Neil Hoyne, Chief Strategist at Google, recently shared something that caught my attention: "This May, the doors are opening in Mountain View once again. Two days of working with the people who built the Gemini to accelerate your startup's growth and help shape the future of AI." The part that stuck with me was not the hype around AI. It was the promise of getting in a room with the builders and doing real work.

If you are building an AI product right now, you know the gap between reading docs and shipping something reliable in production is where most teams get stuck. Neil is essentially pointing to a shortcut through that gap: structured time with the engineers closest to the model and platform decisions, plus a small peer group of founders dealing with the same tradeoffs.

"You'll sit down 1:1 with Google's AI engineers to get under the hood of your tech and solve the actual problems you're facing."

Below is my take on why this kind of forum matters, what you can realistically expect to get out of it, and how to prepare so those two days in Mountain View turn into months of momentum.

The core idea Neil Hoyne is really pushing

Neil did not frame this as a conference. He framed it as a working session. That distinction is everything.

A lot of AI events optimize for breadth: keynotes, trend decks, vendor booths, and networking that is fun but fuzzy. Neil is describing something narrower and more valuable for builders:

1:1 time with engineers who can diagnose what is happening inside your stack
A practical roadmap that is grounded in your product and constraints
A founder circle where the conversations are specific, not performative
A feedback loop back to the teams building Gemini and the surrounding tooling

That is a strong combination because it connects four things startups often separate: product, infrastructure, go-to-market, and platform direction.

What "under the hood" help looks like in practice

When Neil says you will get "under the hood," I read that as hands-on triage across the parts of AI systems that most teams struggle to debug quickly:

Architecture and model choice

Founders commonly ask: Do we need a larger model? Should we fine-tune? Should we use retrieval? The right answer is rarely "bigger." It is usually "more targeted." 1:1 time with experienced engineers can help you map your use case to the simplest approach that meets quality, latency, and cost requirements.

Evaluation and quality

Many teams are still guessing at quality. A practical evaluation setup (golden datasets, adversarial tests, regression checks, human review loops) can turn "it seems better" into "we can ship confidently." If I were attending, I would want to leave with an evaluation plan I can run weekly.

Latency, cost, and reliability

Production AI is a three-body problem: response quality, response time, and unit economics. Small changes like caching, prompt routing, structured outputs, or batching can meaningfully change margins. A forum like this can help you identify the few optimizations that actually move the needle.

Security, privacy, and compliance

As soon as you sell into regulated industries, the questions come fast: data retention, PII handling, auditability, and model behavior guarantees. Having direct access to platform experts can speed up decisions that otherwise take weeks of back-and-forth.

The roadmap point matters more than it sounds

Neil also wrote that the forum will help you "build a practical, no-nonsense roadmap for your AI growth that works in the real world - instead of just a pitch deck." That line is quietly ambitious.

Most AI roadmaps fail for one of two reasons:

They are too research-driven (lots of experiments, few customer outcomes).
They are too sales-driven (big promises, thin technical plan).

A useful roadmap ties model capability to product milestones and business constraints. It answers questions like:

What is the next customer outcome we can reliably deliver with AI?
What will we measure to prove it works (quality, time saved, revenue impact)?
What is the cheapest, simplest technical approach that can hit the bar?
What do we postpone until we have evidence we need it (fine-tuning, agents, multimodal pipelines)?

If you leave Mountain View with a roadmap that has owners, metrics, and a realistic sequence of work, that is worth more than any generic "AI strategy" slide.

The founder circle is not a side benefit

Neil mentioned "a small, global circle of founders" facing the same late nights and hard AI choices. In my experience, this is where a lot of the lasting value comes from.

When you are building with fast-moving AI tooling, your problems can feel uniquely messy. Talking to peers can normalize the reality:

Everyone is negotiating build vs buy.
Everyone is rewriting prompts and pipelines more often than they expected.
Everyone is balancing delight (wow moments) against trust (predictable behavior).

A small group also makes it easier to share specifics: evaluation tactics, pricing experiments, onboarding flows, and the operational burden of supporting AI features. Those are the conversations that change your next sprint.

Shaping the tools: why the feedback loop matters

Neil highlighted that founders will have a chance to tell the teams building these tools what they need on the roadmap and what should be done differently.

That is important because platform roadmaps get shaped by what users repeatedly ask for, especially when the requests come with clear evidence. If you want to influence the direction of developer tooling, bring:

A concrete use case and where it breaks today
Logs or examples that illustrate failure modes (sanitized if needed)
The business impact of the gap (time, cost, churn, risk)
A clear request phrased as a capability (not just a complaint)

The best platform feedback is specific, reproducible, and tied to outcomes.

Why in-person time still beats "just read the docs"

Neil called out something that many technical founders feel but rarely say out loud: it is a "rare chance to step out past reading articles and technical documentation, and get together with Google engineers to get some real work done." Exactly.

Documentation is necessary, but it is not sufficient when:

Your system has multiple moving parts (data, orchestration, LLMs, UI, guardrails).
Your failures are contextual (edge cases, domain language, user behavior).
The best practices are still emerging.

In-person working time compresses the feedback cycle. Instead of spending a week guessing, you can spend an hour testing assumptions with someone who has seen similar patterns across many teams.

If you apply, prepare like you are going into a design review

Neil shared the specifics: the Google for Startups Gemini Forum (second edition) is planned for May 2026 in Mountain View, CA (specific date TBA), with applications due by February 6, 2026.

If you want to maximize your odds of being selected and maximize the value if you attend, I would prepare four things:

1) A crisp problem statement

Not "we are building an AI platform." Instead: who the user is, what job they are trying to do, and what is failing today.

2) A minimal architecture overview

One diagram is enough: data sources, retrieval (if any), model calls, orchestration, and where you store outputs. The goal is to make it easy for engineers to orient quickly.

3) Evidence, not opinions

Bring numbers: latency, cost per task, success rate, top 10 failure categories. Even rough metrics beat vibes.

4) Your top 3 decisions

For example:

Should we fine-tune or stick with retrieval plus prompting?
How do we evaluate agent behavior beyond single-turn tests?
How do we control costs as usage scales?

The more decision-focused you are, the more actionable the two days become.

The bigger takeaway I am taking from Neil Hoyne

Neil is promoting an event, yes. But the deeper message is a builder mindset: stop treating AI as a slide, and treat it as a system.

Build with the people who built the tools, pressure-test your assumptions, and leave with a roadmap you can execute.

If you are a founder trying to turn AI into a durable advantage, that is the work. And it is why a hands-on forum can be more valuable than months of passive learning.

If this sounds like the kind of environment where your team would thrive, you can apply here: https://lnkd.in/dC9v-H8h

This blog post expands on a viral LinkedIn post by Neil Hoyne, Chief Strategist at Google. View the original LinkedIn post →