Dispatches from All In: The Two Sides of AI Adoption

The tension was unmistakable on the Forum Stage at ALL IN on Thursday afternoon. One keynote was all about how to build frontier AI responsibly at a massive scale. The next was about what to do when billions of autonomous agents start making decisions on behalf of consumers. Together, they painted a picture of where AI is heading, and the urgency of getting adoption right.

Building Trust at Billion-User Scale

Tulsee Doshi from Google DeepMind spoke candidly about what it actually takes to deploy advanced AI across products used by 2.5 billion people. The headline: it’s not about moving fast, it’s about moving intentionally.

Google’s approach reveals a core philosophy—start small, expand carefully. They treat safety and testing as features, not afterthoughts. They phased AI Overviews in search gradually. They invested heavily in the “hardest customer” internally (search) because getting it right there mattered most. The message was clear: at this scale, trust isn’t negotiable.

What stood out was how practical this looks in practice. Gemini isn’t one model—it’s a portfolio designed for different use cases. Nano and Flash for budget-conscious builders. Cutting-edge capabilities for complex tasks. And critically, they’re sharing models with the industry 30 days before launch, building transparency into the development cycle from day one.

The Agent Revolution: When Machines Buy Things

Then came the plot twist. While Google is carefully stewarding how AI helps users think, the next panel tackled a bigger question: what happens when AI starts acting on behalf of users?

Agentic commerce is no longer theoretical. Mastercard, Loblaw, and Coveo are actively preparing for a future where machines compare prices, negotiate purchases, and manage subscriptions without human intervention for every decision. Loyalty programs designed for human behavior patterns? Marketing strategies built on human psychology? These break when the buyer is an algorithm.

The Adoption Question

Here’s what connected these two conversations: both are wrestling with the same challenge: how do you get people to trust and adopt AI at scale?

Google’s answer is: build it responsibly from the ground up, be transparent about how it works, test rigorously, and meet people where they are. Don’t force AI into products; enhance the products people already use.

The commerce panelists had a different but complementary insight: adoption won’t come from forcing new behaviors. It’ll come from AI solving real problems—finding the best deal, saving time, enabling better decisions. But brands have to rethink what “customer experience” means when the customer is sometimes a machine.

The Takeaway

Day 2 at ALL IN made one thing clear: the next phase of AI isn’t about the models themselves. It’s about trust, transparency, and meeting users on their terms. Whether you’re Google deploying Gemini to billions or a retailer preparing for agent-driven commerce, the playbook is the same: go slow enough to build trust, but fast enough to deliver real value.

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I work with Founders and Startup Executives to market your product or service to grow and reach your next milestone or raise. I unlock new strategies and tactics to drive growth that are rooted in my years of hard-won lessons: leading marketing at AI product companies selling to enterprises in construction, manufacturing, and other regulated industries. I’ve learned how to identify the barriers to adoption, how to translate domain expertise into compelling product positioning, how to speak credibly to CFOs and CEOs, and how to spot and prioritize the customers ready to champion AI transformation.

If you’re an AI company selling to enterprise buyers and you need someone who speaks both the language of your customers and the language of product and go-to-market strategy, that’s exactly the problem I solve. I understand the constraints, the governance requirements, the ROI questions, and I understand how to position and market to customers ready to move.

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