce company founded by Kevin Ryan
• Business Insider - Media company founded by Kevin Ryan
• DoubleClick - Advertising technology company where Kevin Ryan served as CEO
• ChatGPT, Gemini, Claude - AI models referenced for application layer comparison
• Bloomberg - Financial data terminal mentioned as legacy tool to replace
• Edgar - SEC's electronic filing database system referenced as clunky interface

Technical Concepts:

• GPT-3 - Early AI model available before ChatGPT's public release
• Structured vs Unstructured Data - Key technical distinction in Rogo's product evolution
• Elastic Search Indices - Search technology infrastructure for unstructured data processing
• Data Dictionaries - Documentation required for structured database queries
• Context Window - AI model capability for processing large amounts of text

Timestamp: [13:54-21:47]

Rogo's customer base forced the company to prioritize enterprise-grade security features much earlier than typical startups. The nature of financial institutions demanded multi-cloud capabilities, on-premises deployment options, sophisticated permissioning systems, role-based access controls, and comprehensive governance frameworks from the outset.

Rather than viewing security as a burden that slows development, Gabriel's team reframed it as a core value proposition and product feature. This mindset shift transformed enterprise requirements from obstacles into competitive advantages, treating security infrastructure as essential to product-market fit rather than a necessary evil.

This approach required learning complex enterprise architecture through trial by fire, bringing in specialized expertise when needed, and working closely with partners and stakeholders to solve first-principles engineering problems. While it reduced feature velocity, it became a prerequisite for accessing their target market.

Timestamp: [21:53-23:15]

The AI startup landscape demands unprecedented speed to stay ahead of rapidly advancing foundation models and potential competition from tech giants. However, financial services impose unique constraints where the cost of moving fast and making mistakes can be catastrophically high, requiring careful balance between velocity and risk management.

Gabriel acknowledges that Rogo moves slower than consumer-focused companies due to regulatory and security requirements. Success requires constantly threading the needle between competing priorities, making thoughtful trade-offs, and allocating bandwidth across multiple critical problems simultaneously.

The key insight is that security investments can actually accelerate progress in certain contexts. While horizontal companies like Glean might skip enterprise features to compete broadly against ChatGPT, finance-focused companies find that robust security capabilities enable faster customer acquisition within their target market of large financial institutions.

Timestamp: [23:15-25:21]

Gabriel illustrates Rogo's practical impact through a specific investment banking workflow. When presenting potential acquisitions to healthcare companies, analysts traditionally create detailed company profiles including CEO information, strategic focus, rationale for acquisition, and financial snapshots. This process historically required hours of research, synthesis, and writing.

The challenge intensifies when analysts lack domain expertise - an investment banker analyzing biotechnology companies like Regeneron or Vertex without oncology knowledge must invest significant time understanding R&D pipelines, competitive positioning, and market dynamics before creating coherent presentations.

With Rogo, analysts can request ten PowerPoint slide pages covering ten companies and receive comprehensive output within 30 seconds. This dramatic time compression allows professionals to focus on higher-value analytical work rather than basic information gathering and synthesis.

Timestamp: [25:54-26:54]

Beyond junior analyst efficiency gains, Rogo transforms workflows for senior investment professionals by eliminating traditional delegation hierarchies. Instead of requesting PIBs from junior staff and waiting for deliverables, senior bankers can directly access comprehensive company intelligence through conversational interfaces.

This shift enables immediate deep-dive analysis without preparation delays. Senior professionals can explore questions about market positioning, competitive dynamics, product focus, M&A strategies, and performance drivers through interactive threads rather than formal research requests.

This transformation reduces hierarchical friction and accelerates decision-making by providing senior professionals with direct access to analytical capabilities traditionally requiring dedicated research teams and formal deliverable processes.

Timestamp: [26:54-27:23]

Gabriel predicts that AI will replace specific workflows rather than entirely displacing human professionals, drawing parallels to historical technological transitions. He uses the example of PIB creation and delivery, where technology eliminated manual tasks like printing, binding, and physical delivery to managing directors' homes, but professionals found their time filled with other valuable work.

Financial institutions maintain competitive pressures and high-performance cultures that prevent simple workforce reduction. Instead of resting on improved margins, these organizations typically intensify their analytical capabilities and expand their scope of work when efficiency tools become available.

The future involves different types of work rather than less work. Professionals may spend less time on data manipulation, presentation preparation, and Excel modeling, but more time on strategic thinking, stakeholder relationship building, and executive interaction - higher-value activities that require human judgment and relationship skills.

