From Consulting AI to Collaborating With It

Two recent AI releases caught my attention, not because of what they do, but because of what they signal about where AI is heading.

Anthropic launched native multi-agent teams with massive context windows. OpenAI pushed agentic execution that carries work through end-to-end without constant human prompting. Both announcements pointed in the same direction: parallelism.

The important change here isn't "better answers." It's surface area. For the first time, a single person can think, explore, and execute in multiple directions at once. Not just faster answers to the same questions, but simultaneous exploration of entirely different paths.

That distinction matters more than it sounds.

From Tool to Collaborator

Most people still use AI the way they use a search engine. You have a question, you ask it, you get an answer, you go back to work. It's a consultation model, and it's useful, but it only goes so far.

What these new capabilities point toward is something different: AI as a persistent collaborator that's present in the work itself. Instead of stepping out of your workflow to ask for help, the AI is embedded in the process, carrying context, exploring branches, and flagging what matters while you focus on decisions.

The shift from "I asked AI" to "I worked with AI" may sound like semantics, but the compounding effects on output and learning speed are real.

Why This Matters for Founders

Startups win on iteration speed. The faster you can generate hypotheses, prototype solutions, and validate assumptions, the faster you learn. And learning velocity is what separates the companies that find product-market fit from the ones that run out of runway first.

Multi-agent parallelism changes the math on iteration. Instead of sequentially exploring one idea at a time, a founder can now spin up parallel explorations: test three positioning angles, prototype two technical approaches, and research competitive dynamics, all in the same working session. Each path informs the others. The feedback loops tighten.

This isn't about working harder or even working faster. It's about compressing the learning cycle so dramatically that a two-person team can cover ground that used to require ten.

Why Marketers Should Pay Attention

For growth teams, the gap between "using AI" and "working alongside AI" is about to become very visible in results.

Consider the difference between these two workflows:

The first is convenience. The second is leverage.

Tools like Nano Banana show what this looks like in practice, where creative assets ship in the same loop as copy and experimentation. The workflow collapses from days of back-and-forth into a single session.

The teams that figure out how to build these collaborative workflows won't just produce more. They'll learn faster about what resonates with their audiences, which segments convert, and where to double down. That knowledge compounds in ways that raw output volume never will.

The real risk for marketers isn't AI itself. It's being outpaced by competitors whose teams adapt to this new way of working first.

Where the Leverage Will Concentrate

Here's the question I keep coming back to: if models themselves are being commoditized (and they are, rapidly), where does the durable leverage actually live?

My bet is on the orchestration layer: memory, tool use, coordination between agents, and the workflows that tie them together. The model is the engine, but the value increasingly sits in how you wire engines together to do useful work.

That said, it's an open question whether the orchestration layer itself will commoditize over time. If agents become a standard abstraction above models, does the value move up another level to the domain-specific applications built on top? Or does the orchestration layer develop enough defensibility through proprietary data, workflow lock-in, and compounding data advantages to hold?

The next few years will answer that. In the meantime, the founders and teams who are building with these tools (not just watching from the sidelines) will be the ones best positioned to capture value wherever it settles.