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🎯 How is AI Different Than Other Technology Waves? The Compounding Effect That's Changing Everything

We're witnessing something unprecedented in technology adoption. ChatGPT reached 100 million users in just two months—faster than any consumer product in history. But according to Brett Taylor (OpenAI Chairman, former Salesforce co-CEO) and Clay Bavor (former Google Labs head), this isn't just about speed. It's about compounding.

In another great conversation on Acquired, these two legendary product leaders—who between them helped create Google Maps, led Facebook's platform strategy, and built everything from Gmail to Google's AR/VR initiatives—reveal why AI represents a fundamentally different type of technology wave. One that's riding on the coattails of every previous innovation.

Their company Sierra, which builds customer-facing AI agents, is growing faster than any B2B software company they've ever seen. But the real story isn't just about their success—it's about how AI is leveraging decades of infrastructure investment to create adoption curves that look almost vertical.

Oh and finally, I’ve included only the intro below moving the full piece to a separate article to try to shorten the copy in the newsletter itself.

Let me know what you think on the content and new style!

🎥 Watch the full episode here

📆 Published: Aug 18th 2025

🕒 Estimated Reading Time: 4 mins. Time saved: 60+ mins! 🔥

🏗️ The Compounding Technology Stack: Why AI Adoption Looks Vertical

Brett Taylor frames the current moment through an compelling lens: technology waves don't replace each other—they compound. Each new wave rides on the infrastructure built by previous ones, creating exponentially faster adoption curves.

"To put a PC on every desktop, you had to actually make a supply chain of PCs, lower the cost of chips," he explains. "We got to basically two billion PCs. Then the internet could ride on the coattails of the PC revolution. When smartphones came out, you had the internet already."

The numbers tell the story: The first website came online around 1991, but it wasn't until 2002—eleven years later—that 10% of the world used the web weekly. ChatGPT achieved the same milestone in just 25 months.

This isn't just about better technology. It's about infrastructure leverage. Where previous waves required building physical supply chains, distribution networks, and user education from scratch, AI companies can instantly tap into existing cloud computing resources, global internet connectivity, and billions of connected devices.

Key Takeaways:

• Technology waves compound rather than replace—each builds on previous infrastructure

• AI benefits from decades of internet, mobile, and cloud investment already in place

• Adoption curves are becoming exponentially steeper with each successive wave

🧠 From Scarce to Plentiful: The Intelligence Revolution

Clay Bavor offers a first-principles way to understand AI's transformative potential: "What are you making plentiful that was scarce before, and how does that impact society?"

He draws parallels to previous transformations. Energy went from scarce to plentiful—now you flip a light switch without thinking. Food distribution made nutrition abundant in developed countries, fundamentally changing how people spend their time and mental energy.

"Now we're going to a world where intelligence has gone from something scarce to something plentiful," Bavor explains. "Prior to modern farming, most people spent a lot of their time thinking about food. Now it's not a central part of their day-to-day planning."

The implications are profound. When intelligence becomes a commodity, entire categories of work that required human reasoning can be automated. But unlike previous automation waves that primarily affected manual labour, this one targets cognitive work—including the jobs of the people building the technology.

Key Takeaways:

• AI makes intelligence plentiful rather than scarce—a fundamental shift in human history

• Previous scarcity-to-abundance transitions (energy, food) completely reshaped society

• This wave uniquely affects cognitive work, including the builders of the technology itself

🏢 The Business Model Revolution: From SaaS to Outcome-Based Pricing

Perhaps the most radical aspect of Sierra's approach isn't their technology—it's their business model. Brett Taylor, who helped build the SaaS revolution at Salesforce, is now deliberately abandoning it.

"We started from first principles and asked: what are agents actually doing?" Taylor explains. "In contrast to software that might help you be marginally more productive, agents are actually getting the job done for you. You're hiring software to accomplish a task."

Sierra only charges when their AI agents successfully complete tasks without human intervention. If an ADT customer's alarm panel starts beeping, Sierra's agent can diagnose the issue, identify which of 52 different panels they have, and mail a replacement battery—all autonomously. They only get paid when the problem is fully resolved.

This isn't just pricing innovation—it's a fundamental shift in risk allocation. Traditional software vendors sell tools; Sierra sells outcomes. The implications ripple through everything from procurement processes to post-sales relationships.

Key Takeaways:

• Outcome-based pricing aligns vendor incentives with customer success completely

• AI agents "do the job" rather than just "help with the job"—requiring new business models

• Risk shifts from customer to vendor, creating true partnership dynamics

🔄 The Platform vs. Best-of-Breed Pendulum

Taylor identifies a crucial pattern in enterprise software adoption that explains why AI companies are thriving despite incumbent advantages. The market oscillates between "best of platform" (buying everything from one vendor) and "best of breed" (choosing specialists for each function).

"When new technologies come out, this pendulum swings from best of platform towards best of breed," he observes. "Incumbents typically aren't that great at these new technologies. They have strategic impediments to embracing new business models."

