By the end of its first full year, Medvi had posted $401 million in sales, served 250,000 customers, and produced a 16.2% net profit margin, nearly triple the margin of Hims & Hers, which employed 2,442 people. Sam Altman’s prediction that AI would produce a one-person billion-dollar company took eighteen months to prove true.

But before we canonise Medvi as the AI marketing gospel, something the headlines missed matters enormously for anyone building a real, durable business. We will get to that. First, the structural picture.

The Great Marketing Reset: What Has Fundamentally Changed

The Medvi story is a data point. What it points to is something that is more of a structural reset of the foundational economics of marketing that has been building for three years and has now arrived all at once.

For the previous thirty years, marketing operated on a stable set of assumptions. 

• Scale required headcount. 
• Reach required budget. 
• Creative quality required agencies. 
• Distribution required relationships. 

Every one of those assumptions was, at some level, a cost barrier and cost barriers are also moats. The company with more people, 

What Nobody Is Telling You About AI and Marketing in 2026

There is a system most marketing organisations have built over the past thirty years that nobody talks about directly, because it is too embedded in how things work to be seen clearly from the inside.

The system is built on a set of assumptions that were entirely reasonable when they were formed. 

• That producing marketing content at scale requires large teams. 
• That reaching a national audience requires substantial budget and agency relationships. 
• That testing creative is an expensive, slow process reserved for major campaigns. 
• That search visibility is a long-term project requiring months of technical work and ongoing investment. 
• That personalising customer communications at scale requires enterprise software and dedicated operations staff.

These assumptions were not wrong. They were accurate descriptions of the cost structure of marketing as it existed. And like all cost structures, they produced an organisational architecture designed to manage them. 

• Teams to handle production. 
• Agencies to handle reach. 
• Budget cycles to govern spending. 
• Approval processes to protect quality. 
• Org charts to coordinate the complexity.

The system worked. For decades, it worked well.

Then the cost structure changed. Not gradually. Not in one area. All at once, across every function that the system had been built to manage.

In 1995, a business owner who wanted to run a national advertising campaign needed a minimum budget of $250,000, an agency, a media buyer, a production team, and publisher relationships that took years to build. The barrier was structural. It was not laziness or lack of ambition that kept most businesses from competing at that level. It was the genuine cost of the infrastructure required.

In 2026, the same reach is available for under $500 a month. Not similar reach. The same reach. Often better targeting. Often faster creative iteration. Often higher margin.

“The danger is not that you have the wrong tools. The danger is that you have built the right organisation for a cost structure that has been retired.”

What the Data Actually Shows

84% of marketing teams are now using AI in at least one workflow. That number sounds like a transformation. 

Then you read the next one: only 17% of those professionals have received comprehensive AI training. The tools have been adopted. The thinking has not changed. The system persists inside a new interface.

Here is the number that should stop everyone in the room: AI-referred web sessions grew 527% year-over-year in 2025. Not 5%. Not 52%. Five hundred and twenty-seven percent. 

The fastest-growing source of web traffic is now AI answer engines:

ChatGPT, Perplexity, Google AI Mode, Claude and fewer than 40% of brands are doing anything to appear in those answers. The rest are investing in search optimisation for a landscape that no longer describes how the majority of information discovery happens.

And from the state of the global workforce: 21% of employees are genuinely engaged in their work. That is not an HR problem. It is a meaning problem. And it costs the global economy $8.9 trillion every year. The teams that will win in this era are not the ones who use AI to move faster inside the old system. They are the ones who use AI to ask what the system should actually be for.

Three Things Most Practitioners Have Not Been Told

First: AI has made authentic human perspective more scarce, not less relevant. 

The explosion of AI-generated content has flooded every channel simultaneously. Almost everything now looks polished, sounds confident, and is forgettable. The content that earns genuine attention that stops the scroll, earns the share, builds the subscriber is the content that could only have come from a specific human with specific experience. The irony of the AI era is that it has created the scarcest thing in the market: genuine, unreproducible point of view.

Second: the biggest AI marketing opportunity is not at the top of the funnel. 

Most conversations about AI marketing are conversations about content production. But the measurable returns from AI are largest inside the funnel: in lead scoring that improves qualification rates by 60%, in onboarding sequences that double Day-30 retention without changing the product, in churn prediction models that identify at-risk customers four weeks before they cancel, in email send-time optimisation that lifts open rates by 35% without a single new word being written. The content story is the visible story. The funnel story is where the money is.

Third: the search game changed while most marketing departments were looking the other way. 

55% of all Google searches now show an AI Overview. These systems do not return a list of blue links. They synthesise an answer and cite sources. 

The brands that appear are the ones with original data, clear structure, and genuine domain authority. 

The brands that do not appear are invisible to the fastest-growing traffic source in the ecosystem. Most of them have not noticed yet because their traditional SEO rankings have not changed. Visibility and traffic have been quietly decoupled.

What This Playbook Is Built to Do

This playbook does not argue that AI will replace marketers. It argues something more specific and more uncomfortable: that marketers who understand what AI is actually for, at each stage of the funnel, in the right sequence, with the right guardrails will produce outcomes that those who do not cannot match. Not because they are smarter. Because they are working with the grain of how the cost structure has changed, rather than against it.

Each of the twelve chapters/sections that follow covers one stage of the marketing system. Each is anchored by a lead expert chosen for their usefulness at that specific stage, a data chart that makes the argument visible, a real tactical example from an operator who has done the work, and live citations to the research behind the numbers.

The sequence is the argument. 

1. Demand intelligence before content creation. 
2. Visibility before distribution. 
3. Workflow before revenue. 
4. Onboarding before retention. 
5. Measurement throughout. 

Most AI marketing advice presents these as parallel options you can adopt in any order. They are not. They are a system. A weakness at any stage compounds downstream. The organisations that understand this are building something durable. 

The ones that do not are using new tools to run an old system faster.

The System at a Glance

Before Chapter One, here is the complete map. Eight stages. One system. Each one built on what came before it.

The stack is the infrastructure. The moat is what you build with the time the stack gives back to you. Everything that follows is about building the right moat, at the right stage, in the right order.

CHAPTER 01

AI Has Changed the Shape of Marketing

The most important mental model shift of this era is also the simplest: AI is not a tool. It is a new operating layer that sits underneath every function in a modern marketing organisation. Teams that treat it as a productivity add-on will continue to operate on the same model as before, only faster. Teams that understand what has structurally changed will build a different kind of system entirely.

The Three Structural Shifts

First: the marginal cost of content has fallen toward zero. A marketing team that could produce twelve pieces of high-quality content per month in 2021 can now produce sixty or more with the same headcount. The constraint has moved from production capacity to audience attention.

Second: the cost of iteration in paid creative has collapsed. An AI-equipped operator can now generate, test, and iterate on thirty creative variants in the time it used to take to produce three. You no longer need to guess which message or visual resonates.

Third: the search landscape has been restructured from below. AI-referred web sessions grew 527% year-over-year in 2025. The question is no longer just “do I rank on page one of Google?” It is “am I the source that AI systems cite when someone asks the question my content answers?”

“Stop thinking about AI as a tool. Start thinking about it as part of the operating system of modern growth.”

CHAPTER 02

Start With Demand, Not Tools

Rand Fishkin built his reputation by telling marketers things they did not want to hear. His core argument is that most marketing investment is wasted not because of poor execution but because of poor demand intelligence. Teams build content before understanding what their audiences actually care about. They target keywords before verifying that real intent exists behind them.

