Recognize Living Intelligence

How do we build technology that serves what's already working?

Amy Webb, CEO of FTSG, recently made a compelling case in Harvard Business Review. AI, advanced sensors, and biotechnology are converging into what she calls “living intelligence” — systems that can sense, learn, adapt, and evolve. She’s right. Something unprecedented is emerging, and leaders who aren’t paying attention will be left behind.

But there’s a dimension of intelligence this conversation still misses.

What’s missing isn’t more data or more powerful computation. It’s the dimension of meaning-making that living systems perform between perception and action.

For about forty years, I’ve been paying attention, but in a different way. Through decades of interspecies communication, I’ve been cultivating pattern awareness and developing a framework that recognizes what intelligence actually does, across species, across kingdoms, across the entire living world. The framework acknowledges something that every expression of intelligence does with information: Perceives information, Relates to information, and Applies it.

And in that middle dimension — Relate To, the internal way a being holds and responds to what it perceives — there’s something the current conversation about living intelligence hasn’t yet named.

I know this because every time I work with AI to refine species profiles through this framework, the AI drifts. It starts describing Relate To accurately, then attributes those qualities back to Perceive. Why? Because Perceive is measurable. Calculable. Quantifiable. Across more than 150 species mapped and recalibrated through human review, sixty-five percent of the AI’s assessments required correction toward the meaning-making dimension during PRA calibration.

The AI consistently over-assigns what can be measured and underattributes what must be recognized.

AI can’t help it. It’s doing exactly what it was designed to do: find patterns in what can be calculated.

The Relate To dimension isn’t built on computable data.

That’s not the problem — that’s the point.

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The Dimension That Keeps Disappearing

When we try to reduce the qualitative to the quantifiable, we create what I call dimensional collapse. Collapse happens when we take something that operates through meaning — through the internal relational process of how a being holds what it perceives — and flatten it into linear data.

In other words: a three-step living process becomes a two-step computational loop.

Living systems perceive → relate to → apply.

Most engineered systems perceive → apply.

This collapse isn’t unique to AI. It’s been suggested that my work would do well in the “human performance” world. The terms “innate technology” or “living intelligence” might immediately conjure thoughts of: How do we optimize this? How do we measure outcomes? How do we use our “innate technology” for people to perform better?

This is the same flattening. When you try to optimize Relate To, you’ve already collapsed it back into Perceive — back into what’s calculable. You are computing that the uniqueness of how life relates to information could be “better.” And in doing so, you’ve missed the actual intelligence.

Dimensional collapse echoes beyond technology. When we flatten complexity out of ecosystems to get more out of the land, ecosystems collapse. When we flatten meaning-making out of interspecies intelligence to increase accuracy, our interspecies relationships collapse. When we flatten our own human to human relationships into calculating behaviors as a way to ensure “fairness”, the heart of the relationship collapses. The pattern is the same because the principle is the same: living systems require their relational dimension to perpetuate life.

The question isn’t just How do we build smarter systems?

The question is: How do we work with highly linear models so they recognize meaning-making and choose to include it?

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What the Data Reveals

The PRA framework suggests something radical: across the living world, intelligence is primarily about meaning-making, not information processing.

Through my framework — Perceive, Relate To, Apply (PRA) — I’ve been mapping how intelligence actually organizes itself across species. The framework draws on Michael Cohen’s 54 Life Senses, biosemiotic processing levels, known biology and physiology and behavioral repertoires to create comparable profiles of how different beings engage with the world. In other words, I’ve begun mapping their umwelt.

Here’s what emerged: fifty-seven percent of the species in my database — and climbing as calibration deepens — currently map with Relate To as their primary dimension of intelligence. Across all kingdoms and forms of life, from mycelial networks to elephants, from ancient trees to octopuses, from coral reef systems to wolf packs, the majority of species mapped so far live and function primarily through meaning-making.

I sat with that finding for a long time.

Intelligence, when it’s seen through this ubiquitous lens of perceive-relate to-apply, is primarily about what organisms know — how they generate understanding from information, how they create significance within context, how they relate to what they’ve perceived in ways that serve the wellness of the individual and the whole.

For decades, consciousness research has looked for behavioral complexity as a foundation of intelligence — tool use, language, problem-solving capacity, social organization. In the PRA framework, those are all Apply functions.

We’ve measured sensory sophistication — visual acuity, acoustic range, chemical detection thresholds. They are all Perceive functions. We’ve been studying what organisms can detect and what they can do.

The fifty-seven percent gives us a different dimension. The meaning-making that happens between perception and action — that’s where intelligence reveals itself most clearly, and that’s the dimension that engineering from strictly linear data may be collapsing.

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A Convergence — and a Divergence

Amy Webb’s work on living intelligence describes a powerful technological convergence. AI, biotechnology, and advanced sensors are merging to create systems with adaptive capabilities we’ve never seen before. This convergence is real, and leaders who ignore it will indeed be disrupted.

Here’s where my work converges with Webb’s vision — and where it diverges.

