Speak
Keynotes, conference talks, podcasts and leadership offsites. Specific, candid and built from systems I run.
See talk topics →Engineering leader · Speaker · Workshop leader
I run engineering teams where AI agents do real work.
I'm Thomas Tucker, Senior Director of Engineering at G2 in Austin. I speak and teach about the part most AI talks skip: the delivery system around the agents, what it costs, how you measure it, and how you lead the people inside it.
0 → 92%
of engineers actively using AI, in seven months.
My team at G2, 2026
The record behind the talks
Results from teams I've run.
- Engineers actively using AI tools at G2, in seven months
- 0 → 92%
- Partner-sourced revenue in two years, while traffic fell 38%
- 3.1×
- Revenue per delivered lead
- +32%
- Bugs down, developer efficiency up, after replacing manual QA with AI-assisted, developer-owned testing
- −12% / +20%
25 → 13. Led a restructure across the US, Spain and India, and the separation through G2's acquisition, with delivery kept on track.
At G2 and Gartner Digital Markets. Ratios and percentages only; I keep company financials private.
Ways to work together
Bring this to your stage or your team.
Private workshop
Half-day and full-day, hands-on, on your repositories and your constraints. On-site anywhere in the world, or remote.
Bring it to your team →Public workshop
Small-group sessions in cities around the world. First sessions are forming; tell me where you are.
Register interest →What I talk about
Three questions leaders keep asking me.
How do you get a whole team using AI, not just the enthusiasts?
Every Engineer Is an Agent Manager
Read the talk →How do you keep humans in charge when agents do the building?
The Harness Is the Product: Running Delivery Where Agents Do the Work
Read the talk →How do you measure AI-era engineering without turning it into surveillance?
Judge the System by Its At-Bats
Read the talk →See the system
Don't take my word for it. Open the system.
A live engineering-effectiveness report: AI spend and forecast, adoption, harness practices, and the metric I trust most, shipped experiments with a hypothesis. It runs on a synthetic eight-team organization, so you can click through everything.
All teams, people and spend in the demo are synthetic.
How I lead
Care personally. Challenge directly. Let agents take the toil.
01
Judge the system by its at-bats
We don't judge engineers on whether a feature moved revenue; markets decide that. We judge whether the system gives the business enough at-bats: shipped experiments, each with a hypothesis, a metric and a result. Failed hypotheses are celebrated on the board, not hidden.
02
Every engineer is an agent manager
Directing agents is a skill: scoping work, giving context, reviewing output, knowing when to take the wheel. I coach it explicitly, the same way I coach code review or estimation.
03
Measure to remove friction, never to rank
Usage data is team-level by default and visible to the engineer first. It's for finding the failing test loop, the missing permission, the expensive default, and fixing it in the harness.
Writing
AI strategy, written down.
First essays are in draft.
I'm writing up what has worked, and what hasn't, from running AI-assisted delivery. Get a note when the first one is out.
About
I started as a front-end engineer, moved into leading teams in 2019, and now manage managers across the US, Spain and India at G2. I speak from the operating seat, not the sidelines.
More about me →Planning an event or a team offsite?
Tell me about the audience and the date. I reply within two business days.