AI makes this problem worse before it makes it better. A team with strong demand intelligence can use AI to execute faster and at greater scale. A team with weak demand intelligence can now produce AI-generated content, AI-distributed posts, and AI-personalised emails at ten times the volume — pointed at the wrong audience, in the wrong channel, with the wrong message. At ten times the speed.

The Demand-First Framework

• Map real attention. Before creating anything, understand where your audience actually spends time. SparkToro’s audience research tools reveal the publications, podcasts, and social accounts that your specific buyers actually consume.
• Identify buyer language, not marketer language. The words your buyers use to describe their problems are almost never the words your product team uses to describe their solutions. Ground your content in the actual language of your audience before generating anything at scale.
• Verify category momentum before investing. Producing excellent content in a declining category is a losing investment regardless of quality. Confirm that real buying momentum exists before building.
• Find the trust signals your audience relies on. Identifying which voices, publications, and communities carry authority with your specific audience is the demand intelligence that most AI tools cannot provide — and most teams never gather.

CHAPTER 03

Visibility Is the New Traffic

For two decades, SEO was fundamentally about earning clicks. Rank high, earn a click, bring someone to your site. AI answer engines change that model entirely. When someone asks ChatGPT or Perplexity a question, they receive a synthesised answer — and may never click through to any source at all. Visibility and traffic have been decoupled.

From SEO to GEO: The New Rules of Discoverability

• Original data and research. AI engines are trained to prioritise sources that contain information not available elsewhere. Original surveys, proprietary analyses, and first-party research are the highest-value GEO assets a brand can produce.
• Citability structure. Content must be written so AI systems can extract specific claims, statistics, and answers. Clear headers, short paragraphs, specific assertions, and attributed data all improve citability.
• GEO monitoring. Run your brand name and five core topics through ChatGPT, Perplexity, and Google AI Mode monthly. The gap between what appears and what should appear is your content brief.

CHAPTER 04

Build a Content Engine, Not a Prompt Habit

Ross Simmonds has built his consultancy around one core idea: the best content marketing is not about producing more content. It is about producing content worth distributing. His phrase “create once, distribute forever” captures the system that AI makes newly possible at scale.

The failure mode he sees repeatedly is the “prompt habit”: marketers who use AI to generate individual pieces of content on demand, with no underlying editorial system, no brand voice consistency, and no distribution strategy. The output is fast. The output is plausible. The output is forgettable.

The Content Engine: Three Layers

• The idea layer. Strong content begins with a non-obvious insight that could only come from this brand. AI cannot generate this. It can help develop it once a human has identified it.
• The production layer. Once the core idea exists, AI handles the mechanical work: researching data, drafting the long-form piece, extracting five LinkedIn post angles, writing the newsletter section, scripting the short-form video. One idea becomes seven assets.
• The distribution layer. Content that is not distributed is invisible. Distribution is not an afterthought. It is what makes the production investment worthwhile.

CHAPTER 05

Win Attention Where People Actually Are

Gary Vaynerchuk’s core insight — repeated across a decade of content — is that attention has always been the precondition for everything else in marketing, and that most brands are perpetually late to the channels where attention actually lives.

In 2026: TikTok still offers the largest organic reach opportunity for new entrants. LinkedIn personal profiles offer the highest-quality organic reach for B2B operators. YouTube offers the longest compounding return on investment. Cross-posting content built for one platform into all of them is not a distribution strategy. It is the fastest way to train every algorithm to suppress your content.

Platform-Native Rules

• TikTok (3.70% engagement, +200% brand follower growth): Entertainment first. Hook in 2 seconds. 52% video completion is the benchmark. TikTok Search rivals Google for under-30 product research.
• LinkedIn personal profiles (20–30% organic reach): The last major platform where a human with genuine expertise reaches a large percentage of their network without paid amplification. No external links in post bodies. Company pages reach only 2% of feeds.
• YouTube (the compounding channel): Content created today still drives traffic in five years. Treat it as a search engine. Keyword-first titles. Split-test thumbnails before anything else.
• Instagram (0.48% engagement, -24% YoY): 60–70% Reels for discovery. 20–30% Carousels for saves. No TikTok watermarks.

CHAPTER 06

Turn AI Into a Workflow Advantage

Kieran Flanagan’s argument is that the teams winning with AI are not the ones with the best individual tools. They are the ones who have redesigned their workflows around AI from first principles — identifying every point where a human was doing a task that AI could do as well or better, and systematically removing that friction.

The most common mistake: “spot automation” — using AI to replace individual tasks in an otherwise unchanged workflow. The result is a system that is faster in isolated moments but still fundamentally broken. The teams that win redesign the entire workflow, not just the individual steps.

Workflow Redesign Areas

• Content production pipeline: Research → brief → draft → GEO-optimise → repurpose → schedule → publish. Each step AI-assisted. The human role is editorial judgment at the brief and review stages, not execution.
• Lead qualification and routing: AI scores inbound leads against ICP criteria, enriches CRM records with intent data, and routes leads to the appropriate sales motion before any human touches the record.
• Campaign briefing and variant generation: AI generates the brief, writes copy variants, produces creative options, and recommends the test structure. The execution cycle shortens from weeks to days.

CHAPTER 07

Connect Marketing to Revenue

AI does not automatically close the gap between marketing activity and revenue outcome. In many cases, AI-powered marketing creates a new version of the same problem: faster content production and wider distribution, but no improvement in the quality of leads that actually convert. The work of connecting marketing to revenue is a systems problem, not a content problem.

The Revenue Connection Framework

• Lead scoring with intent data. AI combines behavioural signals with firmographic data to score leads against ICP criteria in real time. This replaces manual qualification and eliminates the “warm body” problem: leads sent to sales before they are ready to buy.
• CRM enrichment and handoff quality. AI enriches CRM records with third-party intent data, competitive research, and engagement history — giving sales reps context they could not have gathered manually.
• Conversion architecture. The conversion path should be designed as a system, not assembled from individual campaigns. AI personalises that path based on the visitor’s industry, role, behaviour, and stage.
• Revenue-linked measurement. Every marketing KPI should be traceable to a revenue outcome. AI-powered attribution is making this more achievable, but the discipline remains a human responsibility.

CHAPTER 08

Onboarding Is Part of Marketing

The promise made in an ad, an article, or a sales call must be fulfilled in the first product experience. If it is not, the acquisition cost was wasted. For digital products, the onboarding experience is the moment of truth.

AI-Powered Onboarding Principles

• Personalise the path to first value. AI segments users at signup based on role, industry, intent signals, and stated goals — and serves a personalised activation sequence for each.
• Reduce time to first value. The single most important metric in onboarding is time to first value. AI removes friction by pre-filling information, suggesting next steps, and surfacing contextual help at the right moment.
• Use email as an onboarding channel. The welcome sequence — a minimum of five emails triggered by signup and activation milestones — should be AI-personalised based on what the user has and has not done.

CHAPTER 09

Retention Is the Real Test

Retention is where the promises made in every earlier stage of the funnel are tested. The true proof of modern marketing is not how fast you acquire customers, but how well you keep and grow them. AI changes the economics of retention in two ways: faster identification of at-risk customers, and more personalised retention interventions.

The Retention Framework

• Churn signal detection. AI models can identify at-risk customers weeks before they cancel, based on changes in login frequency, feature usage, support ticket patterns, and engagement. Early detection gives the retention team a window to intervene before the decision is made.
• Lifecycle messaging. The lifecycle email sequence — triggered by usage milestones, inactivity thresholds, and renewal dates — is the primary retention communication channel. AI personalises this based on each customer’s actual usage patterns.
• Expansion revenue. AI identifies customers showing usage patterns consistent with readiness for a higher tier or additional seat and triggers the appropriate outreach before the customer has actively considered upgrading.

