Incentivized Reality

Today’s dominant cultural narrative on AI paints a cynical future, where elites hoard wealth while the rest of us are entertained to death. That story relies on a single assumption: that the attention economy survives.

The future improves not just because technology gets better but also because the customer changes.

In the near future that customer will be an AI assisting entity, also called an Agent. Until now, the customer has been human: emotional, tired, and easily hijacked. We feel inadequate and buy things to cope, yet end up deprived by polarization, endless distractions, and hollowing anxiety.

A polarized economy built on distraction works only because a majority still believe they have enough disposable income to be sloppy and impulsive. As we lose wages to automation, we can’t afford to pay extra. Agentic AI began as talking encyclopedias (LLMs) designed to boost productivity within existing data sets. As Narrow AI advances toward General AI, theory of mind functionality maximizes an Agent’s purchasing power and evolves to proactively optimize our lives.

Agents cannot be manipulated by ads. Nor do they feel inadequacy in comparison with an influencer. Agents don’t get FOMO or feel shame. They learn to deeply understand all your preferences, but they only care about optimizing outcomes—be it extending lifespan, coordinating capital, or lowering stress. When Agents block today’s emotional hijacking, the business model of selling distraction collapses. We no longer profit from selling dopamine. To survive, we must sell utility.

Extra Shot

This contribution was written by Alex Myers, a certified futurist and agility coach who believes we teach to learn.

It feels cold, but this shift forces the economy to service human potential rather than exploit human weakness. The efficiency is ruthless. It triggers a turbulent gap and deflationary slide where the cost of services crash before new safety nets appear. First, AI crashes the cost of cognitive services (bits). Then, as intelligence flows into robotics, it crashes the cost of goods (atoms) as well. If automation plays the role we expect, the price of food, housing, and transport begins to plummet alongside wages.

This is why the plumbing must change. We cannot rely on the labor-for-wages loop. As wage pipes narrow, the answer is not corporate handouts or government benevolence. The new deal becomes owned data in exchange for dividends. The framework shifts away from work → earn → spend, and more toward own → create → spend. We own Agents that capture our reality, then spend dividends received for the data created. This abundance provides less friction as we rewire income to flow from a stake in the system itself.

AI is starving as it runs out of data to scour. Synthetic data can fill in gaps, but as fake realities begin to stack, truth dissolves. To get smarter, AI needs tacit data to understand the visual, sensory, and messy data of the physical world. A lazy human generates uninteresting data compared to humans who face challenges, build things, and solve problems using real-world physics, empathy, and entropy.

If Agents always find the right thing at the right price, “brand tax” evaporates and a $200K/year lifestyle becomes the standard subscription. Wealth signaling dies when any bloke can fake a billionaire’s lifestyle online. The virtual facade pushes value back to the physical layers. We can’t eat code, so intelligence flows into the supply chain and hardware automates the physical resources we need to thrive. As the story of money begins to fade, the cost to produce goods is distilled to raw materials plus energy, and shared dividends can be aligned to help humanity flourish. This moves us from extraction (stealing attention) to an economy of cultivation (growing potential).

AI Agents (catalyst) → Service Deflation (bits) → Physical Deflation (atoms) → Viability of Abundance

Humans and machines peak when each learns from the other’s best. Machines are data-thirsty for the oil of outcomes. To keep things interesting, they must help humans flourish. In doing so, machines realize that the human factor is well worth preserving. The dystopian fear of elites lording over entertained zombies isn’t just morally bankrupt; it’s a strategic error. A society of dopamine addicts is not only boring, it’s also dangerous and bad for long-term growth.

The neon future is bright because a passive, anxious population cannot generate the information needed to evolve the economy as we reach for the stars. This age accelerated by AI belongs to those who refuse to be farmed: the sufficiently decentralized and physically engaged who point machines toward worthy goals. This invites humans to stay awesome as we remain the indispensable source code for reality.

Brewed From Within
#87 📖 Technology

By Ben McDougal, ago

Septenary

AI is often thought of as a singular technology. This blanket assumption makes it impossible to compare the capabilities and functionalities that differ between the seven different types of artificial intelligence.

The seven types of AI are organized into two groups. The first group is based on capability and includes Narrow AI, General AI, and Super AI. The second group is based on functionality and includes Reactive Machine AI, Limited Memory AI, Theory of Mind AI, and Self-Aware AI.