Timestamp: [27:23-28:33]

• Enterprise security requirements can become competitive advantages rather than development obstacles when positioned as core product features rather than compliance burdens
• AI startups in regulated industries must balance unprecedented speed demands with high-stakes risk management, requiring constant trade-off optimization and disciplined bandwidth allocation
• Real-world AI impact in finance focuses on dramatic time compression for knowledge-intensive tasks, reducing hours of research and synthesis to seconds of automated analysis
• Senior professionals benefit from disintermediation of traditional delegation hierarchies, gaining direct access to analytical capabilities previously requiring dedicated research teams
• AI will likely replace specific workflows rather than entire job categories, shifting human work toward higher-value strategic thinking and relationship building activities
• Financial institutions' competitive cultures prevent simple workforce reduction, instead driving expansion of analytical scope and capability when efficiency tools become available

Timestamp: [21:53-28:33]

Companies/Products:

• Glean - Horizontal AI company referenced as comparison for trade-off decisions
• ChatGPT - Referenced as competitive threat for horizontal AI companies
• Perplexity - AI search company mentioned as OpenAI competitor
• Co-pilot - Microsoft's AI assistant referenced in competitive context
• Anthropic - AI company mentioned as OpenAI competitor
• OpenAI - Referenced for different security trade-offs than finance-focused companies
• Regeneron - Biotechnology company used as example of complex analysis
• Vertex - Biotechnology company used as example requiring domain expertise
• Data Dog - Software company used as example for senior banker workflow

Technical/Business Concepts:

• Multi-cloud - Deployment capability required by enterprise customers
• On-premises deployment - Security requirement for financial institutions
• RBAC (Role-Based Access Control) - Enterprise permissioning system mentioned
• Governance systems - Enterprise compliance framework requirement
• PIB (Public Information Book) - Traditional investment banking deliverable
• R&D pipeline - Pharmaceutical research and development processes

Industry References:

• Investment banking - Primary industry context for workflow examples
• Healthcare company - Client type in acquisition scenario example
• Biotechnology/Oncology - Domain expertise example for analyst challenges

Timestamp: [21:53-28:33]

Large law firms are proactively building proprietary AI tools using their accumulated contracts and legal data, viewing this information as unique competitive assets. These firms resist sharing proprietary insights with companies like OpenAI or Anthropic, fearing that such partnerships could ultimately enable tools that displace their services.

In contrast, investment banks approach AI differently, recognizing that their core strengths lie in relationships, business acumen, and negotiation capabilities rather than technology development. Gabriel suggests that financial institutions will likely pursue partnership models rather than internal development, working with specialized AI companies to create mutually beneficial tools.

This strategic difference reflects varying assessments of core competencies and competitive moats across professional services industries, with legal firms viewing data as proprietary assets while financial firms focus on relationship and advisory capabilities.

Timestamp: [28:40-29:51]

Gabriel advocates for collaborative approaches between AI companies and financial institutions, citing Harvey AI's strategy in legal services as a successful model. Harvey's founder Winston emphasizes working with law firms to create joint products that both parties can offer to customers, representing a middle ground between pure build-versus-buy decisions.

This partnership approach allows financial institutions to leverage specialized AI expertise while maintaining control over client relationships and business models. Rather than competing directly with banks, AI companies can enhance their capabilities and help transform their service delivery models.

The partnership model also addresses concerns about proprietary data sharing by creating aligned incentives between AI providers and professional services firms, ensuring that technological advancement benefits both parties rather than creating competitive threats.

Timestamp: [29:51-30:09]

Gabriel identifies a significant awareness gap between legal and financial professionals regarding AI's potential impact on their daily work. Legal professionals have already recognized that substantial portions of their jobs face automation, creating urgency around AI adoption and strategic responses.

Financial professionals haven't yet experienced the same realization, partly because widespread job displacement hasn't occurred across major corporations or professional services organizations. Without visible examples of AI-driven workforce changes at companies like Apple, Disney, or Microsoft, financial firms continue operating with traditional assumptions about human-AI collaboration.

This awareness gap creates both opportunity and challenge for AI companies targeting financial services. While it reduces immediate competitive pressure, it also requires substantial market education and change management to drive adoption among conservative financial institutions.

Timestamp: [30:09-31:26]

Gabriel and Mike discuss the significant opportunity for early AI adopters in professional services to gain competitive advantages through enhanced productivity and margin improvements. Forward-thinking firms can arm their staff with AI capabilities that function as "superpowers," dramatically improving efficiency while competitors maintain traditional workflows.