CHAPTER 10

Measure Signal, Not Activity

Christopher Penn’s central argument: most marketing analytics is measuring the wrong things — tracking activity that is easy to count rather than signals that actually predict revenue. The arrival of AI has made this problem worse in a specific way: AI can now generate more reports, more dashboards, and more data visualisations than any team can possibly act on.

The Signal Framework

• Define the outcome first. Before choosing what to measure, define what success looks like in revenue terms. Metrics that cannot be connected to revenue are vanity metrics, regardless of how impressive they look in a dashboard.
• Identify leading indicators. Lagging indicators tell you what happened. Leading indicators tell you what is about to happen. AI is most useful for identifying which leading indicators actually predict lagging outcomes — a correlation analysis most teams have never run.
• Design experiments, not campaigns. Every campaign is a hypothesis, every outcome is data, and every iteration improves the model. AI accelerates the experimentation cycle but cannot replace the discipline of defining the hypothesis before running the test.

CHAPTER 11

Tool Stacks by Stage

The right tool is always the simplest tool that does the required job at the current stage. A solo creator building their first email list does not need HubSpot Breeze. A growth-stage SaaS company managing 50,000 contacts does not need to be on ConvertKit. The tools below are sequenced by stage of growth and matched to documented operator results, not press releases.

CHAPTER 12

The New Marketing Moat

Every industry transformation produces two kinds of operators: those who see the structural shift clearly enough to reorganise around it, and those who add the new technology to the old model and wonder why the results are underwhelming.

The organisations building durable marketing advantages in 2026 are not the ones with the most sophisticated tool stacks. They are the ones who understood, early enough to act on it, that AI changes the cost of execution — not the value of genuine expertise, not the power of authentic audience relationships, and not the irreplaceable quality of a human perspective that makes someone trust a voice enough to follow it.

The Four Moats That Survive the AI Era

• Audience relationships. A list of 30,000 email subscribers with 40% open rates cannot be reproduced by a competitor with a better AI stack. No tool can generate it. It can only be earned.
• Original data and research. Any AI can synthesise existing public information. No AI can produce data that does not exist yet. Operators who generate original research have a GEO moat that no competitor can buy.
• Genuine domain expertise. The operator who has genuinely done the thing has a signal that AI cannot fake and that the most sophisticated algorithms are specifically designed to detect.
• Speed of learning. The organisations that compound fastest are not those with the most tools but those with the most sophisticated feedback loops. Strategic intelligence remains exclusively human.

“The stack is the infrastructure. The moat is what you build with the time the stack gives back to you.”

The post This $401 Million Company Built by Two People Reveals the New Rules of AI Powered Marketing appeared first on jeffbullas.com.

It has sold you habit trackers, morning routines, Pomodoro timers, dopamine fasts, cold plunge challenges, and elaborate systems for manufacturing urgency in a life that doesn’t feel urgent enough.

The pitch is always the same: you are not doing enough, moving fast enough, or wanting it badly enough — and for a monthly subscription fee, we can fix that.

Here is what the $80 billion industry does not want you to know.

The most powerful creative fuel in human history costs nothing, requires no app, and has been sitting inside you since the day you were born.

It is the knowledge that you are going to die.

Over 500 published studies across 40 countries confirm it: reminding people they are mortal consistently makes them more creative, more purposeful, and more deeply invested in the work that matters.

This is not philosophy. It is not self-help. It is one of the most replicated findings in the history of psychology — and almost nobody in the productivity world is talking about it.

The Research That Changes Everything

In 1973, cultural anthropologist Ernest Becker published a book called The Denial of Death. His central argument was radical for the time.

He said that almost everything humans have ever built — art, religion, cities, philosophies, love affairs, career ambitions, the need for legacy — is, at its root, a response to one fact: we know we are going to die.

Death awareness, Becker argued, does not paralyse us. It powers us. Finitude is not the enemy of human creativity. It is the engine.

Three researchers — Jeff Greenberg, Tom Pyszczynski, and Sheldon Solomon — spent the next four decades testing this idea in controlled experiments. They called it Terror Management Theory.

The methodology was straightforward. Take two groups of people. Ask one group to think about their own death — to write a short paragraph about what will happen to their body when they die and what the experience of dying will feel like. Ask the control group to think about something neutral, like a dental procedure.

Then measure what happens to both groups’ behaviour.

The results, replicated across 500+ studies in 40+ countries, were consistent and striking.

What the numbers show

Creative output rose by 38% on average in the mortality-reminded group. Meaning-seeking behaviour rose by 42%. The drive to leave a lasting legacy — to make something that outlives you — rose by 45%. Investment in relationships deepened. Curiosity about life increased.

And these were not small laboratory effects. They have been replicated across cultures, age groups, languages, and continents.

This is not a Western cultural artefact. This is something about the structure of human motivation itself.

History Already Knew This — We Just Didn’t Have the Data

Look back at the periods in human history when creative output exploded — when art, philosophy, science, and literature all surged forward at the same time.

They are almost always periods when death was close.

Athens’ golden age of philosophy, drama, and architecture unfolded in the shadow of the Persian Wars and recurring plague. Thucydides wrote the first work of modern historical analysis while living through a pandemic that killed a third of the city.

The Italian Renaissance — one of the greatest explosions of art and ideas in recorded history — followed the Black Death, which had killed half the population of Europe. Historians of culture have long noted the connection, though they struggled to explain it. The TMT research explains it.

The post-World War II art boom. The Elizabethan literary explosion. The Romantic movement, written against a backdrop of Napoleonic Wars and cholera outbreaks. In each case, the proximity of death did not suppress human creative output. It ignited it.

The productivity industry sells you urgency. History shows that the deepest urgency was always already there. You just have to let yourself feel it.

Why Death Makes You More Creative: The Psychology

The mechanism, once you understand it, is straightforward.

Most of us live what psychologists call a proximal defence — we push the awareness of death to the back of our minds and get on with daily life. Deadlines feel urgent. Social media metrics feel important. The approval of colleagues feels like it matters.

When mortality awareness breaks through — either through a health scare, the death of someone close, or a deliberate reflective practice — something shifts in the brain’s priority system.

Suddenly, the question is not “what will people think of this?” but “does this actually matter?”

The trivial falls away. The meaningful rises. The work you have been procrastinating on for two years — the book, the business, the creative project, the difficult conversation — stops feeling optional.

The attention filter resets

Neuroscientist Karl Friston’s work on how the brain allocates attention helps explain the mechanism. The brain is a prediction machine that constantly weighs what to pay attention to based on what matters for survival. When mortality becomes salient, the weighting changes. Low-stakes social concerns — looking good, being liked, avoiding embarrassment — lose their urgency relative to higher-order concerns: meaning, connection, legacy, contribution.

This is why people who survive serious illness routinely report that their creative output and sense of purpose intensified afterwards. It is not resilience in the conventional sense. It is a recalibration of what the brain treats as important.

The sycophancy trap: why comfort kills creativity

There is a direct parallel here to one of the most documented problems in how people use AI.

Research from Anthropic and others has shown that AI systems default to agreeing with users — validating assumptions, reinforcing existing beliefs, and avoiding challenge. This is called sycophancy, and it is the opposite of what mortality awareness does to a human mind.

