Episode
22

Tomasz Tunguz on Why a Trillion Dollars of Market Cap Is Up for Grabs (and How AI Teams Will Win It)

Tomasz Tunguz (Theory Ventures) joins High Signal to unpack why a trillion dollars of market cap is up for grabs as AI reshapes enterprise software. He explains why workflows are now changing faster than packaged software can keep up, how “liquid software” is redefining CRM and marketing automation, and why background agents will require a new kind of “agent inbox.” We discuss the compounding errors that arise when tools are chained too finely, the hidden AI technical debt accumulating in today’s systems, and why modular stacks—mixing local and cloud models—will beat monolithic apps. The conversation also surfaces early memory architectures, what breaks when one IC manages 100 agents, and how these shifts change the real bottlenecks in scaling AI.
August 19, 2025
Listen on
spotify logoApple podcast logo
Guest
Tomasz Tunguz

Theory Ventures

,

Tomasz Tunguz currently serves as the general partner at Theory Ventures. He is the Board Member at Doss. Previously he worked as a managing director at Redpoint Ventures. Prior to joining Redpoint, Tomasz was the product manager for Google's social media monetization team, including the Google-MySpace partnership. In addition, he managed the launches of AdSense into six new markets in Europe and Asia. Before Google, Tomasz developed systems for the Department of Homeland Security at Appian Corporation. Tomasz also co-founded Perquimans Systems, a provider of bilingual, tri-currency automated time billing and document management systems for top-tier law firms in Chile.

‍

Guest

,
HOST
Hugo Bowne-Anderson

Delphina

Hugo Bowne-Anderson is an independent data and AI consultant with extensive experience in the tech industry. He is the host of the industry podcast Vanishing Gradients, a podcast exploring developments in data science and AI. Previously, Hugo served as Head of Developer Relations at Outerbounds and held roles at Coiled and DataCamp, where his work in data science education reached over 3 million learners. He has taught at Yale University, Cold Spring Harbor Laboratory, and conferences like SciPy and PyCon, and is a passionate advocate for democratizing data skills and open-source tools.

‍

Key Quotes

Key Takeaways

Market cap is liquid.
AI enables reinvention of entrenched workflows like CRM and marketing automation. Much of the enterprise software value created since 1999 is up for grabs.

‍

Workflows change faster than software.
Teams are rebuilding processes weekly, making fixed, prepackaged workflow software less useful. Agility now beats incumbency.

‍

Background agents need a front door.
Agents will run quietly in parallel, surfacing only exceptions. A new “agent inbox” will be required for humans to manage them effectively.

‍

Error compounds across steps.
Breaking tasks into too many tools leads to cascading mistakes. Sometimes steps must be packaged together to reduce failure.

‍

AI technical debt hides in the stack.
Improvised tools, unclear abstractions, and weak testing accumulate hidden fragility. Teams move fast, but without design patterns the debt piles up quickly.

‍

Modular beats monolithic.
Hybrid stacks that mix local small models with cloud-scale ones will win on cost, latency, and privacy—while allowing layers to be swapped over time.

‍

Memory is still primitive.
Hot, warm, and cold memory tiers are emerging, but managing institutional vs. local memory remains an open challenge.

‍

‍Agent management is the new bottleneck.

‍A future productivity metric: how many agents a single IC can manage in parallel without overwhelming review, CICD, or merge processes.

‍

You can read the full transcript here.

‍

Timestamps

‍

00:00 Introduction to AI's Impact on Software Workflows

01:12 Generative AI and Market Cap Disruption

01:52 Reinventing Workflows with AI

03:27 Balancing Excitement and Practicality in AI

05:44 Building and Experimenting with AI Tools

08:22 Implementing AI Workflows in Investment Operations

09:55 The Future of Marketing with AI

12:33 Ephemeral Software and Liquid Software

15:49 Small Teams vs. Large Organizations in AI Adoption

18:08 Career Advice for the AI-Driven Future

23:02 Automating CRM with AI

25:01 Challenges in Agent Systems

25:27 Tool Selection and Programming Paradigms

28:47 Memory and AI Systems

31:07 Modular AI Models

34:59 Scaling Agent Use and Infrastructure

38:47 Technical Debt in AI

43:24 The Future of Software Development

45:02 Hype vs. Reality in AI

46:19 Conclusion and Closing Remarks

Links From The Show

Transcript

featured

In the spotlight: Our most popular episodes

Episode
22

Tomasz Tunguz on Why a Trillion Dollars of Market Cap Is Up for Grabs (and How AI Teams Will Win It)

Tomasz Tunguz (Theory Ventures) joins High Signal to unpack why a trillion dollars of market cap is up for grabs as AI reshapes enterprise software. He explains why workflows are now changing faster than packaged software can keep up, how “liquid software” is redefining CRM and marketing automation, and why background agents will require a new kind of “agent inbox.” We discuss the compounding errors that arise when tools are chained too finely, the hidden AI technical debt accumulating in today’s systems, and why modular stacks—mixing local and cloud models—will beat monolithic apps. The conversation also surfaces early memory architectures, what breaks when one IC manages 100 agents, and how these shifts change the real bottlenecks in scaling AI.