Right now, the value of AI agents in displacing labour costs is so significant that companies will choose the highest quality solution regardless of vendor relationships. A single AI agent might save hundreds of thousands in operational expenses—making software costs almost irrelevant.

But this window won't last forever. "At some point, we'll be talking and it's like 'oh yeah, AI agents—I made 12 this weekend,'" Taylor predicts. "It will no longer be technically hard, and then you start to swing back towards platforms."

Key Takeaways:

• Technology disruption temporarily favours best-of-breed over platform solutions

• AI's labour cost savings make software costs nearly irrelevant in purchasing decisions

• The current quality advantage window for AI specialists won't last indefinitely

🚀 The Speed of Modern Company Building

The infrastructure compounding effect isn't just changing user adoption—it's transforming how quickly companies can scale. Sierra went from concept to major enterprise deployments in under 18 months, a timeline that would have been impossible in previous technology waves.

"For a company like Sierra, we're growing so quickly because the plumbing is already there," Taylor explains. "People already have phone numbers getting 100 million calls a year. The technology is available. We're riding on the coattails of all these amazing technology investments."

Clay Bavor highlights the immediate feedback loops this enables: "We can have a breakthrough in our agent architecture on Monday, implemented on Tuesday, deployed with hundreds of customers on Wednesday, and directly see the impact."

This speed creates both opportunities and challenges. Companies can reach massive scale faster than ever, but they must also execute flawlessly from day one. There's no time for gradual improvement when competitors can emerge and scale in months rather than years.

Key Takeaways:

• Existing infrastructure enables unprecedented company scaling speed

• Immediate deployment and feedback loops accelerate innovation cycles

• Speed advantages compound—early movers can establish dominant positions quickly

🎯 The Human Capital Paradox

Despite building technology that automates cognitive work, both founders emphasise that Sierra is hiring aggressively. This apparent contradiction reveals something important about the current AI landscape.

"Everything we do as a company is going to be the direct or indirect result of talented, smart, amazing people," Bavor notes. "We look for every point of leverage we can find with AI, but we're growing the company in terms of people and geographies as quickly as we possibly can."

Their approach to AI-augmented work is telling. Rather than just fixing incorrect code produced by AI coding assistants, they fix the context that led to the error. "If cursor produces incorrect code, our philosophy is: don't fix the code, fix the context that cursor had," Taylor explains.

This represents a fundamental shift in how work gets done. Instead of people doing tasks, people become orchestrators of AI systems—responsible for providing context, setting objectives, and ensuring quality outcomes.

Key Takeaways:

• AI companies are hiring aggressively despite automating cognitive work

• The role shifts from doing tasks to orchestrating AI systems effectively

• Success requires fixing AI context and processes, not just AI outputs

🔮 The Second and Third-Order Effects

Both founders acknowledge that predicting AI's broader impact is extraordinarily difficult. Taylor draws parallels to previous technology waves: "I don't think many of us predicted the second or third-order effects of mobile app stores or social networks correctly. This one's even harder to predict."

One area they're watching closely: agent-to-agent interactions. Sierra has an internal bet about when more than 50% of conversations with their customer service agents will be with customers' personal AI agents rather than humans.

The implications extend far beyond customer service. If personal AI agents become widespread, how does that change demand generation, advertising, and commerce? Will agents make mathematically optimal decisions, eliminating the psychological pricing strategies that drive much of modern marketing?

"I think we're at the cusp of something that will in five years give us a very different market on the internet," Taylor predicts. "The economy of the internet will be upended in significant ways."

Key Takeaways:

• Second and third-order effects of AI are harder to predict than previous technology waves

• Agent-to-agent interactions may become dominant in customer relationships

• The entire internet economy may be restructured around AI intermediation

🎯 Getting Started: What This Means for Product Teams

For product leaders watching this transformation, the message is clear: the window for AI-first thinking is now, but it won't last forever. Companies that learn to orchestrate AI systems effectively will have significant advantages, but those advantages will compress as the technology becomes commoditised.

The key is developing what Taylor and Bavor call "AI-first" thinking—not just using AI tools, but redesigning processes around AI capabilities. This means providing better context to AI systems, measuring outcomes rather than outputs, and building feedback loops that improve AI performance over time.

Most importantly, it means embracing the discomfort of transition. As Bavor notes, "This period of transition where how I've come to identify my own worth has been disrupted—that's very uncomfortable. But we'll come out the other side as a higher leverage species."

Key Takeaways:

• The window for AI-first competitive advantages is open but temporary

• Success requires redesigning processes around AI capabilities, not just adding AI tools

• Embrace the discomfort of transition—it's temporary but necessary for long-term success

The compounding effect of technology waves means we're not just witnessing faster adoption—we're seeing the emergence of entirely new business models, work patterns, and economic structures. The companies that understand this compounding effect and position themselves accordingly won't just survive the transition—they'll define what comes next.

🎥 Watch the full episode here

Sierra ( https://sierra.ai/ )