Now, you can ask an even more pointed question – perhaps adding in a preference for arch support, a shopping mile radius – to a large language model (LLM) and get a clear, context-rich answer: “Here are three nearby options that fit your criteria. The top-rated one is available for pickup in 40 minutes.”

It’s an improved interaction, but not at the cost of a more complex user experience. This new way of search is redefining consumer behavior and expectations, and how marketers must approach brand visibility. In fact, it represents a reconfiguration of digital marketing and a new economy of visibility.

As these interactions become more complex and context-rich, the way we measure success must evolve too.

Visibility Is the New KPI

In traditional SEO, success means ranking on page one of Google. In the AI era, success means being part of the answer — cited, mentioned, or described accurately when an AI system responds.

This is not a mere marketing nuance: it’s a structural shift in how digital presence is valued. Companies that understand this will treat AI visibility as a new form of brand capital, something to monitor and manage as carefully as reputation or market share.

Advertising economics are already following this pattern: U.S. advertisers are projected to spend over $25 billion annually on AI-powered search placements by 2029, which is nearly 14% of total search budgets.

But, understanding how visibility is measured is just the first step. To capture it effectively, brands must recognize that product discovery itself is being reconstructed, with two distinct search experiences shaping how users find and interact with information.

Two User Experiences, Two Optimization Models

We now have two search experiences — traditional search and AI-driven search — each serving different user needs.

Frankly, this is the simplest framework to offer, when in fact, it is even more complex and nuanced once you take into account AI agents that act autonomously on behalf of the customer.

Traditional search is navigational, guiding users through lists of pages. Effectively, it points them in the right direction.

Meanwhile, AI-driven search is conversational, contextual, and consultative. It’s able to perform multi-step research, interpret context, and merge data from multiple sources into one synthesized response. For marketers, that means building for two visibility models: in SEO, we optimize for keywords; in AI discovery, we optimize for prompts.

The shift in user behavior is measurable and gaining ground. According to Semrush AI Visibility Index, between August and October 2025:

To stay visible, brands must start by identifying which questions matter most to their business – prioritizing prompts that are both high-volume and high-impact. Irrelevant traffic is wasted effort; rare relevance won’t scale. The sweet spot has always been where volume meets relevance, and AI discovery only raises the stakes—rewarding context, authority, and precision the same way great SEO always has.

As AI-driven and traditional search continue to evolve, the line between them is beginning to blur. Brands that optimize for both experiences today will be best positioned to thrive as these models converge into a single, unified discovery interface.

Preparing for the AI + Traditional Search Convergence

Eventually, you’ll see conversational answers alongside maps, reviews, and transactional links — a mix of synthesis and structure. When that happens, businesses will track two main metrics:

• Traffic, the traditional measure of visits
• AI Visibility, a new measure of how often and how accurately a brand appears in AI-generated responses

But visibility alone won’t be enough. The next wave of competition will happen at the content layer.

Brands will need to build for both bots and humans — crafting content that reads naturally, ranks intelligently, and feeds the context these models rely on. It’s a new kind of content development, where clarity for users and machine readability carry equal weight.

When that becomes common, websites will need to work as seamlessly for bots as they do for people. Features like SMS-based authentication or manual verification could block machine-driven transactions entirely. Businesses will need to rethink checkout and navigation to accommodate non-human operators.

While optimizing for visibility and content readiness is essential, the larger shift is economic: the convergence of AI and search is redefining how value is created, measured, and captured across the digital landscape.

AI Discovery and the New Economics of Search

The economics of search are changing.

This convergence of SEO and AI visibility is not a short-term marketing trend. It’s a deeper transformation — the creation of a discovery layer that connects information accuracy, credibility, and commercial outcomes in a continuous loop.

Within five years, we’ll unlikely distinguish between “search engines” and “AI assistants.” Instead, we’ll talk about several intelligent systems from companies such as Google and OpenAI that decide what people see, trust, and buy.

While the system itself is changing, the opportunity remains open. AI Search doesn’t belong only to the biggest players — it’s a reset. Smaller brands can rise faster by being precise, credible, and contextually relevant, while larger enterprises must relearn agility and authority at scale.

