In the opening keynote of the Future Intelligence Forum, cognitive neuroscientist Dr. Heather Collins argued that the economy has entered a new phase. Information became free with the internet; with AI, knowledge has become free as well. What remains scarce, and what will separate the organisations that succeed from those that fail, is human judgment.

At MDOTM’s Future Intelligence Forum, cognitive neuroscientist Dr. Heather Collins opened with a simple claim: the economy has entered a new phase. The internet made information free. AI is now making knowledge free as well. What stays scarce, and what will separate the organisations that succeed from those that fail, is human judgment.

For asset and wealth managers, who increasingly use AI in portfolio construction, research and client reporting, her message is practical: AI can produce the signal, but people still own the decision.

Key takeaways

• AI makes knowledge abundant, so human judgment becomes the differentiator.
• The brain is a prediction machine that reads context, something AI does not do.
• Fluency bias makes fast answers feel true. When the stakes are high, slow down.
• Use AI for “brain gain”, not “brain drain”, with a three-step framework: Construct, Challenge, Calibrate.
• Meaning, not capacity, is where humans still lead.

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From a knowledge economy to a judgment economy

For most of history, information was scarce and valuable. The internet made information abundant, and advantage moved to knowledge: the ability to make sense of it. AI now makes knowledge abundant too. “Our culture, society and economy are no longer knowledge-based. They’re judgment-based,” Collins said.

There is an important difference from the internet era. Search engines gave us information, but we still had to select, organise and integrate it, a demanding process that built knowledge. AI delivers that knowledge in a single, finished answer. Collins expects the consequences to become clear in about ten years, when the first generation of AI natives enters adulthood. Employers are already adjusting: many expect new hires to use AI fluently, including deploying agents, while keeping their own critical thinking.

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How the brain makes decisions: a prediction machine

Collins began with a basic principle of neuroscience: the brain is a prediction machine. Its primary function is to anticipate what happens next in order to keep us alive. The prefrontal cortex extends this capacity to professional life, letting us predict how a meeting will unfold, whether a deal will close and which move is most likely to succeed.

This predictive capacity is the foundation of human decision-making. Understanding how it works, and where it can mislead us, is the first step toward using AI well.

Why context is a feature, not a bug

To show how the brain differs from AI, Collins ran a simple experiment. Half the audience saw an image of a dinner plate, the other half a flower. Everyone then unscrambled the same letters. The room split almost exactly between “plate” and “petal”, each shaped by what that group had just seen.

Both answers were correct. The point is that the human brain is highly sensitive to context, whereas AI processes data in a supposedly neutral way. “We have a room to be in. We have an environment to pay attention to. We have context,” she said. That context can change the quality of a decision entirely.

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AI produces the signal, humans own the decision

Collins used radar in the Second World War to describe the relationship between people and technology. Radar was excellent at producing a signal that something was in the airspace. But the operator still had to decide whether it was an enemy aircraft or a flock of birds.

That decision carries two possible errors. A false alarm wastes resources and puts people at risk unnecessarily. A miss lets a real threat pass unnoticed. In the same way, AI produces signals, and different people interpret the same output differently depending on their tolerance for risk. Neither the cautious nor the bold reading is automatically right. What matters is that responsibility stays human:

AI can make the call, but only humans can live with the consequences.

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Fluency bias: why fast answers feel true, and why to slow down

A second experiment, a version of the Stroop test, asked the audience to name the colour in which each word was printed, while the word itself named a different colour. The task is uncomfortable because reading is automatic and has to be actively suppressed.

Collins used it to explain fluency bias: “Anything that comes fast, your brain believes is true.” Because AI generates information faster than we can absorb it, it can create an illusion of accuracy. Her advice is counterintuitive: when the stakes are high, slow down. A deliberate pause engages the anterior cingulate cortex, which she described as the brain’s router. It detects a conflict and sends the information to the prefrontal cortex for careful evaluation. “If we slow down a little bit, the decision-making quality will actually increase.”

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Brain drain or brain gain? A three-step framework for using AI

Collins was clear that AI is extremely useful, and that she uses it every day. The question is how. In brain drain, people offload their thinking to the tool, fall into fluency bias and develop miscalibrated confidence in answers they have not verified, despite the known risk of hallucinations. In brain gain, AI amplifies human strengths through knowledge building, critical thinking and metacognitive awareness.