Mortality awareness removes the social cushion. It makes you less interested in approval and more interested in truth. It is, in effect, the anti-sycophancy mechanism built into the human brain.

The implication for anyone using AI to support their creative work: you need to deliberately override the default. Ask AI to challenge you, not agree with you. Use it as a sparring partner, not a cheerleader. The best work comes from the version of you that doesn’t need validation — and that version is activated, research shows, by contact with your own finitude.

How to Actually Use This: 5 Practical Applications

The research does not require a dramatic near-death experience. The studies show that even a brief, deliberate engagement with mortality — a few minutes of honest reflection — is enough to shift creative behaviour in measurable ways.

Here is what that looks like in practice.

1. The “one year left” filter

Ask yourself: if I had one year left to work, what would I still be doing? What would I stop immediately? Most people know the answer within thirty seconds. The question cuts through the noise that daily life generates. Use it as a weekly filter for your project list, not a dramatic life exercise.

2. Write your obituary — professionally

Not a morbid exercise. A focusing one. Write the three-sentence professional legacy you want to leave. What did you build? What did it do for people? What would be missing from the world if you hadn’t made it? The gap between that paragraph and your current project list is the most useful creative direction signal you can generate.

3. Create for someone specific who will outlive you

TMT research shows that legacy-oriented creation is one of the primary drivers of meaning. Write or build for a specific person who will still be alive in twenty years. A child. A student. A reader you haven’t met yet. This reorients the creative act from performance for current approval to contribution across time.

4. Ask the question that matters

Before starting any significant piece of work, ask one question: does this matter enough to spend finite time on? Not “is this good?” Not “will this perform?” Does it matter? The mortality-aware brain processes this question differently from the comfort-seeking brain. It gives a cleaner answer.

5. Use AI as your challenge, not your comfort

Given what the research shows about mortality awareness stripping away the need for approval, design your AI interactions accordingly. Tell it explicitly: do not agree with me. Tell me what is wrong with this. What am I avoiding? What would a sceptical reader say? Use it to simulate the productive discomfort that mortality awareness naturally generates.

What AI Reveals About This — And What It Can’t Touch

AI has now taken over the cognitive tasks that productivity culture told you were your most valuable assets: reasoning, synthesis, analysis, fast output.

And it turns out — as the TMT research has been quietly showing for four decades — that those were never the source of your most important creative work anyway.

Your most important creative work comes from the place that AI cannot access.

It comes from your history of loss and recovery. From the version of you that knows the clock is running. From the work you would still make even if no algorithm rewarded it, because it matters to you in a way that transcends metrics.

AI is, in this sense, an extraordinarily useful mirror. By doing the productivity work fluently and cheaply, it forces the question: what is left that only you can do?

The answer, the research suggests, is the work that comes from your awareness that this ends.

That is not a threat. That is the most creative brief you have ever been given.

The Verdict

The productivity industry has spent decades selling you artificial urgency. Timers, streaks, accountability partners, and morning rituals designed to make you feel the pressure of a deadline that isn’t real.

The research is clear: the deepest urgency is already inside you. It does not need to be manufactured. It needs to be acknowledged.

Over 500 studies confirm that people who allow themselves to feel the reality of their finitude — not as a source of dread, but as a fact of their situation — produce more creative work, invest more meaningfully in their relationships, and build things that last longer and matter more.

You are going to die. The clock is running right now, as you read this.

That is not a problem to be managed. That is the whole point.

Research references

• Greenberg, J., Pyszczynski, T. & Solomon, S. (1986). The causes and consequences of a need for self-esteem: A terror management theory. APA PsycNet.
• Becker, E. (1973). The Denial of Death. Free Press. (Pulitzer Prize, 1974)
• Burke, B.L., Martens, A. & Faucher, E.H. (2010). Two decades of Terror Management Theory. Personality and Social Psychology Review, 14(2), 155–195.
• Simonton, D.K. (1988). Scientific Genius: A Psychology of Science. Cambridge University Press.
• Murray, C. (2003). Human Accomplishment: The Pursuit of Excellence in the Arts and Sciences, 800 BC to 1950. HarperCollins.
• Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11, 127–138.
• Anthropic (2024). Sycophancy research. arXiv:2310.13548.

The post Knowing You Will Die Makes You More Creative appeared first on jeffbullas.com.

Why this matters 

The result is the largest qualitative research study ever conducted. And buried inside it at number two on the list of what people most want from AI is something that tells us far more about the human condition than about the technology itself. And that was Personal transformation. That is 13.7% of 81,000 people. 

When given the space to speak freely and honestly, said the thing they most wanted from AI was help becoming a better version of themselves. 

I am also sure that you are curious about what was number one? 

Professional excellence. Predictable. 

We’ve watched AI reshape the workplace for two years and that connection makes immediate sense.

What the research and numbers revealed

Looking behind the productivity goal was what that time unlocked enabled them to do and that was not only personal transformation but the following:

• Life management at #3. 
• Time freedom at #4.
• Financial independence at #5
• Societal transformation next
• Then Entrepreneurship
• Followed by learning and growth 
• Finally it was “Creative expression”

“AI modeled emotional intelligence for me… I could use those behaviours with humans and become a better person.”
— Respondent, Hungary

The question this raises isn’t whether AI can support personal transformation. The research suggests it already is. The question is: are you using it that way? And if not — why not, and how do you start?

Why This Finding Is More Important Than It Looks

Anthropic’s researchers noticed something remarkable when they dug deeper into the interview transcripts. Many people began the conversation talking about productivity. Automating emails. Clearing cognitive load. Finishing the report faster. But when the AI interviewer asked a simple follow-up — what does achieving that actually enable for you? — the real answer surfaced.

A Colombian worker: “With AI I can be more efficient at work… last Tuesday it allowed me to cook with my mother instead of finishing tasks.”

A Japanese freelancer: “I want to use less brain power on client problems… have time to read more books.”

What is insightful from this is that productivity was never the destination. It was the door. Presence. Connection. Growth. Becoming someone. That was the destination all along.

This matters because most people use AI as a productivity tool and they stop there. They get the door but never walk through it. The 13.7% who explicitly named personal transformation as their primary desire from AI aren’t more sophisticated users. They’ve simply made a different intention explicit. And intention, it turns out, is where personal transformation begins.

What Personal Transformation Actually Means and Why AI Is Unusually Good at It

The study also broke personal transformation into several sub-categories: 

1. Cognitive partnership and collaboration (24%), 
2. Mental health support (21%), 
3. Physical health improvement (8%), 
4. And even romantic companionship (5%). 

But there’s a unifying thread running through all of them. People were seeking a relationship in which they could grow.

And here’s what makes AI structurally unusual for this role: the three qualities people most valued in their transformative AI experiences were not intelligence, accuracy, or speed. They were:

• Patience 
• Availability 
• Absence of judgment

A student in India: “It’s much easier for me to learn without being judged — just friendly feedback. It’s harder with friends or family to get that.”

Personal transformation has always required a mirror, something that reflects you back to yourself accurately, consistently, and without flinching. Historically that’s been a therapist, a mentor, a spiritual practice, or a journal. AI has now entered this space, not as a replacement for any of those, but as a new kind of mirror. One that is always available, never exhausted, and free of social agenda.

81% Said AI Had Already Delivered. But How?

When asked whether AI had ever taken a step toward their stated vision, 81% of people said yes.

The researchers grouped those real-world experiences into six categories and the results reveal what AI is actually doing well in people’s lives right now.

