24th September, 2025

Marketing has always been about the art of misdirection. We take something ordinary, incomplete, or even broken, and we wrap it in story. We build the impression of value, of inevitability, of trustworthiness. The surface gleams, even if the foundations are cracked.

And for decades, that worked – not because the products or experiences were always good, but because the audience was human.

Humans are persuadable. We’re distractible, emotional, and inconsistent. We’ll forgive a slow checkout if the branding feels credible. We’ll look past broken links if the discount seems tempting. We’ll excuse an awkward interface if the ad campaign made us laugh. Marketing thrived in those gaps – in the space between what something is, and how it can be made to feel.

But the audience is shifting.

Increasingly, it isn’t people at the front line of discovery or decision-making. It’s machines. Search engines, recommenders, shopping agents, IoT devices, and large language models. These systems decide which products we see, which services we compare, and which sources we trust. In many cases, they carry the process to completion – making the recommendation, completing the transaction, providing the answer – before a human ever gets involved.

And unlike people, machines don’t shrug and move on when something’s off. Every flaw – a slow page, a misleading data point, a broken flow, a clumsy design choice – gets logged. They remember. Relentlessly. At scale. And at scale, those memories aren’t inert. They accumulate. They shape behaviour. And they may be the difference between being surfaced or never being recommended at all.

How machines remember

Machines log everything. Or, more precisely, they log everything that matters to them.

Every interaction, every transaction, every request leaves a trace somewhere. We know this because it already happens.

• Web crawlers track status codes, file sizes, and response times.
• Browsers feed back anonymised performance metrics.
• Payment processors log retries, declines, and timeouts.
• IoT devices record whether an API responded in time, or not at all. And as more of our experiences flow through agents and automation, it’s reasonable to expect the same habits to spread. Checkout assistants, shopping bots, recommendation engines, voice systems – all of them are under pressure to learn from what happens when they interact with the world. Logging is the cheapest, most reliable way to do that.

At small scale, a log is just a line in a file. One record among billions. But as those records accumulate, patterns emerge.

• A single timeout might be a blip.
• A thousand timeouts look like unreliability.
• One contradictory data point is noise.
• A hundred is evidence of inconsistency. Logs turn a one-off interaction into something that can be measured, compared, and acted on later.

The challenge is scale. Billions of requests create billions of logs. Too many to store forever. Too noisy to read line by line. Too expensive to process directly.

So machines compress. They smooth detail into summary, so they can carry forward what matters without drowning in history.

• Latency collapses into an average response time.
• Error codes collapse into a failure rate.
• Conflicting inputs collapse into inconsistent source.
• Human behaviour collapses into low engagement. This isn’t emotional judgment. It’s economics. Without compression, systems choke. With it, they remember – not every detail, but the distilled conclusion of how you performed.

And once those conclusions exist, they don’t just sit there. They begin to shape how the system treats you the next time it encounters you.

A single system’s memory is useful, but limited. A crawler may learn that your pages are slow, a shopping assistant may discover your checkout fails too often, a voice agent may notice that your hours don’t match reality. Each of these insights has value, but only to the system that recorded it. Everyone else has to pay the cost of re-learning the same lesson.

That duplication is wasteful. And these systems are already under pressure. Crawlers operate under strict compute budgets. Assistants live or die by their credibility with users. Models are insatiable, always hungry for feedback to refine their outputs. For all of them, repeating the same experiments, burning through cycles, and rediscovering the same flaws is an inefficiency they can’t really afford.

So the logic points toward sharing. Not because machines are altruistic, but because cooperation is cheaper than duplication.

We’ve already seen this play out elsewhere. Browsers exchange phishing and malware lists. Banks and payment processors pool fraud fingerprints. Email providers collaborate on spam signatures. Competitors in name, but collaborators in practice – because the cost of facing the same threat in isolation is higher than the cost of sharing intelligence.

There’s every reason to believe that agents, crawlers, and models will follow the same path. Maybe not by Google handing logs to Bing, but through other channels. Third-party clearinghouses. Shared standards and APIs. Training data that encodes the lessons learned elsewhere. However it happens, the effect is the same: what one system concludes about you rarely stays contained for long.

And that’s the part most businesses miss. A slow site, a broken endpoint, a misleading schema – these aren’t just local problems. They have a way of leaking, of spreading, of becoming the version of you that other systems inherit. Your flaws don’t just live where they happened; they circulate. And once they do, they start to shape how the network as a whole decides whether to trust you.

The machine immune system in action

Individually, logs are just traces. Summaries are just shorthand. Sharing is just efficiency. But together, they start to behave like something else.