She proposed three steps to stay on the right side of that line:

1. Construct. Define the problem before using AI, whether it is an investment, a partnership or an acquisition. Even a few minutes of independent thinking builds a foundation. “Your brain deserves a right to a first draft.”
2. Challenge. Keep prompting, examine the details, and ask the AI to challenge your assumptions, biases and blind spots. This turns a one-way flow of information into a genuine exchange that stimulates critical thinking.
3. Calibrate. Develop metacognitive awareness, an accurate sense of what you know and what you do not. Collins asked the audience for the capitals of Chile and Australia: many answer Sydney with high confidence, although it is Canberra. Regularly asking “how confident am I?” is the quickest way to build this skill.

Whether AI will change the brain itself remains an open question, she noted. Research has not caught up with the technology, although one finding is well established: attention spans have measurably declined over the past two decades. The outcome will depend on how AI is used. “It’s not that AI is bad for the brain or good for the brain. It’s all about how it’s being used.”

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Meaning over capacity: what humans still do better

In a final experiment, the audience memorised a sequence of twelve digits, then a sequence of twelve letters. Most recalled only a few digits, but nearly everyone recalled the twelve letters, because they formed three words.

For an AI system, both sequences contain the same amount of information. For humans, meaning turns twelve items into three. “There’s no question, AI beats us on capacity. But for humans, meaning is important.” The hippocampus builds meaning into our semantic memory, which also lets us infer, fill gaps in incomplete information and read the room: noticing hesitation in a colleague’s voice or a reaction during a meeting. “You are in the room. AI is not.”

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How leaders can keep their brains strong

If judgment depends on the brain, its health becomes a leadership concern. Collins pointed to three habits supported by research:

• Nutrition. Omega-3 fatty acids and antioxidants give brain cells the infrastructure they need, and compounds found in foods such as blueberries are particularly beneficial for the hippocampus.
• Continuous learning, which physically strengthens the brain, including the corpus callosum that connects its two hemispheres.
• Social interaction. Contrary to the narrative of cognitive decline, social information processing improves with age, and it underpins the skills leaders rely on most: reading the room, anticipating a conversation and building relationships.

She also looked further ahead, to “mini brains”: small clusters of brain cells grown from adult stem cells that can communicate when connected by electrodes. Early data centres are already placing living brain cells on microchips, and this “biological intelligence” has so far proved more stable than conventional AI infrastructure, with a much smaller carbon footprint. “We’re just at the tip of the iceberg.”

What this means for asset and wealth managers

At MDOTM, we build AI to support investment professionals, not to replace their judgment. Collins’ framework translates directly into how investment teams can work with AI:

• Decide what stays human. In a regulated business, which decisions remain with people is a design choice, not an afterthought.
• Build in the pause. For high-stakes calls, slow down and challenge the output before acting on it.
• Calibrate your confidence. Know what the model tells you, what you know, and where you are guessing.

AI can read the data. It cannot read the room, build an emotional bond with a client, or recall a conversation from weeks ago that changes today’s decision. In an economy based on judgment, those are the differentiators.

Collins closed with this thought:

Better brains build better leaders, and better leaders build better worlds.

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Frequently asked questions

What is human judgment in the age of AI?
It is the ability to interpret AI outputs in context, weigh risk and take responsibility for a decision. As AI makes knowledge abundant, this becomes the scarce skill.

What is fluency bias, and why does it matter when using AI?
Fluency bias is the tendency to believe information that arrives quickly and smoothly. AI generates fluent answers fast, which can create an illusion of accuracy, so for high-stakes decisions it pays to pause and verify.

What is the difference between “brain drain” and “brain gain” with AI?
Brain drain happens when people offload their thinking to AI and trust unverified answers. Brain gain happens when AI amplifies critical thinking, through the steps Construct, Challenge and Calibrate.

How can investment professionals use AI without losing critical thinking?
Define the problem before prompting, ask the AI to challenge your assumptions, regularly check how confident you are, and decide in advance which decisions must stay human.

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