This first-mover advantage becomes particularly valuable in professional services where marginal improvements in productivity directly translate to profitability gains. Early adopters can capture market share by offering superior service delivery speed and quality while maintaining or reducing costs.

However, the opportunity extends beyond incumbent optimization to entirely new business models built around AI-native operations, suggesting that competitive pressure will emerge from multiple directions as the technology matures.

Timestamp: [31:26-31:51]

Emerging companies are building professional services organizations from the ground up with AI at their core, representing a fundamental threat to traditional business models. Gabriel mentions OffDeal, founded by former investment banker Adie, which aims to create "the next Goldman Sachs" using technology to operate with fewer human bankers.

This represents a dual-front competitive challenge: incumbent firms learning to digitize their existing workforce while new entrants build AI-native operations without legacy constraints. The startup approach allows for optimization around AI capabilities rather than retrofitting technology onto existing human-centric processes.

This trend aligns with broader private equity and venture capital strategies of acquiring traditional cash flow businesses and technology-enabling them through rollup models, suggesting systematic transformation across professional services sectors.

Timestamp: [31:51-32:30]

• Legal firms prioritize proprietary AI development to protect competitive data advantages, while financial institutions focus on partnerships that leverage their relationship and advisory strengths
• Professional services AI adoption follows partnership models rather than pure build-versus-buy decisions, creating joint value propositions for clients
• Finance professionals lag behind legal counterparts in recognizing AI's automation potential, partly due to lack of visible workforce displacement examples across major corporations
• First-mover advantages in professional services AI adoption can deliver significant margin improvements and competitive positioning before market saturation
• Competitive pressure emerges from both incumbent digitization efforts and AI-native startups building professional services from the ground up
• Broader market trends show systematic technology enablement of traditional businesses through venture capital and private equity rollup strategies

Timestamp: [28:40-32:30]

People:

• Winston - Founder of Harvey AI, mentioned for partnership strategy with law firms
• Adie - Former investment banker who founded OffDeal to build AI-native financial services

Companies/Products:

• Harvey AI - Legal AI company pursuing partnership models with law firms
• OffDeal - AI-native financial services startup founded by former investment banker
• Goldman Sachs - Referenced as traditional investment bank model for AI-native disruption
• OpenAI, Anthropic - AI companies that law firms resist sharing proprietary data with
• Apple, Disney, Microsoft - Major corporations cited as lacking visible AI-driven job replacement examples

Industry/Business Concepts:

• Big Law - Large law firms building proprietary AI tools with contract data
• Professional Services Organizations - Broader category including both legal and financial firms
• Cash Flow Private Equity Businesses - Traditional businesses being acquired and technology-enabled
• Rollup Models - Investment strategy of acquiring and consolidating businesses with technology enhancement

Timestamp: [28:40-32:30]

Gabriel deliberately avoids expanding Rogo into corporate Financial Planning & Analysis (FPNA) departments despite frequent requests, viewing these workflows as less intellectually stimulating than investment-focused work. He characterizes FPNA as primarily accounting-oriented rather than strategic thinking about investment opportunities, business models, and capital allocation.

Instead, Rogo focuses on corporate development teams that think like investors and bankers about strategic acquisitions, market positioning, and capital allocation decisions. Gabriel uses Disney's Bob Iger as an example, distinguishing between his corporate development team (strategic thinking) versus FPNA departments (operational accounting).

This strategic focus reflects a preference for high-value, intellectually complex work that aligns with Rogo's core competencies in investment analysis and strategic advisory functions.

Timestamp: [32:37-33:36]

Gabriel models Rogo's strategy after Bloomberg's focused approach to financial markets rather than pursuing broad horizontal expansion. Bloomberg never attempted to sell beyond finance professionals, instead continuing to innovate for a single buyer type with nearly unlimited ability to pay for productivity improvements.

The key insight centers on unconstrained willingness to pay for value creation. Hedge fund investors will pay any amount for tools that make them 20% better investors because improved performance directly translates to unlimited upside potential. Corporate FPNA teams, while appreciating efficiency improvements, operate under budget constraints that cap their technology spending.

This economic reality drives Rogo's strategic focus on investors and bankers rather than pursuing broader market expansion that would dilute their positioning in the highest-value segment.

Timestamp: [33:36-34:28]

Consulting firms represent a logical expansion opportunity for Rogo, with McKinsey, Accenture, and similar organizations already adopting the platform. Interestingly, one of the "big three" consulting firms is implementing Rogo not just for front-office consultants but also for their internal research teams.