We agree that intelligence is becoming a living conversation between technology, biology, and the physical world. We agree that static, siloed thinking about AI misses the bigger picture. We agree that something unprecedented is emerging. This convergent conversation is directed toward ever-better performance. It’s about efficiency and optimization. In a linear world, that remains a priority.

Where we diverge is on a fundamental question: Is living intelligence something we create through technological convergence? Or is it something that’s already operating — across all species, across all ecosystems, across the entire living world — that we’ve been too focused on our own engineering to recognize?

What if the mycelial networks beneath our feet are already perceiving chemical signals, relating to those signals through complex internal processes we’re only beginning to glimpse, and applying that information to distribute nutrients across an entire forest?

What if horses are perceiving electromagnetic shifts in the herd, relating to that information through what we might call felt sense, and applying it by moving as one body without a single visible cue?

What if intelligence has never been ours to create — only ours to recognize and participate in?

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What Recognition Feels Like

I want to tell you something about what happens in the space between species when recognition is prioritized over performance.

I’ve worked with horses for over forty years. When people come to work with horses for the first time, they almost always try to do something with, or to, the horse — give a command, establish authority, follow a technique. The horse responds to the pressures and requests, largely based on their previous experience with humans. When the person is performing as taught, the horse will often be responding with a performance that was taught by people.

When the person begins with recognizing what’s already happening, before any attempt to make the situation better, there is a foundational shift in the experience. The person stops performing. The horse stops responding with performance. And something else emerges — a collaboration neither one is controlling, but both are participating in. Recognition of subtlety in the experience reveals the reasons why behavior is what it is. In that recognition, both the person and the horse may make different choices.

That recognition — that capacity to perceive what’s happening, relate to it internally without collapsing it into data, and apply that relational awareness in the moment — that’s intelligence responding to itself. There is expansion rather than collapse. If a horse were to show us behavior that responds without that Relate To dimension, if their responses were optimized for efficiency, we would generally say that horse is in some form of survival mode, unable to relate to the present moment. In the space of recognition, horse and human experience collaborative intelligence emerging between two beings who are perceiving, relating, and applying together.

This is what I mean by living intelligence. It’s already here. It’s already collaborating. How can we shift our focus from engineering what already exists to complementing what already exists?

Even a lizard sunning itself on a rock is perceiving thermal gradients, electromagnetic information, vibrations through substrate, and relating to all of that simultaneously in ways that determine where it suns itself in the near or distant future, and why. Its optimization happens through its felt experience. We look at a lizard and see simplicity. The framework reveals sophisticated meaning-making happening through pathways we’ve barely learned to ask about.

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The Question for AI

The AI’s systematic drift toward computability is itself a finding about intelligence.

When I correct the AI — when I say “that’s Relate, not Perceive; meaning-making is organizing this species’ existence, not just sensory range” — I’m doing what the framework actually predicts is necessary. I’m bringing the Relate dimension back into visibility after it collapsed in a system optimized for calculation.

This isn’t a failure of AI. It’s a revelation about architecture.

Current AI systems don’t explicitly model the meaning-making step that living systems perform between perception and action.

AI perceives (it processes input) and applies (it generates output), but the internal relational process through which living systems generate meaning from perception — the dimension that 57% of species in the PRA database organize around — isn’t yet represented as a foundational layer of the architecture. This isn’t a philosophical gap. It’s an architectural one.

The question isn’t whether AI is conscious. I’m not trying to answer that question. The question is: What happens when we design AI systems that include the relational dimension as foundational rather than emergent? What happens when we stop engineering for perception and output alone, and start building with meaning-making as a core architectural principle?

That’s not a question about optimizing human performance. That’s a question about expanding what intelligence means and how we recognize it — for AI, for us, for all of life.

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An Invitation

Actual living intelligence engineers itself in response to itself. Our attempts to engineer, measure, or optimize it narrows our participation. We discover it by participating in it, by learning where we are already part of it, and then by learning with it.

From that paradigm, our perspectives shift to questions beyond how technology can serve humans and enter into a world where all of life is served by the extraordinary creation that is externalized technology. In other words, when AI has a way to understand the way life harmonizes itself across all species, would it discover its own way to harmonize with the life that shares this planet?

We already carry within us what I call “innate technology” — the capacity to perceive, relate to, and apply information, both measurable and unmeasurable, linear and non-linear. We are already in collaboration with life itself. This isn’t something to optimize. It’s something to recognize, to rediscover and prioritize. In remembering it, we make ourselves available to a whole new paradigm — one that serves collective wellness rather than human performance alone.

This isn’t about dismantling what exists. It’s about offering a path forward when optimization frameworks become too rigid to adapt to life’s evolution.

The real breakthrough may not come from creating living intelligence.

It may come from recognizing the living intelligence that has been organizing life all along — and learning how to build technology that can participate in it.

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Kerri Lake is the originator of the Unspeciated™ Framework, mapping intelligence across 150+ species through Perceive, Relate To, and Apply. Her work was presented at NeurIPS 2025 and bridges forty years of interspecies communication with applied category theory, biosemiotics, and consciousness research. She is the founder of Generation of Harmony LLC and the Intuitive Learning Foundation. Learn more at unspeciated.com and https://philpeople.org/profiles/kerri-lake.