In traditional SEO, the strongest often dominated; in AI discovery, the most relevant wins.

Businesses that measure and manage their visibility within this new system will define the next era of digital competition.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

The latest: Amazon reportedly is in talks to invest $10 billion or more in the ChatGPT maker, which already counts giants like Microsoft among its investors. Perhaps the most recent (and loudest) news cycle, however, had nothing directly to do with funding at all—social media lit up with reports that CEO Sam Altman had issued a “code red” to the OpenAI team, saying it was time to double down on improving ChatGPT (the LLM that started it all) or risk falling behind.

Fortune’s tech team recently dove into what’s going on behind the scenes, in a feature published this week and helmed by Jeremy Kahn, Alexei Oreskovic, and Lee Clifford. They wrote: 

The internal call to arms lays bare the very precarious position this market leader is now in, particularly as it confronts industry titans like Google (as well as Microsoft and Meta), with tens of billions of dollars in cash on their balance sheets and massive ecosystems of products to boost their distribution.  

For Altman, a longtime tech entrepreneur, the historic matchups of Silicon Valley’s past, pitting innovators and incumbents in winner-takes-all battles, are surely contributing to the sense of urgency: The annihilation of browser pioneer Netscape by Microsoft or the eclipse of BlackBerry’s handheld communications gadgets by Apple’s iPhone comes to mind. But there’s also the example set by Facebook founder Mark Zuckerberg, whose famous “lockdowns” over a decade ago helped repel the threat of Google’s nascent—and ultimately doomed—social networking product. 

The decisions made by OpenAI and its competitors at this critical juncture in a fast-moving market will decide which company cements its hold on what some have called the most transformative technology since electricity, and which will end up as odd footnotes in the final writing of the history of AI. 

Many Term Sheet readers know I love a good history lesson. (Did you know that venture capital has its roots in the financial structure of whaling ventures?) And lots of people like to say that AI marks an Industrial Revolution-esque change. If I’m honest, for all my skepticism, I do believe that. 

So, that means history is being written right now. One “code red” here or there is incremental, but could make all the long-term difference. Read the whole story here. 

Term Sheet Next… My colleague Lily Mae Lazarus just published this Term Sheet Next profile of Ari Malik, cofounder of Salient AI. He talks candidly about the company’s $25 million ARR—and how they’ve yet to lose a customer to churn. Read it here.

See you Monday,

Allie Garfinkle
X: @agarfinks
Email: alexandra.garfinkle@fortune.com
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Venture Deals

– Lovable, a Stockholm-based vibe coding platform, raised $330 million in Series B funding. CapitalG and Menlo Ventures led the round, and were joined by Salesforce Ventures, HubSpot Ventures, Accel, Evantic, Creandum, and others.

– Edison Scientific, a San Francisco-based AI platform for scientific R&D, raised $70 million in seed funding. Triatomic Capital, Spark Capital, and an undisclosed major US institutional biotech investor led the round. They were joined by Pillar VC, Susa Ventures, Striker Venture Partners, Hawktail VC, Olive VC, and others.

– Endra AI, a Stockholm-based mechanical, electrical, and plumbing engineering platform, raised $20 million in seed funding. Notion Capital led the round, and was joined by Norrsken VC.

– Ember LifeSciences, a Westlake Village, Calif.-based cold chain technology company, raised $16.5 million in Series A funding. Sea Court Capital led the round, and was joined by Cardinal Health, Carrier Ventures, and others.

– ZeroPhase, a Munich, Germany-based startup building a communication layer for unmanned defense systems, raised €5.8 million in seed funding. BlueYard Capital led the round.

– Thread, a New York-based AI service desk platform for managed service providers, raised $18 million in growth equity funding. Susquehanna Growth Equity led the round, and they were joined by Headline.

Private Equity

– Arctos Partners acquired a minority stake in Monumental Sports & Entertainment, a Washington, D.C.-based sports and venue management company. Financial terms were not disclosed.