This adoption pattern surprises Gabriel, as he initially expected Rogo to replace research teams that support consultants rather than empowering those teams to better serve consultants. The reality suggests that even specialized research professionals benefit from AI augmentation rather than being displaced by it.

This pattern indicates that professional services work sits squarely within Rogo's target market, representing natural adjacency to their core investment banking and private equity focus.

Timestamp: [34:46-35:22]

Rogo faces significant challenges implementing product-led growth (PLG) strategies due to financial services' strict information security policies. Even if the platform offered simple web access, financial institutions' security frameworks would prevent employees from using external tools without formal approval processes.

Gabriel's investor Vince Hanks from Thrive has consistently pushed for PLG capabilities, but the regulatory environment makes traditional viral adoption impossible. Domains get blacklisted, shared links won't unfurl in enterprise Slack channels, and managing directors can't access URLs due to firewall restrictions.

While PLG might work on the buy-side (hedge funds and private equity) where professionals sometimes use personal devices before requesting enterprise accounts, the overall financial services market requires traditional enterprise sales approaches rather than bottoms-up adoption strategies.

Timestamp: [35:22-37:11]

• Strategic focus on high-value analytical work (investment thinking, corporate development) rather than operational functions (FPNA, accounting) aligns with customer willingness to pay unlimited amounts for performance improvements
• Bloomberg's model demonstrates the power of continuous innovation within a single, high-value market segment rather than horizontal expansion into constrained-budget sectors
• Consulting firms adopt AI tools to enhance rather than replace internal research capabilities, suggesting augmentation over displacement across professional services
• Financial services security requirements create fundamental barriers to product-led growth strategies, necessitating traditional enterprise sales approaches
• Investment-focused professionals operate with unconstrained technology budgets when tools directly improve performance, creating sustainable pricing power for specialized AI platforms
• Viral adoption mechanisms fail in regulated environments where domain blacklisting, firewall restrictions, and infosec policies prevent informal tool sharing

Timestamp: [32:37-37:11]

People:

• Bob Iger - Disney CEO used as example for strategic capital allocation thinking
• Vince Hanks - Investor at Thrive who advocates for product-led growth strategies

Companies/Products:

• Bloomberg - Financial data company used as strategic model for focused market approach
• Disney - Company referenced for corporate development versus FPNA distinction
• McKinsey, Accenture - Consulting firms mentioned as natural market extension
• Thrive - Investment firm with investor pushing PLG strategies
• Lightspeed - Mike's firm used as example for unconstrained willingness to pay for performance improvements
• Notion, Airtable - Companies referenced for traditional PLG sales motions
• ChatGPT - Referenced for potential buy-side PLG adoption on personal devices

Business/Technical Concepts:

• FPNA (Financial Planning & Analysis) - Corporate finance function Gabriel deliberately avoids
• Corporate Development (Corp Dev) - Strategic function that aligns with Rogo's target market
• PLG (Product-Led Growth) - Sales strategy challenged by financial services security requirements
• InfoSec (Information Security) - Enterprise security policies that prevent viral tool adoption
• TAM (Total Addressable Market) - Market size consideration for expansion decisions
• Big Three - Reference to major consulting firms (likely McKinsey, Bain, BCG)

Timestamp: [32:37-37:11]

Gabriel describes enterprise sales as a challenging learning experience for someone without a sales background. Coming from computer science education and investment banking, he had never encountered sales professionals or understood the complexity of enterprise selling processes.

Enterprise sales involves navigating approximately 40 stakeholders with varying interests, political dynamics, pain point identification, and careful message crafting. Unlike simple product demonstrations, enterprise selling requires understanding organizational hierarchies, decision-making processes, and stakeholder motivations.

The team overcame this challenge by hiring experienced sales professionals who understood the financial services market, significantly accelerating Gabriel's learning curve and the company's sales success. This highlights the importance of bringing domain expertise to complement technical capabilities in enterprise-focused startups.

Timestamp: [37:18-38:07]

Without product-led growth options, Rogo employs traditional top-down sales approaches, literally "banging on doors" to reach decision-makers. However, the company has developed some expansion capabilities, particularly on the buy-side where they can start with five-seat pilots and expand based on organic demand.

The pilot approach allows Rogo to demonstrate value with limited initial commitment, then return in three months with evidence of broader organizational interest. When 20 additional people request access after seeing colleagues use the platform, expanding to enterprise licenses becomes a natural conversation.