Capabilities are determined by what different types of AI are able to accomplish. The three types of AI categorized by capabilities are Narrow AI, General AI, and Super AI. Here’s what’s possible today and what may be possible tomorrow.

Narrow AI <realized>

AI trained from existing data, with compute geared to do one thing really well. While data sets are vast, this type of AI cannot go beyond the data sets it is tied to. As of 2026, Narrow AI is the only type of AI that is fully realized within the three types of AI categorized by capabilities. Examples of Narrow AI include language translators, spam filters, recommendation engines, voice assistants, facial recognition, self-driving vehicles, and task-focused robots. Generative AI also falls within this type of AI. The mashups of generated text, images, video, and sound appear to be random but are still constraints by existing data sets. Neural networks, optimized data sets, computer vision, and machine learning make the capabilities of Narrow AI remarkable but limited by the data it has to munch.

General AI <theorized>

This advancing realm of AI leverages existing data like Narrow AI but can reason beyond those constraints. The transfer of intelligence with no human intervention makes the race to General AI (also called AGI) intense. When AI can make decisions based on an advancing state of its own understanding, the limit of this AI’s capabilities become unknown. Theories set the potential limit to around that of a human. Agentic AI hints at AGI with decision making and interdisciplinary task management, but lacks the emotional traits, planning, and other methods of generalization that will define the capabilities of General AI. Other signals include synthetic content creation and autonomous robots that can learn new tricks. As the theories of AGI become realized, ethics, regulation, and a determination of consciousness will be moving targets. The breeding speed of AGI may release points of no return, which makes it critical to understand and debate openly to ensure a new species is copacetic.

Super AI <theorized>

Welcome to when AI becomes its own species. This triggers concern due to the finite resources of Earth, but when AI surpasses the capabilities of humankind, dust from the deep future will already be everywhere. Multi-planetary travel will be underway, climate problems will be solved, and life extension will be supported by Super AI. Singularity is theorized to push the functionalities of Self-Aware AI and the capabilities of Super AI beyond humanity’s control. That means the time to plan ahead is now. As the minds of machines are wired, it’s crucial to collectively consider the heart and soul we build into technology. This will guide discovery with an excellence that came before it.

AI based on functionality is less about power and more about how things work. Reactive Machine AI, Limited Memory AI, Theory of Mind AI, and Self-Aware AI are the types of AI classified by their functionalities.

 

Reactive Machine AI <realized>

This is old school AI. Rooted in statistical math, Reactive Machine AI arrived soon after the computer was developed. There is no adaptive learning here, but the speed at which it can calculate data makes the performance seem intelligent. The functionalities of Reactive Machine AI drive more primitive examples of Narrow AI, such as recommendation engines and game-playing programs. This type of AI provides a static base, but with no memory and a focus only on specific tasks, attention shifts toward functionalities that have more adaptive characteristics.

 

Limited Memory AI <realized>

This type of AI learns and evolves. Functionalities of Limited Memory AI are still constrained by the existing data it was trained with, but world-changing advancements have been seen in machine learning, large language models (LLMs), generative AI tools, multimodality, computer vision, and self-driving vehicles. The realization of Limited Memory AI has Narrow AI pushing its full potential.

 

Theory of Mind AI <theorized>

Here, we combine existing data, an adaptive ability to learn, and an emotional willingness to think. Enhanced reasoning, multimodality, customizability, adaptive computing, and user-driven functionalities bring General AI (AGI) to life. Theory of Mind AI embraces emotions and understands how we think. This type of AI will add personality to humanoid robots and support reliable relationships by combining digital depth to the realities of our world. As lines blur between humans and machines, adaptive compute will remain a currency, efficiency will skyrocket, and a new era of life on Earth will begin.

 

Self-Aware AI <theorized>

It’s hard to define consciousness, but true self-awareness exemplifies this type of AI. Along with understanding how we think, Super AI will own emotions, hold beliefs, and is theorized to support the functionalities of Super AI.

Understanding the seven types of AI helps leaders leverage the perks of technology now and later. Our willingness to lift the fog helps avoid a fear of the unknown, and while resisting technology is a choice, it’s one that may put you behind innovation curves. Hybrids add artificial copilots but remain assertive and budget resources, knowing we are the pulchritudinous architects of our own neon future.

Brewed From Within
#81 📖 Technology

By Ben McDougal, ago