SPOTLIGHT: The AI category GTM teams are about to converge on | Adam Liska, CEO & Co-Founder @ airspeed
Adam Liska, CEO of Airspeed, discusses how his AI platform helps revenue teams execute on customer intelligence from sales conversations. He shares the journey from founder-led sales to building a scalable organization, emphasizing the importance of data capture and playbook systematization as foundations for scaling.
Summary
Adam Liska, co-founder and CEO of Airspeed (formerly Glyphic), joins the podcast after closing a $20M Series A to discuss the evolution of his company and the GTM category it operates in. Liska and his co-founder left DeepMind/Google four years ago to build a solution addressing a fundamental go-to-market execution problem: salespeople make commitments (like "I'll follow up") but fail to act on them, cascading into pipeline visibility issues and organizational forecasting failures.
Liska positions Airspeed in the emerging "revenue execution" category rather than traditional revenue intelligence or orchestration. The platform captures and organizes customer intelligence from sales conversations and helps teams act on that intelligence. He emphasizes that the solution isn't headless middleware—it's a purposeful interface where teams log in daily to do work, similar to how they use their CRM. The platform handles conversation recording, data structuring, and acts as an agentic interface that coordinates execution across the tech stack while maintaining a front-end experience tailored to different user roles (reps, managers, CROs).
Regarding the transition from founder-led sales to building a scalable organization, Liska outlines a specific sequencing of priorities. First, companies must capture comprehensive data from products, customer conversations, and sales interactions—treating this as foundational. Second, they should structure this data and reason about it to build playbooks that codify how the business operates. Liska describes playbooks not as static PDFs but as hierarchical, continuously updated systems that encode tribal knowledge in a format accessible to new hires and AI agents. He notes that Airspeed recorded everything internally and externally from day one, creating a massive dataset that enabled them to understand winning patterns, pain points, and use cases.
On data architecture, Liska explains that Airspeed currently pulls from multiple systems (HubSpot, Airspeed itself, Alguna) via APIs into a central interface for analysis rather than maintaining a single data warehouse, but sees eventual consolidation into owned infrastructure as best practice. He argues that organizations should own or have direct access to their data rather than leaving architecture design to third-party vendors.
Liska credits the acceleration of his Series A to a market shift in late 2024 and throughout 2025, where buyers finally understood where AI was going and felt urgency to invest. He notes his pitch remained consistent for four years, but execution timing and market readiness became the differentiators. He emphasizes the importance of founder-led sales activities—attending conferences, networking, building relationships—even as the company scales.
On organizational change in the AI era, Liska references influences including Mustafa Suleiman's "modern Turing test" (can an AI system run a business profitably?), Tom Blomfield's concept of AI-native organizations, and Satya Nadella's thinking on context sharing in AI-enabled companies. He concludes that the future involves encoding organizational knowledge in accessible, continuously updated formats that both humans and AI agents can leverage.
About this episode
<p>A rep says "follow up on that" on a call, then buries it under five more calls and a late-night inbox. Three days later the deal's gone cold, but the CRM still says it's live. The manager forecasts off that. <br /> <br /> The CRO forecasts off the manager. The board asks the CEO if anyone actually has a grip on the number. The whole thing was built on a promise nobody kept. </p> <p>Adam Liska worked on the early Gemini models at Google DeepMind before leaving to close that gap. His company, airspeed, just raised a $20M Series A on a bet that "revenue execution" is the next real category, not another tool that logs calls and calls it intelligence. </p> <p>Sam Jacobs traces the full arc with him: DeepMind to founder-led sales to the unglamorous work of turning a founder's instincts into a system a team can actually run. </p> <p>What we get into: <br /> Why "follow up on that" breaks the forecast chain from rep to CRO to board What "revenue execution" means, and why it isn't revenue intelligence with a new label <br /> Whether you own the interface or hand it to a chat model and live as middleware <br /> The Nashville lunch that became airspeed's first six-figure deal <br /> The real sequence from founder-led to scalable: capture the data first, then build the playbooks <br /> Owning your data instead of renting Salesforce's architecture <br /> The modern Turing test, AI-native orgs, and where the back office is headed</p> <p>Chapters: </p> <p>00:00 Intro<br /> 00:44 From DeepMind to airspeed, and the $20M Series A <br /> 01:30 "Follow up on that" — the execution gap that breaks forecasts <br /> 04:00 What category is this? Revenue execution, defined <br /> 05:40 Headless vs. owning the interface <br /> 08:00 The origin story: leaving DeepMind in 2022 <br /> 11:39 Founder-led sales and the Nashville lunch that closed a six-figure deal <br /> 13:18 The hard part: turning founder-led into a repeatable system <br /> 14:14 Why they recorded everything from day one <br /> 16:35 Owning your data, and where the source of truth lives <br /> 19:03 The playbook: data first, then hierarchical playbooks <br /> 20:52 Influences: the modern Turing test and AI-native orgs <br /> 23:37 Where to find airspeed <br /> Try airspeed: goairspeed.com</p>
Key Insights
- Liska argues that the core GTM execution problem isn't data logging or simple automation, but the gap between capturing insights and actually acting on them—salespeople make commitments like 'I'll follow up' then forget, causing cascade failures in pipeline visibility and organizational forecasting
- Liska contends that revenue execution (not headless middleware) requires maintaining a daily-use interface where teams log in to do work, with the UI adapted to different roles, rather than deferring interface ownership to LLM providers
- Liska's sequencing for scaling from founder-led sales prioritizes comprehensive data capture first, then structuring that data into continuously-updated hierarchical playbooks that codify how the business wins, rather than hiring sales leadership before understanding these patterns
- Liska observes that his pitch for AI-driven execution remained constant over four years, but market conditions changed dramatically in late 2024-2025 when buyers shifted from intellectual agreement to genuine urgency to invest in AI solutions
- Liska references the 'modern Turing test' concept—that true AI capability is demonstrated by letting an AI system run a business with real resources and measure profitability—as a framework for thinking about how AI will eventually restructure organizational operations
Topics
Transcript
. Hey everybody, it's Sam Jacobs. Welcome to Topline Spotlight. We are back with another episode. If you don't remember, Topline Spotlight is a segment where we go deep on a tactical issue that a real operator out there in the real world struggled with. Our goal is to have authentic conversations with leaders, founders, CEOs, executives, investors, in go-to-market and figure out how they solve specific challenges over the last 12 to 24 months and what we can learn from that as the common layman. Now, today on the show, we've got Adam Liska. He's the founder, co-founder, and CEO of Airspeed, formerly known as Glyphic. Airspeed's an AI platform built to help revenue teams capture, organize, and act…
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