For traditional banks, the process remains more formal, requiring pilot programs, extensive stakeholder conversations, and detailed ROI demonstrations before any significant deployment decisions.

Timestamp: [38:07-38:50]

Gabriel envisions Rogo becoming the most effective analyst on Wall Street within 5-10 years, making the platform essential for competitive operation in investment banking and asset management. Firms without Rogo's capabilities will struggle to compete in time-sensitive transactions and sophisticated client presentations.

The competitive advantage will manifest in transaction speed and analytical sophistication. Firms lacking AI analyst capabilities won't submit competitive bids quickly enough or demonstrate the analytical depth required for complex deals like S-1 bake-offs and client pitches.

This vision positions Rogo not as a nice-to-have productivity tool but as fundamental infrastructure for competitive operation in modern financial markets.

Timestamp: [39:03-39:40]

Beyond serving elite financial institutions, Gabriel sees opportunity to democratize sophisticated financial services for smaller companies that currently lack access to high-quality M&A advice, fundraising processes, and strategic advisory services.

A $20 million HVAC business typically doesn't have access to professional sell-side processes, resulting in suboptimal pricing when selling to private equity-backed rollups. These companies can't justify Goldman Sachs' $5 million fees but still need sophisticated financial advisory services.

This democratization opportunity could significantly expand access to professional financial services while making private markets more efficient through better information and process quality.

Timestamp: [39:40-40:40]

Gabriel explores scenarios where AI enables companies to access financial services directly rather than through traditional intermediaries. For example, major corporations might use AI-powered tools to generate fairness opinions at a quarter of the traditional cost and double the quality, rather than paying investment banks $25 million for the same service.

However, this direct approach has limitations. While AI can handle analytical and documentation-heavy services, relationship-dependent activities still require human bankers. Transaction brokerage, negotiation, and trust-building rely on personal relationships, industry knowledge, and counterparty networks that remain valuable human capabilities.

This hybrid model suggests AI will handle analytical work while humans focus on relationship management and complex negotiations.

Timestamp: [40:40-42:03]

• Enterprise sales requires deep understanding of organizational politics, stakeholder management, and pain point identification - skills that technical founders must learn or hire
• Pilot programs with limited seats can create organic expansion opportunities even in security-constrained environments where traditional PLG doesn't work
• AI platforms in finance will become essential competitive infrastructure rather than optional productivity tools, fundamentally changing market dynamics
• Sophisticated financial services democratization represents significant market expansion opportunity for AI-powered platforms serving smaller companies
• Hybrid human-AI models will emerge where AI handles analytical work while humans manage relationships, negotiations, and trust-dependent activities
• Market transformation will create new direct-service opportunities while preserving human value in relationship-intensive aspects of financial services

Timestamp: [37:18-42:03]

Podcast Information:

• Thank you message encouraging listeners to rate, review, and subscribe to Generative Now
• Follow Lightspeed at @LightspeedVP on X, YouTube, and LinkedIn for more content
• Generative Now is produced by Lightspeed in partnership with Pod People
• Host: Michael Mignano, returning next week with new episodes

Social Media & Contact:

• Twitter/X: @LightspeedVP
• YouTube: Lightspeed channel
• LinkedIn: Lightspeed company page

Timestamp: [42:03-42:34]

People:

• Michael Mignano - Host and Lightspeed Partner
• Gabriel Stengel - Rogo founder and CEO being interviewed
• Bob Iger - Disney CEO used as example for potential AI-enabled financial services

Companies/Products:

• Rogo - AI platform for financial analysis and investment banking
• Goldman Sachs - Investment bank referenced for high-fee financial services
• Lazard - Investment bank mentioned for potential AI-powered fairness opinions
• Apple - Referenced as potential Disney acquirer in hypothetical scenario
• Chime - Digital banking service mentioned for contrast with high-finance services
• Robin Hood - Stock trading platform referenced as accessible financial service
• Lightspeed - Venture capital firm producing the podcast
• Pod People - Production partner for Generative Now podcast

Business/Financial Concepts:

• S-1 bake-off - Competitive process for initial public offering underwriting selection
• Fairness opinion - Investment banking service valuing transaction terms
• Sell-side process - Investment banking service for company sales
• Private equity-backed rollup - Acquisition strategy consolidating smaller companies
• HVAC business - Example of mid-market company needing financial services
• EV (Enterprise Value) - Company valuation metric

Timestamp: [37:18-42:34]