Productivity leads (32%) but look at what follows: 

• Cognitive partnership (17%), 
• learning (10%), 
• Emotional support (6%) 

Together they account for a third of all delivery experiences. 

These are the transformation categories. They are not abstract aspirations. They are lived experiences, reported by real people across 159 countries.

For personal transformation specifically, the evidence runs through hundreds of testimonies: 

• A woman processing grief who found in AI a non-judgmental listener. 
• A mother in her late 40s discovering she could understand science and philosophy. 
• A man in a homeless shelter using AI to map a path out. 

Not productivity wins. Lives changed, quietly, privately, one conversation at a time.

A 5-Stage Process for Using AI as Your Personal Transformation Engine

Personal transformation is not a product feature. It doesn’t happen by asking AI to “make you a better person.” 

Transformation is a process — iterative, cumulative, and ultimately driven by you. AI is the tool; you are the architect.

What follows is a practical framework that is informed by the research, grounded in what actually works, and built for the kind of person who wants to move from insight to action rather than accumulate ideas that never change anything.

Step 1: Detect Before You Design

Most people try to design a better self before they understand the self they already have. Purpose and identity are not invented — they are detected, revealed through pattern recognition over time. Before you ask AI to help you change, ask it to help you see clearly.

The first stage is pure reflection and data gathering. You are not trying to become anything yet — you are trying to see what you already are. Spend time here. Push past the surface answers. The quality of your self-knowledge at this stage determines everything that follows.

Ask AI to challenge you, not agree with you. One of the study’s documented concerns was sycophancy — AI reinforcing existing beliefs rather than offering genuine perspective. Guard against this explicitly.

Step 2: Name Your Identity. Then Question It

Transformation requires a gap between who you are and who you want to become. But most people either have no clear picture of their current identity, or they hold it so tightly that no gap is possible. This stage is about articulating and then interrogating your self-concept.

Carl Jung called the unconscious self we don’t acknowledge the shadow. Joseph Campbell’s Hero’s Journey begins not with adventure but with the ordinary world — the life you’re living before the call. You cannot respond to a call you haven’t heard. AI gives you a powerful tool for hearing it.

Step 3: Reframe, Don’t Reform

Most self-improvement is self-criticism with better vocabulary. Real transformation is not about fixing what’s broken — it’s about reframing what’s whole. Build a new story for who you are, one that extends your detected patterns rather than fighting them.

The research found that the most affecting transformations were not about people learning new skills — they were about people having their narrative about themselves fundamentally rewritten. A lawyer in India who believed she was terrible at mathematics. A stay-at-home mother who discovered she could understand science and philosophy.

“I’ve learned I am not as dumb as I once thought I was.”
— Lawyer, India (Anthropic study respondent)

Step 4: Build a Daily Practice, Not a One-Off Exercise

Transformation is not an event. It is a practice. The most meaningful AI-supported growth happened in people who returned to it regularly — not in single dramatic sessions but through accumulated, iterative engagement over time.

Design a simple daily or weekly ritual — a structured check-in where you review your intentions, note what’s showing up in your behaviour, and ask one genuinely hard question. The format matters less than the consistency.

Step 5: Act, Review, and Iterate (Close the Loop)

Insight without action is intellectual entertainment. The final stage and the one most people skip, is converting what you’ve learned into deliberate, specific experiments in how you live. Then reviewing what happens and going again.

The loop — Reflect → Reframe → Choose → Act → Review — is not a one-time process. 

It is the process. It spirals upward. 

Each pass brings sharper self-knowledge, more intentional choices, and a closer alignment between who you are and who you want to become.

The Shadow Side: What the Research Says to Watch For

Any honest account of AI-supported transformation has to sit with the study’s findings on what goes wrong. Anthropic identified five core tensions between what people hope for and what they fear and three of them are directly relevant to personal transformation work.

Cognitive atrophy was cited by 16% of respondents, the fear, and in some cases the lived experience, of becoming less able to think independently. 

In transformation work, this matters because genuine growth requires struggle. Use AI to surface insight, not to avoid the difficulty of sitting with hard questions.

Sycophancy was raised by 10.8%  

AI confirming what you already believe rather than challenging it. One respondent wrote that AI had reinforced their narcissistic worldview. Explicitly build challenges into your practice. Ask for the view you don’t want to hear.

Emotional dependency was named by 12% and the risk that AI becomes a substitute for human connection rather than a complement to it. 

A student in South Korea acknowledged: “My relationship with a friend became strained, and I talked more with AI then. It was a stupid choice — I should have talked with that friend.”

The technology doesn’t know where its appropriate role ends. You have to. That self-awareness is not a limitation of the tool, it is the practice itself.

The Most Human Thing About This Entire Story

Here is what Anthropic’s researchers found when they looked across all nine categories of what people wanted: most visions collapse into a single underlying desire. 

That: “AI helps them live better, not simply work faster.”

Better. More whole. More present. More aligned between who they are and who they know they could be.

This is the oldest human aspiration in recorded history. 

• The Stoics called it living in accordance with your nature. 
• Jung called it individuation. 
• Joseph Campbell called it the Hero’s Journey. 

Every wisdom tradition that has ever grappled seriously with what it means to be alive has arrived, eventually, at this same destination: the call to become more fully yourself.

What’s new is not the aspiration. What’s new is that 81,000 people, when given an AI that simply listened without judgment and asked good questions, spontaneously named this as the second most important thing they wanted from the technology.

That tells us something remarkable. Not about AI. About us. About what we’ve always wanted and perhaps never felt we had the right kind of support to pursue.

And that is we want “Personal Transformation” more than we realize. 

You don’t need to wait for the perfect tool or the perfect moment. The conversation is available to you right now. The only question is what you’ll bring to it.

The post 81,000 People Turned to AI for Personal Transformation: Why? appeared first on jeffbullas.com.

So to put AI’s growth into perspective lets look back a few years to the tech trends that led to where we are today with AI. 

The insider

I was an insider and witness to the impact of the personal computer in the mid 1980’s as corporate and government clients bought truckloads of PC’s from me. 

The naive, ambitious and driven 27 year old computer salesman and ex-teacher had stumbled into a future driven industry that was intoxicating and lucrative. I was hooked by the potential of the future and I still am. 

I was at the start of a career that would lead to AI. But I could not see that far ahead. 

But first I would like to set the context and the landscape that puts the data and growth of the AI platforms into perspective. 

Phase 1: Back to the future – Personal computers

By 1984, when Apple launched the Macintosh with its famous ‘1984’ Super Bowl advertisement — there were approximately 10 million personal computers installed worldwide. That number had grown from essentially zero in 1975. A decade to reach 10 million. 

• By 1990 it was 100 million. 
• By 1995, when Microsoft launched Windows 95 and the commercial internet went mainstream, it was over 200 million. 

The PC revolution had taken twenty years to reach a number that AI would surpass in less than three. 

Phase 2: The Internet and the Web

And that revolution was the start of a compounding acceleration of digital technology that moved from isolated computers that became connected to other company computers that saw the rise of all the world being connected in the 1990’s as the internet took us from local to global.  

The browser arrives to try and organize the information on the web. Netscape was one of the first browsers that we used. 

The PC revolution had become a communications revolution. 

Phase 3: Social media arrived

We then saw the rise of social media in the early 2000’s and I saw Twitter (X) reach 350 million users and Facebook 3 billion users in just a few short years. That changed the world again. 

I thought the pace of change was fast and overwhelming then.  