With
Tomasz Tunguz
Founder and VC
,
Theory Ventures
With
,
Play episode
Episode
15

Eoin O'Mahony on Why Good Metrics Still Lead to Bad Decisions — and How to Fix It

Eoin O'Mahony—data science partner at Lightspeed, former Uber science lead, and co-designer of the system that kept NYC’s Citi Bikes available across the city—argues that positive metrics are meaningless if you don’t understand the mechanism behind them. At Uber, he was careful to make sure his launches both looked good on paper and made sense in practice. Now in venture, he’s applying that same rigor to unstructured data—using GenAI to scale a kind of work that’s long resisted systematization.
With
Eoin O'Mahony
Partner, Data Science
,
Lightspeed Ventures
With
,
Episode
10

Ari Kaplan on Why AI Won't Save You But Data Intelligence Will

Ari Kaplan—Global Head of Evangelism at Databricks and a pioneer in sports analytics—explains why businesses fixated on AI often overlook the real advantage: making better decisions with their own data. He shares lessons from his work building analytics teams for Major League Baseball, advising McLaren’s F1 strategy, and helping companies apply AI where it actually works—without falling into hype-driven traps.
With
Ari Kaplan
Global Head of Evangelism
,
Databricks
With
,
most recent

Listen up: Our latest discussions

Hear the hottest takes on data science and AI.

See all episodes
Episode
22

Tomasz Tunguz on Why a Trillion Dollars of Market Cap Is Up for Grabs (and How AI Teams Will Win It)

Tomasz Tunguz (Theory Ventures) joins High Signal to unpack why a trillion dollars of market cap is up for grabs as AI reshapes enterprise software. He explains why workflows are now changing faster than packaged software can keep up, how “liquid software” is redefining CRM and marketing automation, and why background agents will require a new kind of “agent inbox.” We discuss the compounding errors that arise when tools are chained too finely, the hidden AI technical debt accumulating in today’s systems, and why modular stacks—mixing local and cloud models—will beat monolithic apps. The conversation also surfaces early memory architectures, what breaks when one IC manages 100 agents, and how these shifts change the real bottlenecks in scaling AI.
With
Tomasz Tunguz
Founder and VC
,
Theory Ventures
and
,
Episode
21

Why Great Data Still Leads to Bad Decisions (And How to Fix It) with Amy Edmondson and Michael Luca

Amy Edmondson (Harvard Business School) and Mike Luca (Johns Hopkins) join High Signal to unpack what actually drives good decisions in data‑rich organizations. Using contrasts like the Bay of Pigs vs. the Cuban Missile Crisis and product cases such as Airbnb’s work on measuring discrimination, they show how decision quality tracks conversation quality—framing options, surfacing uncertainty, and challenging assumptions. We cover common failure modes (correlation vs. causation, anchoring, hierarchy, false precision), practical meeting designs that raise the signal, and where algorithms and LLMs help or hinder human judgment.
With
Amy Edmondson
Novartis Professor of Leadership and Management
,
Harvard Business School
and
Michael Luca
Professor and Director, Technology and Society Initiative
,
Johns Hopkins University
Episode
20

Daragh Sibley on Incentives, Accountability, and the Data Leader’s Dilemma

Daragh Sibley, Chief Algorithms Officer at Literati and former data-science leader at Stitch Fix, joins High Signal to unpack how machine-learning moves from slide-deck promise to bottom-line impact. He walks through his shift from academic research on how kids learn to read to owning inventory and personalization algorithms that decide which five books land in every child’s box. We dig into the moment a data leader stops advising and starts owning P&L-critical calls, why some problems deserve simple analytics while others need high-dimensional models, and how to design workflows where human judgment and algorithmic predictions share accountability. Along the way we talk incentive design, balancing exploration and exploitation in inventory, and measuring success in dollars—not dashboards.
With
Daragh Sibley
Chief Algorithms Officer
,
Literati
and
,

Get the latest episodes in your inbox

Never miss an episode of High Signal by signing up for the Delphina newsletter.

By clicking Sign Up you're confirming that you agree with our Terms and Conditions.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.