If you’re on the technical side of the house, these articles can feel disconnected from my series on Harness, Loop, and Graph and Causal Workflows. However, business and value architecture are just as important as agentic architecture. My weekly office hours always include questions about finding customers, product-market fit, and even sales.
In a time of unprecedented opportunity, the definition of full-stack engineering now includes the business. No one who can substantively advance your career wants to talk to you if you don’t understand business architecture. Customers and clients don’t want to talk to you unless you understand value engineering. VCs won’t fund you until you can speak to both.
I see a lot of businesses that did multiple things right with information, AI, and agents, but revenue is still falling. They built the platform, shipped agents, and customer engagement is trending up and to the right. But when the CFO asks why none of it shows up in top-line growth, my phone rings, and it’s the start of a challenging conversation about business architecture.
I have some version of it with prospective clients multiple times every quarter, going back to long before the ChatGPT moment. I must explain the Orchestration Imperative and the Big Picture. AI, information, and agents (like data and ML before them) are extensions of the business and operating models in ways that prior technologies weren’t.
Every CEO says that technology is a core pillar of strategy, but when revenue doesn’t show up…is it really? If technology or agents or AI is a core pillar of strategy…where is it?
Most leadership teams can show me a business and operating model. When I ask, “Where is your Technology Model?” typically, I get a list of initiatives, a hiring plan, and the budget for technology investments. Those are tactics. What strategy informed those tactics?
AI strategy should help the business see the Big Picture, but most AI strategies are aspirational, not actionable.
AI strategy should help the business make 3 critical decisions that align technical maturity with opportunities, value creation, and business transformation. Most AI strategies provide cover to delay those decisions with a flurry of interesting-sounding activity.
They overfit to technology investment, which creates multiple problems that don’t become obvious for several quarters after the decisions that lead to them. Without the Big Picture and 3 Critical Decisions, that distance makes finding and addressing the root causes challenging.
How do opportunities move parts of the business and operating model into the technology model?
What new parts of the business and operating model is the business building for the first time in the technology model?
How does the business monetize those transformations?
How does it keep them aligned with core strategic goals, not technical goals?
These are core tenets of Top-Down Opportunity Discovery. An actionable AI strategy manages a lot of complex orchestration across the enterprise. Handwaving, slide decks, and playbooks have never been enough, but we are finally seeing the weaknesses exposed in quantifiable ways.
Yesterday I explained what happens when you get the architecture right. In this article, I will explain what happens when you get the architecture right, but the business lags behind it. Agentic architecture is business architecture, and vice versa.
Baidu just had this conversation publicly during its most recent earnings call, and it wasn’t pretty. It didn’t have answers to those questions, and doesn’t seem to have the frameworks or methods to get them. Its AI business grew 25% and now accounts for half of Baidu’s core revenue. GPU cloud grew 283%. Total revenue fell 4%, and that’s the fifth straight quarterly decline.
Google faced the same disruption with the same technology and grew 24%. Investors are making comparisons in this market, and this is your opportunity to provide clarity to the C-Suite.
I understand many will be quick to point out that Baidu operates in China while Google is in a very different market. Alibaba’s results today prove that the China variable isn’t a factor. Strategy is. Its revenue is up by 9% for the same quarter that Baidu just reported.
Yes, there are significant differences between the two companies’ business models and where they derive most of their revenue from. As I’ll explain in a minute, that’s my point. It’s not enough to just be ready for the technology. Businesses must also be ready for the new monetization paradigms as well. Most aren’t.
In this article, I’ll explain what Baidu got right, where the monetization strategy broke, and how you can tell whether your own transformation is heading in the same direction. The pattern generalizes beyond search. I’ve watched it repeat in insurance, finance, retail, industrial equipment, and enterprise software. The leading indicators are the same every time. The gap and steps to resolve it also follow a formula.
You can transform your technology model, but leave your business model behind, and it’s all for nothing. That’s what Baidu did. Technology can do everything right, and the business still fails to generate the growth it should.
I want to start here, because the simple analysis of Baidu’s results is wrong, and you’ll take the wrong lesson from it. Baidu climbed the capability maturity model in the right order and executed well.
Their GPU cloud has delivered four consecutive quarters of triple-digit growth. Management said that demand is broadening across gaming, autonomous driving, smartphones, and financial services rather than concentrating in one or two customers. Concentrated demand proves risk, rather than revealing a market. That’s why Microsoft went out of its way to make the same point.
Their model-as-a-service platform grew token revenue from external customers more than 9X. I don’t love the token-based pricing model, but it’s hard not to love that kind of growth story. Obviously, the demand is there.
Their robotaxi business delivered a million driverless rides in the quarter. They were granted the first fully driverless permits in a right-hand-drive market, started open road testing in London, and launched in Dubai. Baidu’s leadership made a solid unit economics case for international expansion. Higher taxi fares overseas produce better margins than in the Chinese market. (This is the one place that business, operating, and technology models align, but it appears accidental vs intentional.)
They’re also managing costs better than Google. Baidu reported a 12% non-GAAP operating margin and positive operating cash flow for a fourth straight quarter, even while the top line shrank. Google’s free cash flow, on the other hand, went negative for the first time in the company’s post IPO history.
This isn’t a case of a company that failed to understand AI, information, and agents. Baidu figured the technology out early and built more of the stack than most of its competitors did. The problem starts one layer above the technology: monetization.
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