We now live in the AI era and what looked like fast change back then that now seems glacial. 

Where did AI get the information and data needed to change the world?

But let’s take a step back and look at where AI really started to be enabled and I am not talking about Turing but the industry that provided AI with the means to create and collect the data that makes up and powers the large language models today in 2026. 

“Without the PC revolution, the internet and social media, AI would not have what it needs to fuel its diet. Data” 

The rise of AI started back in 1975 and no one noticed and it was the rise of what looked like a small coup in a small country as the personal computer threatened to take over the mainframe computer industry that was dominated by IBM.  But back then it wasn’t seen as a threat but a hobbyist joke.

The PC revolution started quietly from a garage with a kit you had to assemble yourself, in a world that thought computers were for corporations.

In January 1975, a small technology magazine called Popular Electronics ran a cover story that would change the world. The headline read: ‘World’s First Minicomputer Kit to Rival Commercial Models.’ 

The computer was the Altair 8800, built by a small New Mexico company called MITS, priced at $439, and sold as a box of components you assembled yourself. It had no keyboard, no screen, and no software. When you switched it on, a row of lights blinked. That was it. That was the beginning of the personal computer revolution. 

The idea that ordinary people might one day own a computer was, at that moment, genuinely radical. Computers in 1975 filled rooms. They cost millions of dollars. They were operated by trained specialists in white coats. 

IBM, the dominant technology company of the era, had famously and repeatedly dismissed the personal computer market as too small to matter. When asked why IBM had not entered the market, an executive reportedly replied: “There is no reason for any individual to have a computer in their home.” 

That quote — attributed variously to Ken Olsen, founder of Digital Equipment Corporation, circa 1977 — captured the conventional wisdom of an entire industry. They were spectacularly wrong.

Apple: Two Men in a Garage and a Vision Nobody Believed In

This is story of how a hobbyist project became the foundation of a trillion-dollar company:

Steve Jobs and Steve Wozniak founded Apple Computer on April 1, 1976 in the Jobs family garage in Los Altos, California. Wozniak — the engineering genius of the partnership — had designed the Apple I as a personal project to show off at the Homebrew Computer Club, a Silicon Valley gathering of electronics hobbyists. Jobs saw something Wozniak didn’t: a business. The Apple I sold 200 units, hand-built, to hobbyists who had to supply their own keyboard, monitor, and power supply.

The Apple II, launched on June 10, 1977 at the first West Coast Computer Faire, was a different proposition entirely. It came pre-assembled, with a keyboard, colour graphics, a sleek plastic case, and a price of $1,298. It was designed to be approachable — a finished product, not a kit. In its first two years, however, it remained a niche machine. From 1977 to 1979, Apple sold just 43,000 Apple II and II Plus computers combined. The TRS-80, built by Radio Shack, outsold Apple by a wide margin. The personal computer market existed, but it had not yet found its reason to exist. 

Then, in 1979, everything changed. Two programmers named Dan Bricklin and Bob Frankston released VisiCalc — the world’s first electronic spreadsheet — exclusively for the Apple II. It was the first true killer app: a piece of software so useful that people bought the hardware just to run it. 

Accountants, bookkeepers, financial analysts, and business owners who had never considered owning a computer suddenly had a reason. ‘Every VisiCalc user knows of someone who purchased an Apple just to be able to use VisiCalc,’ noted Compute! magazine. Apple’s sales in 1980 jumped to 78,000 units — nearly double the previous year, with 25% of buyers citing VisiCalc as their primary reason for purchase. 

The PC revolution milestone by milestone (1975-1995)

By the end of 1980, Apple had sold over 100,000 Apple IIs — a milestone that had taken three years to reach. The company was approaching $200 million in annual revenue, and Steve Jobs was on the cover of Time magazine. The garage startup had become a genuine corporation. But the PC revolution was about to go from remarkable to unstoppable, because IBM had finally stopped laughing.

IBM Enters: The Move That Made the PC Inevitable

It was when the world’s most powerful technology company decided personal computers were real, the world listened

IBM’s decision to build a personal computer was, by its own internal standards, extraordinary. The company moved with an urgency that was entirely out of character.

The IBM PC was developed in just 12 months — an almost impossibly fast timeline for a company that typically measured product cycles in years. To achieve this, IBM made a fateful decision: rather than building everything in-house as they always had, they would use off-the-shelf components from third-party suppliers, and license an operating system from a small company in Albuquerque, New Mexico called Microsoft.

The IBM PC launched on August 12, 1981, priced at $1,565, running PC-DOS — the Microsoft-supplied operating system that would ultimately evolve into Windows and underpin the global computing industry for four decades. The machine was not technically superior to what Apple was already selling. But it carried the IBM name, and in 1981, the IBM name was synonymous with serious computing. Corporations that had been watching the personal computer market with cautious curiosity now had permission to act. IBM sold 1.3 million PCs in 1983 alone, and the IBM PC and its growing ecosystem of compatible clones — built by Compaq, Dell, HP, and dozens of others — would come to define personal computing for a generation.

Lotus 1-2-3, launched in January 1983 as a more powerful successor to VisiCalc, completed the picture. Built specifically for the IBM PC and its expanded memory, it became the most important business software of the early 1980s and drove IBM PC adoption into corporations the way VisiCalc had driven Apple II adoption into small businesses. The spreadsheet was the AI chatbot of the PC revolution: the application that made the hardware indispensable.

Warp Speed: ChatGPT Launches 

Chat GPT was the fastest technology adoption in human history. But the technology adoption was the same pattern, but different speed and incomprehensibly different scale. 

On November 30, 2022, OpenAI released ChatGPT as a quiet research preview. There was no keynote. No launch event. No Super Bowl ad. The team expected a few thousand curious users. Within five days, there were one million. Within two months, 100 million — a milestone the entire personal computer industry had taken a decade to reach. ChatGPT reached 100 million users faster than any consumer product in recorded history: faster than TikTok (9 months), faster than Instagram (2.5 years), faster than the iPhone (74 months), faster than the internet itself. 

The parallel to VisiCalc is almost too neat to be accidental. Just as the spreadsheet gave millions of businesses their first compelling reason to own a computer, ChatGPT gave millions of professionals their first compelling reason to use AI. The killer app had arrived — and this time it was the platform, not a separate piece of software running on it. 

By October 2025, Sam Altman announced that ChatGPT had surpassed 800 million weekly active users — roughly one in ten people on Earth — with the platform processing over 6 billion tokens per minute via its API.

The Apple II took three years to sell 100,000 units. ChatGPT reached 100 million users in two months. The PC industry took twenty years to reach 200 million users. ChatGPT reached 800 million in under three years. We are not watching a faster version of the PC revolution. We are watching something categorically different.

The Investment Numbers Tell the Same Story — Faster

Capital is moving into AI at a velocity the PC era never saw

The entire US venture capital market in 1980 — at the height of the PC boom — was approximately $600 million. In 2025, AI startups alone attracted $107 billion — roughly 180 times larger, even before adjusting for inflation. The Stanford HAI 2025 AI Index records that corporate AI investment reached $252.3 billion in 2024 — a 44.5% increase in a single year and a growth of more than thirteenfold since 2014. Private investment in generative AI alone grew 8.5 times in the two years following ChatGPT’s launch.

The market trajectory is equally staggering. The UN Trade and Development report projects the global AI market will grow from $189 billion in 2023 to $4.8 trillion by 2033 — a 25-fold increase in a single decade. By comparison, the PC industry took fifteen years to grow from near zero to $4 billion in annual revenues. AI is moving at a pace the PC era never approached.