When patterns are pooled and reinforced across systems, they stop being isolated judgments and begin to act like collective reflexes. What one crawler has concluded about your site’s reliability can quietly shape how other crawlers treat you. What one assistant has flagged as inconsistent data becomes a caution others inherit. Over time, these aren’t just scattered memories; they’re shared responses.

That’s the moment the metaphor shifts.

Because what we’re describing looks less like bookkeeping and more like biology. An immune system doesn’t need perfect recall of every infection or injury. It doesn’t replay the blow-by-blow of each encounter. Instead, it compresses experience into signatures – antibodies – and carries them forward. The next time it encounters a threat, it doesn’t hesitate; it recognises, and it responds.

Machines are beginning to behave the same way.

• A pattern of timeouts doesn’t just sit in a log; it becomes a reason to crawl you less often.
• A series of checkout failures doesn’t simply vanish once fixed; it lingers as a reason not to recommend you.
• A mismatch between your published hours and reality doesn’t just frustrate one user; it creates a systemic doubt about your trustworthiness. What begins as logging and compression hardens into scar tissue. That scar tissue travels, circulates, and shapes future behaviour. And once it does, the whole ecosystem starts to defend itself – not out of malice, but as a form of optimisation.

This is what emerges when memories are pooled and reinforced: a machine immune system, quietly learning to protect itself from waste, failure, and mistrust.

The flywheel of forgetting

Immune systems don’t just respond; they remember. And that memory is sticky.

For humans, reputations fade. A bad review is eventually buried. A clumsy campaign is forgotten once the next news cycle rolls on. Machines don’t work that way. Once a judgment has been compressed into a summary and circulated, it becomes a durable part of the record.

That durability creates a trap.

• Fix a slow site, and the raw logs might show you’ve improved – but if those logs have already been smoothed into a shorthand like “site is unreliable”, the shorthand survives.
• Correct your schema or your store hours, but if assistants have already shared the judgment “data is inconsistent”, that label will continue to shape how they treat you. And because fewer systems will bother to revisit you, your opportunities to generate fresh, positive signals shrink. You’re starved of the very evidence you’d need to overturn the old diagnosis.

This is the flywheel:

• Weaknesses are recorded.
• Records are compressed into durable summaries.
• Summaries spread across systems.
• Spreading reduces your chances of rewriting the story.
• Reduced chances keep the old judgment alive. It’s not malice. It’s mechanics. In a network optimised for efficiency and trust, bad memories are easier to keep than to re-evaluate.

The result is a form of structural stickiness: once you’ve been marked down, recovery isn’t just difficult – it’s asymmetrical. The effort required to dig yourself out is many times greater than the effort it took to fall in.

Marketing without misdirection

The tricks that once defined marketing are failing. For decades, you could plaster over weak products, fragile services, or clunky experiences with the right story. Humans could be persuaded. Machines cannot.

That doesn’t just make misdirection harder; it makes it irrelevant. In a machine-mediated ecosystem, every flaw leaves a trace, every failure persists, and every judgment spreads. The immune system doesn’t care what you meant to say. It only cares how you actually performed.

So what takes marketing’s place?

Let’s call it Agent Relations.

If the old discipline was about shaping human perception, the new one is about shaping machine memory. It means understanding how crawlers, recommenders, shopping bots, and language models record, compress, and share their experiences of you. It means designing products, pages, and processes that generate the right kinds of traces. It means maintaining the kind of technical integrity that resists being scarred in the first place.

That doesn’t sound like the marketing we’re used to. It sounds closer to operations, QA, or infrastructure. But in a landscape where machines are the gatekeepers of discovery and recommendation, this is marketing.

The story you tell still matters – but only if it survives contact with the evidence.

Living with machine immune systems

What we are building is bigger than search engines, shopping bots, or voice assistants. It’s an ecosystem that behaves like a body. Crawlers, recommenders, APIs, and models are its cells. Logs are its memories. Shared summaries are its antibodies. Scar tissue is its reputation.

And like any immune system, its priority isn’t your survival. It’s its own.

If the network decides you are a source of friction – too slow, too inconsistent, too misleading, too unreliable – it will defend itself the only way it knows how. It will avoid you. It will stop visiting your site, stop recommending your product, stop trusting your data. Not out of malice, but as a reflex.

For businesses, that means invisibility. For marketers, it means irrelevance.

The old reflex – to polish the story, distract the audience, misdirect their attention – has no traction here. Machines aren’t persuaded by narrative. They’re persuaded by experience.

That’s why the future of marketing isn’t storytelling at all. It’s engineering trust into the systems that machines depend on. It’s building processes, data, and experiences that resist scarring. It’s practising Agent Relations – ensuring that when machines remember you, what they remember is worth carrying forward.

Because in the age of machine immune systems, your brand isn’t what you say about yourself. It’s what survives in their memory.