I had to read Jon Block's piece in The Media Leader about five or six times before I properly understood what he was arguing. Not because it's poorly written - it's actually a complicated and nuanced subject that needs some serious thinking. So let me try to summarise it, in case you felt the same way.
Here's what I think he's saying: AI agents are starting to make media planning decisions, and they're favouring digital over TV. Not because digital works better, but because the data is denser and easier to compute. His solution is that broadcasters need to build "queryable knowledge architectures" - structured data sources that agents can access in real-time via protocols like MCP (think of it as an API, but specifically built for AI) - rather than falling back on whatever the foundation model learned during training.
I think that's... actually pretty sensible?
But before we move on, I want to push back on one bit of the framing. Block suggests that foundation models are "trained on the open internet" which is "dominated by digital metrics," with the implication being that AI is somehow ignorant of TV's value.
I don't think that's true.
Here's the thing... foundation models have read a lot. They've ingested decades of IPA Effectiveness data, everything Peter Field and Les Binet have written, the Thinkbox research library, Ehrenberg-Bass, Byron Sharp. Claude knows the 60/40 rule. It knows why it's been revised. Ask any LLM "is TV advertising effective?" and you'll get a nuanced answer about reach, attention quality and long-term brand effects. Because it's read the research.
The idea that AI is ignorant of TV's value just doesn't stack up.
Where I agree with Jon - and this matters - is that knowing the theory isn't the same as being able to act on it. Digital wins by default not because AI prefers it, but because digital metrics are easier to compute. Clicks, conversions, last-touch attribution... they're clean signals. Brand lift, memory encoding, attention quality... they're messier and harder to feed into an optimisation loop.
So when agents favour digital, I don't think that's bias per se - and this is important, because "bias" is becoming one of those words thrown around AI quite a lot, in many instances with absolutely good reason. AI assuming people are white middle-aged men, for example. But this isn't one of those occasions. I think that's agent configuration. There's a difference between a foundation model and an agent. Agents use foundation models, yes, but you can point them at specific context and tell them to prioritise it. That's what RAG is for. It takes work to set up properly - I'm not pretending it's trivial - but it's absolutely doable.
Which brings me back to Block's solution - broadcasters building queryable knowledge architectures - an idea I actually really like. Imagine Thinkbox or ITV or Channel 4 creating structured effectiveness data with MCP access that any agent could query would be genuinely useful.
But here's my concern - can you trust broadcasters, or any media owner let's be honest, to be honest brokers of that data? If the MCP says "TV is always the answer," that's just a sales team with an API, isn't it? You'd be swapping one bias for another.
I think the responsibility sits with whoever's building the agent - probably the agency. Use broadcaster data, sure, but as one input among many. Test it, validate it against your own models, blend it with sources you trust. Same as you'd do today with any media owner research. Nobody would just plug into a broadcaster's MCP and say "right, that's our planning sorted."
And let's not forget the most important thing in all of this - keep the human in the loop. The planner absolutely needs to be the one who looks at the plan and has final approval before any execute button is pressed. What makes media planning great is not just data analysis and historical expectations of efficiency - it's the human judgement and experience that you just can't codify. Not yet anyway. So please don't read this as "we can build an agent that actually works so we can get rid of planners and buyers." I just don't believe that's the case.
I'm writing this because I worry articles like this - however well-intentioned - risk creating an anti-AI narrative in TV circles that becomes self-fulfilling. If broadcasters believe AI is inherently biased against them, they'll disengage rather than lean in and help shape how these tools are built. And that would be a shame, because the fix is genuinely within reach.