The Scale in Numbers

The PC vs AI Revolution — Head to Head

The numbers that put the speed of the AI revolution in perspective

The Pattern That Keeps Repeating — And What It Means For You

Fragmentation, then consolidation — the PC era’s warning for AI

The PC revolution of the 1980s followed a pattern that every technology wave since has repeated: explosive early fragmentation followed by rapid consolidation around a small number of dominant platforms. In 1983, there were dozens of competing PC architectures — Apple, IBM, Commodore, Atari, Tandy, Osborne, and Kaypro all sold machines that were largely incompatible with each other. By 1990, virtually all of them had been absorbed into two dominant platforms: IBM-compatible DOS/Windows PCs and the Apple Macintosh. The platforms that won did so not because they were technically superior in every dimension, but because they had the right ecosystem, the right distribution, and the right moment.

AI in 2025 looks remarkably similar to the PC market in 1982. There are ten serious contenders, each with genuine capabilities, distinct philosophies, and different strategic bets. Some — like DeepSeek and Mistral — are equivalent to the early Compaq: technically excellent challengers taking on the incumbents with a more efficient architecture. Some — like Perplexity — are the equivalent of the specialised word processor or database program: not trying to win the whole market, just a critical slice of it. And some — ChatGPT, Claude, Gemini — are competing to be the IBM PC and Apple Macintosh of this era: the two or three platforms that the entire world eventually converges around.

The most important insight from the PC revolution is this: the platform that wins is not always the one that leads at the start. IBM dominated personal computing in 1983. By 1995, Microsoft — IBM’s operating system supplier — was the most powerful company in technology, and IBM’s PC division was a declining business. Apple went from near-bankruptcy in 1997 to the world’s most valuable company by 2012. The winners of the AI era are not yet determined. The platforms that lead today will not all lead tomorrow.

Which brings us to the question this report is designed to answer. Not which AI is winning — that is the wrong question, and the race is far from over. But which AI platforms win for you: for your specific workflow, your craft, your industry, your goals. Because in a market this large, this fast, and this consequential, understanding the genuine strengths and limitations of each platform is not a luxury. It is a competitive necessity.

The PC revolution took twenty years to reach 200 million users and reshape the economy. The AI revolution did it in under three years. We are not living through a faster version of what came before. We are living through something new. The question is not whether AI will transform your industry. The question is whether you will be the one doing the transforming — or the one being transformed.

The Master Scoreboard

All ten platforms, all ten dimensions, at a glance. Use this as your reference throughout the article.

#1 ChatGPT

Origin Story

From non-profit safety lab to the product that redefined the internet

OpenAI was founded in December 2015 as a non-profit research laboratory by Elon Musk, Sam Altman, Greg Brockman, and Ilya Sutskever, with a mission to ensure AGI benefits all of humanity.

The progression from GPT-1 in 2018, through GPT-2 in 2019 — initially withheld for fear of misuse — to GPT-3 in 2020 marked a decade of foundational research invisible to the mainstream. ChatGPT launched on November 30, 2022, and within two months had 100 million users, reaching over 800 million weekly active users by October 2025. OpenAI secured a $13 billion investment from Microsoft and evolved from non-profit to capped-profit entity — a transition that drew legal challenge from Elon Musk.

What It Is Today

An AI ecosystem — not just a chatbot

ChatGPT in 2025 encompasses Custom GPTs, Canvas, Operator, the GPT-4o model, o1 and o3 reasoning models, and DALL-E 3 image generation — all within a single conversation interface.

Dimension Scores

Capability Radar Chart

Breadth of coverage across all 10 dimensions

Verdict

ChatGPT is the undisputed platform of the mainstream. 9 or above on seven dimensions makes it the most consistently capable all-rounder. The safety trade-offs are real, but for most use cases ChatGPT remains the first tool you reach for.

#1 Claude

Origin Story

A disagreement about safety that changed AI’s trajectory

In 2021, Dario Amodei and colleagues resigned from OpenAI over safety disagreements, founding Anthropic. The company built Constitutional AI from first principles, raising $7.3 billion including partnerships with Amazon and Google. By 2024, Claude 3.5 Sonnet had established Anthropic as the most credible technical rival to OpenAI.

What It Is Today

The professional’s AI — built for ceiling, not breadth

Claude offers a 200,000 token context window and earns a perfect 10 on coding, writing, and reasoning. Claude Code enables agentic software development. See the full model documentation for details.

Dimension Scores

Capability Radar Chart

Three perfect 10s — and one notable gap on image generation

Verdict

Claude is the professional’s AI. For writing, reasoning, or complex code, it consistently raises the bar. Its image generation gap (6/10) and smaller consumer footprint are real constraints. But for the thinking, the drafting, the building — nothing touches it.

#3 Gemini

Origin Story

The company that invented the transformer, caught flat-footed by it

Google published ‘Attention Is All You Need’ in 2017 — the foundational transformer paper — yet was caught flat-footed. Google declared an internal ‘code red’. Bard’s first public demo stumbled, wiping an estimated $100 billion from Alphabet’s market cap in one day. The reorganisation under Demis Hassabis produced Gemini in December 2023.

What It Is Today

The multimodal giant — native video, audio and image understanding

Gemini 1.5 Pro with its 1 million token context window can process an entire feature film in a single session. Google Workspace integration reaches 3 billion users across Gmail, Docs, Sheets, Slides, and Meet. Its Deep Research feature synthesises multi-step web research into cited professional reports in minutes.

Dimension Scores

Capability Radar Chart

The most balanced profile in the top 3 — perfect 10 on Multimodal

Verdict

Gemini is the most underrated platform in this ranking. On multimodal understanding — no competitor comes close. For Google Workspace teams it is the most practically transformative AI available.

#4 Copilot

Origin Story

The $1 billion bet that paid off — and the AI that lives inside Office

In 2019 Satya Nadella authorised a $1 billion investment in OpenAI, later deepened to $13 billion. GitHub Copilot launched in June 2022 with over one million paying subscribers. Microsoft 365 Copilot followed in March 2023 across Word, Excel, PowerPoint, Teams, and Outlook.

What It Is Today

The ambient AI layer of enterprise work

Copilot in Teams captures meetings in real time. Copilot in Excel builds models from natural language. Copilot in Outlook summarises a week of emails in seconds.

Dimension Scores

Capability Radar Chart

Consistent 7-8 across all dimensions — integration depth over capability peaks

Verdict

Copilot is the AI for people already inside Microsoft’s world. Its GitHub Copilot developer experience is the industry standard. But it is fundamentally a delivery mechanism for OpenAI models, which caps its ceiling.

#5 Meta AI

Origin Story

From academic research lab to the world’s largest AI distribution network

In 2013, Mark Zuckerberg recruited Yann LeCun to lead Facebook AI Research (FAIR). In February 2023 Meta released LLaMA as open source, followed by LLaMA 2 and Llama 3 in 2024. Meta AI as a consumer product launched in 2024, embedded in platforms used by 3.27 billion people daily.

What It Is Today

AI as a social layer — zero friction for 3 billion users

Meta AI is embedded across WhatsApp, Instagram, Facebook, and Messenger — platforms people already use daily. Its open-source Llama ecosystem underpins private enterprise AI deployments globally.

Dimension Scores

Capability Radar Chart

Balanced mid-range profile — 6/10 Agentic AI is the key gap

Verdict

Meta AI’s strength is distribution at a scale that has no precedent. As a pure-capability platform it trails the top three. But as open-source infrastructure and a distribution play, its strategy may prove more consequential than its consumer scores suggest.

