How a three‑year‑old internal note is showing up in the real world
A few years ago I read an internal email from the CEO of Google that felt less like a rallying cry and more like a calm, strategic bet. The message — paraphrased across news reports at the time — asked employees to focus on building excellent products, to treat user feedback as the engine of improvement, and reminded people that being first to market isn't the only path to lasting success (Business Insider summary of the memo).
Today, as we watch the AI arms race settle into something more structural than momentary hype, that internal note reads prophetic. Media coverage over the last months points to product launches, tighter integration of AI across search and productivity tools, and financial signals that suggest the patience and craft the memo urged are paying off (Times of India analysis).
What the email actually asked for (in practical terms)
- Embrace dogfooding and user feedback as continuous improvement loops.
- Prioritise product quality and trustworthiness over rapid showmanship.
- Lean on the company’s deep research and infrastructure advantages rather than just race to launch.
Those are small sentences, but they imply a cultural practice: iterate with real users, measure what matters, and internalise lessons from mistakes.
Why that approach is proving durable now
There are three reasons I keep coming back to when I think about why the memo’s instincts are being vindicated:
Deep technical foundations compound over time
Google’s investments in things like transformers, BERT, PaLM and infrastructure didn’t produce instant consumer lightning, but they created a platform where later product work compounds quickly. That matches the memo’s reminder that some of the most successful products weren’t first — they were right.
Feedback loops win in generative systems
Models improve rapidly when exposed to scale and diverse user feedback. The memo’s call to test internally and externally ("things will go wrong; feedback is critical") is exactly how you take a prototype and make it reliable enough for millions.
Full‑stack optimisation reduces long‑term costs
Building models is one thing; serving them affordably at Google scale is another. Reports of engineering work that reduces serving costs and integrates models into existing user flows show an attention to operational detail that turns flashy demos into sustainable services.
What founders and product leaders should take from this
I’ve spent years thinking and writing about how companies should react to technology shifts. A few short, practical lessons:
- Ship slowly, iterate fast: release early to a controlled audience, instrument tightly, and treat real‑user failures as data, not humiliation.
- Trust your assets: unique data, integration points, and engineering culture are defensible moats — use them.
- Invest in cost and latency: a better model that is prohibitively expensive to run isn’t a product.
- Keep the moral compass: when AI gives advice, the stakes (trust, safety, bias) matter. Building responsibly is both ethical and a competitive advantage.
I explored related themes earlier in my own writing — the need for companies to reinvent product metaphors around AI and to think of models as part of a service, not a one‑time feature (Time for Google to Re‑invent itself?). That continuity — from prediction to present reality — is what makes this moment interesting to me.
A cautious optimism
I write this with a mixture of admiration and caution. Admiration for teams that turned engineering depth into usable products; caution because the next phase of adoption will expose edge cases we haven’t yet thought through — privacy, agency, and what it means for humans to rely on machine counsel.
The memo’s most useful legacy may be cultural: a reminder that prudence, craft and attention to users are not slow‑motion virtues but competitive necessities in AI.
What I want to know from readers: where are you seeing the most convincing examples of this patient, iterative approach paying off — in consumer apps, enterprise products, or unexpected corners of the web? Reply and tell me what surprised you.
Regards,
Hemen Parekh
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