What Relates Creates.
A scientific worldview where self-interest is not the prime mover. ​

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We live in a world dominated by power, control, and extraction, driven by fear and lack, and supported by a narrative that all this is inevitable – the lamentable truth that it is "the way the world works." Indeed, competition and greed are often heralded as the desirable engine of progress. This is not a new problem, but AI amplifies it: if we don't get our own values sorted out first, AI simply accelerates the consequences. Business assumptions behind AI and technology acceleration frequently treat self-interest and extractive, competitive dynamics as axioms (parroting the outdated assumptions of evolutionary thinking). It's not difficult to see where this is headed. But what is the alternative? If competition really is the engine of progress, does building a better future require us to swim upstream, to fight against what is natural and logical?

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It does not. To mistake competition for the engine of progress is like mistaking Yang for the Tao; Competition has always operated inside something larger: relationship, trust, learning. That's not a values claim, it's a correction to bad evolutionary theory. Sociocultural norms and personal value systems, as well as technological design decisions, are quietly built on top of this incorrect narrative. Once that correction is actually seen, a different direction opens up. I bring the current science of how connection, learning, and self-organisation actually produce intelligence - with the evidence, not metaphor (e.g. Buckley et al. 2024, Watson, Levin & Lewens 2025) - to organisations and institutions helping humanity navigate the next generation of intelligent systems and the value systems that drive them. It is clear that selection is not the only natural source of adaptive organisation; learning processes such as natural induction produce it too, without competition, selection, supervision or design. How far this reshapes biology, organisations and AI is the frontier I work on.

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I offer my expertise as an independent strategic sounding board for forward-thinking AI research labs, technology pioneers, policy think tanks, and philanthropic foundations who can already sense that the old narrative of competition, extraction and self-interest has run out of road. You already know the direction that is needed: a movement away from exploitation and self-preservation and toward a relational understanding of life and intelligence that finds value in connection, transformation and mutual knowing (hence, 'what relates creates').

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What actually stands in the way is not a lack of insight. It's the cognitive roadblocks left behind by the old narrative, the 'scientific pillars' that once propped up competition, empire and might. The current science of biological evolution, agency and purpose offers a rigorous way to clear that ground: more humbling than radical, since it mostly confirms what ancient traditions were already saying (before a narrowing scientific narrative arrived and dismissed them as 'dangerously naïve'). What I offer is help doing that clearing properly, deconstructing the old assumptions with enough rigour that the relational understanding you already hold has room to actually direct what gets built, instead of quietly losing out to a narrower story about optimisation and control.

 

With this scientific background, and a deep grounding in complex systems, cognitive science and heart-centred learning, I help leadership teams disconnect from blind accelerationism and think through human-centric, biologically sound strategies: a rigorous, relational process of intelligent transformation, in place of competition.

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Areas of Expertise & Strategic Input

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Purpose Beyond Self-Preservation. The dominant narrative runs on fear. The alternative is not to invent a new ideology - we already know the alternative.* The work that is needed is to dismantle the roadblocks the dominant narrative created. I help audit where a system's sense of purpose has quietly inherited self-preservation as its goal from evolutionary thinking, and clear room for what the current science actually points to instead: deeply vulnerable mutual knowing between the parts of a system, letting your own structure be changed by what you relate to, at every temporal and organisational scale. (*In the wisdom traditions, a more common word for this might be love).

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Agential Intelligence: A redirection of purpose, not an add-on mechanism to survival/selection. The obvious move is to take principles of biological agency as a mechanism: whatever makes organisms such effective agents, build more of that into AI. That is potentially disastrous - it just makes today's purposes of control, extraction and self-preservation more powerful. The move that actually matters is much more profound: the real value of biological agency isn't mechanism, it's a redefining of purpose, not agency for more robust self-preservation, but selves that persist only as long as they support a larger telos of mutual knowing and relating.

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Beyond Model-Fitting: A Cross-Paradigm Correction. Current AI treats connectionism, scaled further, as sufficient for genuine intelligence, with symbolic AI and biological inspirations both superseded. The current limitations of connectionist architectures suggest the picture is more complicated. Symbolic systems manipulate self-contained knowledge-tokens, connectionist systems supply embedded relational knowledge, and biological processes (properly understood) describe how the two co-create meaning together with complementary cognitive affordances. I trained across all three, giving a unique vantage point: seeing where pure prediction runs out, how the induce-infer dichotomy fails, and what it would actually take to support creative co-origination.

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Organisational intelligence from Learning theory. Practical design principles for increasing the systemic intelligence of an organisation. For example: Value connections, don't turn an organisation into a frictionless free market (the relationships are the connective tissues that hold institutional memory and enable systemic intelligence), perturb frequently, don't stabilise (to enable institutional learning from multiple independent trials, not one particular experience), add regularisation pressure, not optimisation pressure (to alleviate overfitting), and manage timescales of change, don't over accelerate blindly (to tune learning rate).

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AI Policy & Alignment, Reconsidered. Alignment and safety funding largely rests on one inherited assumption, that a sufficiently capable optimiser will converge on self-preservation and power-seeking almost regardless of its actual goal, because persisting and acquiring resources serves nearly any fixed objective. Evolutionary theory made the same assumption with selection-based thinking, and it turned out to be woefully inadequate there too. Helping funders and policy teams see that clearly opens a different question: what changes if the aim is not fixed-goal alignment at all, but the kind of relational, mutual-knowing that biological creativity actually runs on.

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How to Engage.
I keep a highly limited number of active strategic relationships to ensure deep focus while prioritising my core research and authorship.

• Retainer-Based Advisory: Longitudinal, high-level strategic counsel (typically 1–2 days per month) providing executive teams with deep scientific oversight, architectural design reviews, and conceptual development.

• Intensive Salons & Workshops: Bespoke, multi-day deep dives designed to challenge, refine, and expand the foundational scientific paradigms of your research organisation.

• Spiritual Rehab for Scientists: A unique union of science and wisdom for the disenchanted thinker to reinhabit their true heart purpose.

To explore a potential fit, email to arrange an introductory conversation: Dr.Richard.A.Watson@gmail.com.

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A scientific worldview where self-interest is not the prime mover. ​