Math, Bees, and the AI Language of Marketing

Most businesses think they have a messaging problem. They don’t. They have a translation problem.

Your customers don’t live in one place: they bounce between Google Search, Instagram, email, marketplaces, in-store, and word-of-mouth. They skim fast, they change their minds, and they bring different expectations to every channel. If you’re in Australian agriculture or AgriTech, you’ve also got an extra twist: your audience might be growers in the Riverina, procurement teams in Brisbane, exporters, agronomists, distributors, and seasonal casual staff—each with their own vocabulary and priorities.

Here’s a surprising place to borrow a solution from: interstellar communication theory. Scientists ask a hard question—how would we communicate with intelligent aliens with no shared language?—and one answer keeps coming up: mathematics. Even better, we can “test” this idea on Earth using a creature that’s genuinely alien to us in brain structure and evolution: the honeybee.

This post connects that bee-and-math idea to something practical: AI marketing automation. Not as hype, but as a structured approach to communication that behaves like a “universal language” across platforms and customer segments.

Bees show why “universal language” needs structure

If you want a universal language, you need a system that doesn’t rely on shared culture. That’s why maths is such a strong candidate. It’s consistent, repeatable, and can be transmitted as patterns.

The original research discussed how humans have tried to send maths into space for decades:

• Voyager Golden Records (1977): the cover used etched scientific and mathematical references as instructions for how to play the record.
• Arecibo message (1974): a binary signal of 1,679 bits arranged to describe basic numbers and DNA-related information.
• More recent work (2022) has explored binary languages designed to introduce maths, chemistry, and biology.

Now for the part that’s relevant to marketing: bees also respond to structured signals. Honeybees use the waggle dance to communicate food location—distance, direction, angle relative to the sun, and even quality. It’s not “language” in the human sense. It’s a compressed data format.

A useful definition for marketers: A universal language isn’t poetic. It’s structured.

That’s the bridge to AI. If your marketing relies on “clever wording” but lacks structure, it won’t translate across channels, devices, audiences, or seasons.

Bee maths is a lesson in what intelligence actually looks like

The myth is that intelligence requires a big brain. Bees are a clean rebuttal.

Between 2016 and 2024, researchers trained freely flying honeybees with outdoor tests (rewarding correct choices with sugar water). Across that work, bees showed evidence they can:

• Solve simple addition and subtraction (notably adding/subtracting one)
• Categorise quantities as odd or even
• Order quantities and understand magnitude
• Demonstrate a concept of zero
• Link symbols to numbers in a basic “numeral” system

This doesn’t mean bees “think like humans.” It means something more interesting: a different kind of mind can still converge on maths-like reasoning when the environment rewards it.

For AgriTech teams, that should feel familiar. Growers don’t care how your model works internally. They care that it’s reliable when conditions change—soil type, rainfall, pest pressure, input prices, labour constraints.

Intelligence, in practice, is pattern recognition + repeatable decision-making. That’s also what good AI systems bring to marketing.

Why this matters in Australian agriculture right now

January is a planning month for a lot of businesses: budgets, crop cycles, field trials, product releases, and event calendars get locked in early in the year. If you wait until Q2 to “fix marketing,” you’re usually patching problems you could have prevented with better structure.

In agriculture, the stakes are higher because:

• Buying cycles are seasonal (and often weather-driven)
• Decisions involve multiple stakeholders (grower + agronomist + partner + finance)
• Trust and proof matter more than vibes

So the question becomes: how do you make your message consistent across time, channels, and buyer types?

AI marketing automation works when you treat it like maths

AI isn’t a magic writing machine. I’m opinionated on this: if you use AI to produce more content without tighter structure, you’ll just publish more inconsistency.

The better approach is to treat AI as a way to encode and transmit structured meaning—a bit like the prime-number signal in the movie Contact, or like how bees encode direction and distance.

Here’s what “math thinking” looks like in marketing automation:

1) Define a shared “measurement system” across channels

If maths is universal, it’s because everyone agrees on units and rules. Marketing needs the same.

Set a small set of non-negotiable definitions, for example:

• What counts as a lead (form fill? phone call? qualified by job role?)
• What counts as engaged (30s on page? 2+ pages? video watch?)
• What counts as sales-ready (pricing page + case study + demo request)

Then your AI tools can actually optimise something real instead of chasing vanity metrics.

