When I think about the next generation of engineers and product creators, one capability consistently separates the great from the good: technical taste. It’s the intuition to choose the simplest viable path, the humility to iterate, and the courage to ask “what if” before everyone else. In this piece, I share how I frame technical taste, what it means for AI strategy, and how to scale software teams in 2024 without losing speed or product-market fit.
Sam Schillace is the CVP and Deputy CTO at Microsoft. Before Microsoft, Sam held prominent engineering roles at Google and Box. He has also founded six startups, including Writely, which was acquired by Google and became Google Docs.
In this deep dive, I explore themes like “Sam’s advice for future engineers,” “What’s next for AI,” “How to develop technical taste,” “The importance of asking ‘what if’ questions,” “Lessons on market timing,” and “Scaling a software company in 2024.” My lens is product management leadership at scale, with a bias toward clear decision-making, rapid learning, and compounding leverage.
On market timing, my experience echoes the principle that momentum compounds only after you align product insight with the market’s inflection point. “The Innovator’s Dilemma” reminds us that the very systems designed to protect current value can block new value. The smartest move I’ve seen is to treat disruptive bets like venture portfolios inside the company—small, time-boxed, outcome-driven, and shielded from legacy KPIs. That’s how you preserve execution excellence while creating space for the next S-curve.
Technical taste is developable. I look for three signals: first, engineers who reduce a problem to its essence and deliver a working slice quickly; second, product creators who anchor on outcomes vs output OKRs; third, teams who habitually ask “what if” questions to surface non-obvious constraints and new leverage. When this mindset meets strong product discovery, you get faster cycles, fewer dead ends, and clearer product-market fit lessons.
“Building Google Docs” is a case study in choosing the web as the platform before it was fashionable—an act of taste under uncertainty. It’s also a reminder that what looks inevitable in hindsight was controversial in real time. Discussions about “The decline of Google apps” are less about any one company and more about the drift that occurs when focus fragments; taste is how you steer back to the core job-to-be-done.
On “The Innovator’s Dilemma facing Microsoft” and “The differences between Google and Microsoft,” I’ve seen how culture shapes product motion. One optimizes for experimentation at massive consumer scale; the other, for enterprise trust and durability. The playbook to reconcile both: define two operating modes—explore and exploit—and make the seams explicit. Use forward deployed engineers to learn with customers, while platform teams industrialize the wins.
“How to build a winning product” in 2024 is straightforward to say and hard to do: shorten the distance between insight and impact. I prioritize gen AI for product prototyping to test feasibility early, pair it with real-user loops from day one, and instrument everything to learn faster than competitors. Ruthlessly prune scope to ship a lovable slice, then iterate. That’s how you scale software in 2024 without bloating teams or code.
On “Becoming an optimist,” I’ve learned optimism is a practice: assume better solutions exist, then run experiments to find them. “What makes a great engineer” and “One thing the best engineers do” often collapse into the same behavior—holding high standards while moving fast. The best engineers I know ask precise “what if” questions, surface edge cases early, and translate ambiguity into a plan the team can execute.
“Sam’s prediction about AI,” “Capturing the value of AI,” and “How you should think about AI” all converge on a few product truths. Co-pilots and agents will become table stakes; differentiation will come from domain-specific data, workflow depth, and trust. Value accrues where AI is closest to the decision or outcome—embedded in the flow of work, not bolted on. For customer support AI strategy, the win isn’t a clever bot; it’s reducing time-to-resolution with explainability, guardrails, and continuous learning from real tickets.
“Microsoft’s new leverage,” “Scaling software in 2024,” and “The future of AI across several sectors” point to a broader shift: platforms that combine distribution, identity, and compliance will set the rules of engagement. But even in that world, local excellence matters—tight loops with customers, forward deployed engineers, and outcome-centric roadmaps will out-execute feature factories. The teams that treat gen AI as a capability—not a feature—will capture durable advantage.
Referenced:
Amazon: https://amazon.com
Box: https://www.box.com/
Elon Musk: https://twitter.com/elonmusk
Google Docs: https://docs.google.com
Itzhak Perlman: https://itzhakperlman.com/
Microsoft: https://www.microsoft.com
Netflix: https://www.netflix.com
Tesla: https://www.tesla.com/
The Innovator’s Dilemma: https://www.amazon.com.au/Innovators-Dilemma-Clayton-M-Christensen/dp/0062060244
TurboTax: https://turbotax.intuit.com/
Uber: https://www.uber.com/
Walmart: https://www.walmart.com/
Workday: https://www.workday.com/
Writely: https://techcrunch.com/2005/08/31/writely-process-words-with-your-browser/
Where to find Sam Schillace:
LinkedIn: https://www.linkedin.com/in/schillace/
Newsletter: https://sundaylettersfromsam.substack.com/
Twitter/X: https://twitter.com/sschillace
Timestamps:
(00:00) Introduction
(02:54) Lessons on market timing
(07:30) Developing technical taste
(09:51) Asking “what if” questions
(14:03) Building Google Docs
(19:32) The decline of Google apps
(20:57) The Innovator’s Dilemma facing Microsoft
(22:53) The differences between Google and Microsoft
(24:42) How to build a winning product
(27:46) Becoming an optimist
(29:12) Why engineering teams aren’t smaller
(32:00) Sam’s prediction about AI
(34:11) Capturing the value of AI
(37:43) How you should think about AI
(45:33) Advice for future engineers
(48:18) What makes a great engineer
(49:45) One thing the best engineers do
(51:37) Microsoft’s new leverage
(56:01) Scaling software in 2024
(59:50) The future of AI across several sectors
(64:28) What Sam and a violinist have in common
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