“She was chief AI scientist at Google Cloud”
Google Cloud
By Google
2 recommend/use · 6 sourced episodes
Every sourced reference
Short attributed excerpts only. Timestamps are approximate.
“we all built stuff on this new Google Cloud platform”
“Google reached out to me about a a role on their team on Google Cloud for doing SEO”
“both Google as in Google cloud and anthropic were able to meet the Privacy requirements we had”
“constrained by where AWS or Google cloud or Azure have built out”
“this is offered by ro cloud and essentially it allows you to train high quality custom machine learning models”
Related frameworks
Breadth-Then-Depth Platform Strategy
Go wide to discover your niche, then put all your wood behind the few arrows that lift the rest
Celebrate Adoption, Not Shipping
Replace hours and feature counts with customer-adoption outcomes teams can actually steer toward
Draft-First LLM Augmentation
Never ask the model to do your job — write your version first, then have it improve it.
Energy-Aligned Writing
Write only what energises you, only what overlaps your job, and publish nearly all of it.
Fake the AI Before You Build It
Never train a model for an MVP — prototype the AI's output and test demand first.
Handbook-as-Code
Treat how the company operates as a versioned repo anyone can submit a merge request against
Imperfect Metrics as Education
Report an imperfect engineering metric on purpose, then use every question about it to educate upward.
Mission-Over-Minutiae Opportunity Choice
Choose your next big move by passion, mission, and team — not by optimizing every minor variable.
Northstar Problem Commitment
Pick one foundational building-block problem and commit years to it, not a scatter of trendy ones.
Objective-Data Alignment Test
Before betting on an AI capability, check that your training data is the same shape as your desired output.
Physical-System Reality Check
For anything embodied, budget for three things — a brain, a body, and real application scenarios.
Product-Led SEO
Treat SEO as a product you build for the search user, not content you optimize for a keyword.
Programmatic vs Editorial Decision Rule
Go programmatic only where scale meets a real use case — otherwise you scale nothing.
Right Model for the Right Use Case
Route each AI feature to a model chosen for that job, not one popular LLM stretched over everything
SEO Conversion-Metric Rigor
Rankings and traffic are not results. Hold SEO to the same efficiency standard as paid.
Short Toes
Engineer feedback so it lands on the work, never the person — the async collaboration protocol
Stocks-and-Flows Pipeline Diagnosis
Model any org process as stocks and flows, then find where reality disagrees with the model.
The 11 Zero-Day Marketing Exploits
Eleven recurring meta-patterns for engineering an unfair go-to-market advantage.
The Adjacent-Precedent De-Risk Pitch
Win leadership buy-in for a big AI bet by anchoring it to a past bet that already worked.
The AI PM Upskilling Path
Learn the fundamentals, shadow a research scientist an hour a week, and build one model end to end.
The Attributed Influencer Launch Sequence
Stagger tracked influencer shares over days, then weaponize the leaderboard.
The Boring Engineering Strategy
Write the strategy down, get the diagnosis honest, then constrain options so energy goes to what matters.
The Deep Dive PM Interview
Make candidates write real async requirements and defend them — a two-way test for remote PM fit
The Four Challenges of AI Product Management
Uncertainty, pivots, data scarcity and a broken promo path — the four taxes of the AI PM role.
The Funnel-Position Defense Against AI Overviews
AI answers eat top-of-funnel search. Move your SEO to where intent and conversion live.
The Golden Recipe of Modern AI
Every AI breakthrough sits on three legs: big data, the right neural architecture, and enough compute.
The Honest-Applicable-Reversible Values Test
Three tests that separate values which drive decisions from values that just describe your self-image.
The 'Just Evil Enough' Ethics Test
An Occam's-razor line between subversive-and-clever and actually evil.
The Model Launch Bar
With probabilistic products, the PM — not the scientist — decides what accuracy is good enough to ship.
The Recon Canvas
An 18-square grid for scanning product, medium, and market for subversive openings.
The Remote-First Operating Stack
Five mechanisms that make an all-remote company across 60+ countries actually work
The SEO Go/No-Go Investment Test
SEO is not free. Price it fully, then compare it against every other growth channel.
The Shared-Fate EM/PM Pair
Give the eng manager and PM the same performance rating, and make them understand before they argue.
The Shiny Object Trap (Problem-First AI)
A regular PM ships the right product; an AI PM solves the right problem.
The Subversive Mindset (System Awareness → Novelty → Disagreeability)
Get a system to behave in a way its creators didn't intend, in three learnable steps.
The Transparency Ramp
Default everything to public except three named exceptions, then ramp until it feels uncomfortable
Top-Down TAM SEO Forecast
Forecast SEO from market size down, not from keyword-tool volumes up.
Treat Your Course Like a Product
Hypothesise the audience, interview them, iterate the ICP, and run three weeks — not one.
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