2) Build “signal patterns” customers recognise

Bees learn patterns. Humans do too.

Across your website, email, and social, keep consistent:

• Core offer structure (problem → proof → next step)
• Visual cues (product names, trial outcomes, key claims)
• Proof assets (trial results, ROI calculators, agronomist endorsements)

AI can help generate variations, but the pattern is what makes it coherent.

3) Use AI to translate, not to invent

Interstellar communication assumes you can’t rely on shared references. Marketing across segments is similar.

A grower, an agronomist, and a procurement manager can be looking at the same product and hearing three different things.

Use AI to:

• Reframe the same value proposition for different roles
• Adapt length and format (one-pager vs. email vs. landing page)
• Maintain compliance-safe wording (especially for claims in ag products)

The content should change. The meaning shouldn’t.

A practical “universal language” framework for AgriTech marketing

If you want a system you can deploy this quarter, use this 5-part framework. It’s built to be measurable, channel-agnostic, and easy to automate.

1) Start with a small set of “customer maths” variables

Pick 5–7 variables that show up in almost every buying decision. In Australian ag, common ones are:

• Hectares / herd size (scale)
• Crop type or enterprise type
• Region (climate + logistics reality)
• Input cost sensitivity (budget pressure)
• Labour availability (automation appetite)
• Risk tolerance (early adopter vs proven-only)
• Compliance constraints (export standards, residue limits)

These become your segmentation inputs.

2) Create message templates that behave like formulas

Write messaging as a formula you can re-use:

• Claim: what improves (yield stability, labour hours, wastage, disease pressure)
• Mechanism: how it improves (monitoring, prediction, automation, decision support)
• Proof: trial, case study, benchmark
• Constraint: where it doesn’t fit (honesty builds trust)
• Next step: demo, quote, field day, trial

That’s your “binary” language—simple, consistent, and hard to misinterpret.

3) Automate cross-platform delivery (without losing intent)

Cross-species communication is hard because signals distort. Cross-platform marketing is the same.

A tight automation stack should:

• Track intent (pages, downloads, replies, calls)
• Trigger the next best message (email sequence, retargeting, SMS reminders)
• Keep a single source of truth in the CRM

If you can’t map a customer journey in 10 boxes or less, it’s too complex to automate cleanly.

4) Add a “dialect layer” for segments

The bee-and-alien metaphor matters here: even if maths is universal, expression differs.

So keep your core structure constant, and vary the dialect:

• Growers: outcomes, reliability, season timing
• Agronomists: mechanism, evidence quality, integration
• Procurement: cost, risk, supply continuity, service levels

AI makes these translations fast—but only if you’ve defined the core meaning.

5) Audit your system like a scientist (monthly)

Bees learned through consistent feedback (reward). Your automation needs the same.

Once a month, review:

• Lead-to-meeting rate by segment
• Cost per qualified lead (not just lead)
• Time-to-first-response on inbound enquiries
• Content that accelerates deals (case studies, calculators, trial protocols)

If you don’t measure it, you can’t improve it. That’s not a slogan; it’s the entire reason maths works.

“People also ask” questions (answered straight)

Is mathematics really a universal language?

It’s the strongest candidate we have because it relies on patterns, quantities, and relationships that don’t require shared culture. The open question is whether different intelligences develop different “dialects” of maths.

What do bees prove about intelligence?

Bees show that small, very different brains can still perform basic numerical reasoning (including simple arithmetic and symbol-number learning) when the environment rewards it.

What’s the marketing takeaway from interstellar communication?

Structured signals beat clever wording. The more your marketing behaves like a system—clear definitions, repeatable patterns, measurable outcomes—the easier it is to automate and scale across channels.

Where this fits in AI in Agriculture and AgriTech

This series is about practical AI—crop monitoring, yield prediction, sustainable inputs, smarter logistics, and systems that reduce waste. Marketing is part of that system.

If you’re building or selling AgriTech, you’re already comfortable with models, variables, and feedback loops. Apply the same mindset to communication:

• Define the variables that matter
• Encode meaning consistently
• Let AI help translate across audiences and platforms
• Measure outcomes like you would in a trial

The reality? AI becomes most useful when it turns marketing into something closer to maths: structured, repeatable, and testable.

What would change in your pipeline this season if every channel spoke the same “language”—and every prospect got the version that matched their role and risk profile?