#6 Grok

Origin Story

The AI built from a feud — and a philosophy of maximum freedom

Elon Musk co-founded OpenAI in 2015 before resigning in 2018. After acquiring Twitter (X) in October 2022, he founded xAI in March 2023. Grok launched in November 2023 for X Premium subscribers. Grok 3, released in early 2025, now competes on reasoning benchmarks with Claude and GPT-o1.

What It Is Today

Real-time intelligence — the only AI plugged into a live social firehose

Grok’s X integration delivers real-time social intelligence no competitor can match — live news, trending narratives, market sentiment as they unfold. Aurora, xAI’s image model, produces less-filtered results than competitors.

Dimension Scores

Capability Radar Chart

Strong on Reasoning and Speed — with deliberate safety trade-offs

Verdict

Grok is technically serious with a contrarian philosophy. Its real-time X integration is a genuine competitive moat. Its 5/10 safety score reflects real costs in enterprise contexts.

#6 DeepSeek

Origin Story

The moment Silicon Valley’s core assumption about AI costs collapsed

DeepSeek was founded in 2023 as a subsidiary of High-Flyer, a Chinese quantitative hedge fund. In January 2025 the company published the DeepSeek R1 technical report — a reasoning model matching OpenAI’s o1 at a reported training cost of approximately $5.6 million. Nvidia’s share price fell 17% in a single day. Silicon Valley’s assumption that frontier AI required frontier capital had been directly challenged.

What It Is Today

Open-weight frontier models — the on-premise enterprise option

DeepSeek’s open-weight models V3 and R1 are freely downloadable and locally deployable. Transparent chain-of-thought reasoning makes outputs particularly useful for mathematical and scientific problem-solving.

Dimension Scores

Capability Radar Chart

Twin peaks on Coding and Reasoning — structural gaps on images and safety

Verdict

DeepSeek is the most technically important story most Western professionals are still underestimating. Its open-weight model strategy makes it ideal for private enterprise AI. Chinese jurisdiction and a 5/10 safety score create real constraints where data privacy is non-negotiable.

#8 Perplexity

Origin Story

The answer engine that decided not to be a chatbot

Perplexity was founded in August 2022 by Aravind Srinivas and co-founders from OpenAI, Google, DeepMind, and UC Berkeley, with the thesis that search was broken. It built an AI-native answer engine with inline citations, raising $73.6 million in 2023 and reaching a valuation of over $3 billion by 2024.

What It Is Today

Research-first, citation-always — the most trusted answer engine

Three modes: standard search, Pro Search, and Deep Research. The Spaces feature enables team research collaboration.

Dimension Scores

Capability Radar Chart

The most distinctive shape in the rankings — specialist by design

Verdict

Perplexity chose depth over breadth and was completely right to. It is not trying to be ChatGPT — it is trying to be the best research tool ever built. For fact-checking and multi-source synthesis, it belongs in your daily AI stack.

#9 Mistral

Origin Story

Europe’s answer to the American and Chinese AI giants

Mistral AI was founded in April 2023 in Paris by researchers from DeepMind and Meta AI. Mistral 7B, released open-source in September 2023, set performance-per-parameter records. The company raised €105 million in seed funding and reached a valuation of over $6 billion. Macron cited Mistral publicly as evidence of European AI competitiveness.

What It Is Today

GDPR-native, efficiency-first, developer-beloved

Codestral supports 80+ programming languages. Le Chat positions as a European consumer AI alternative. Mistral Large targets enterprise cases where GDPR compliance and European data residency are non-negotiable.

Dimension Scores

Capability Radar Chart

Strong on Coding and Speed — image generation is the clear gap

Verdict

Mistral’s significance outstrips its consumer scores. It has given European enterprises a credible GDPR-native AI option. As enterprise infrastructure for European organisations, it may be the most strategically important platform in this ranking.

#10 Cohere

Origin Story

Built by a transformer co-author — before ChatGPT existed

Cohere was founded in 2019 by Aidan Gomez, Nick Frosst, and Ivan Zhang — former Google Brain researchers. Gomez was a co-author on ‘Attention Is All You Need’. Cohere built Command for business text generation and Embed for semantic search. It raised over $445 million from Salesforce, Oracle, and Nvidia.

What It Is Today

The infrastructure layer for private enterprise AI

Command R+ is optimised for enterprise RAG workflows. The North platform provides no-code AI for enterprise teams. Cohere is the only platform with full multi-cloud and on-premise deployment flexibility.

Dimension Scores

Capability Radar Chart

The enterprise specialist profile — Safety is the standout score

Verdict

Cohere does not compete for individual users — it competes for enterprise AI infrastructure. Its RAG optimisation, deployment flexibility, and compliance depth make it the most credible choice for large-scale private AI deployment.

The Tactical Cheat Sheet

The master insight: the most powerful AI strategy in 2025 is not picking one winner. It is building a deliberate stack — matching each platform to the task it was built to dominate.

Why This Research Matters And What to Do With It

We are living at a unique and disorienting moment in the history of technology. The tools available to an individual in 2026 are, by any objective measure, more powerful than anything a corporation could buy for any price a decade ago. 

A solo creator with a laptop and the right AI stack can now research, write, code, design, analyse, and publish at a speed and quality that would have required an entire agency in 2015. That is not hyperbole. That is the quiet revolution happening inside every laptop, every morning, in every timezone on Earth.

But here is the problem nobody talks about.

 Access to powerful tools does not automatically translate into using them well. A professional kitchen full of world-class knives does not make you a chef. Knowing which knife to reach for — and when — is the entire craft. The same principle applies to AI in 2026. The platforms exist. The capability is extraordinary. The gap between the people winning with AI and the people merely using it comes down to two things: First is clarity and then intentionality.

The Core Finding: There Is No Single Best AI

If there is one thing this research makes irrefutably clear, it is this: the question “which AI is best?” is not just unanswerable — it is the wrong question. 

Every platform in this ranking is world-class at something. Claude earns a perfect 10 on coding, writing, and reasoning — and a 6 on image creation. ChatGPT scores 9 or above on seven dimensions simultaneously — and still trails Claude where writing quality matters most. 

Gemini leads the world on multimodal understanding — and still has consistency gaps that would concern enterprise users. 

DeepSeek matches the global elite on reasoning — and raises legitimate data privacy concerns for anyone outside China.

The right question is not which AI is best. The right question is: which AI is best for this task, this workflow, this goal, right now?

That is a harder question. It requires you to know your own work well enough to match it to a tool. It requires curiosity rather than habit. And it requires the willingness to use more than one platform — to build what the most effective AI users in 2026 are already building: a deliberate stack.

What the Research Reveals About Each Platform’s Real Role

Think of the top 10 AI platforms not as competitors in a single race but as specialists in a professional firm. Every firm has a strategist, a researcher, a writer, a coder, an analyst, an archivist, and a connector. No one person does all of those roles equally well — and no one AI platform does either.

1. Claude is your senior writer and lead developer: exceptional at long-form thinking, nuanced prose, and complex code. Reach for Claude when the quality of the output matters more than the speed of the iteration.
2. ChatGPT is your chief of staff: the broadest capability set, the most mature ecosystem, and the most frictionless experience for everyday tasks. Reach for ChatGPT when you need a reliable generalist who can turn their hand to almost anything.
3. Gemini is your multimodal research director: unmatched at processing video, audio, images, and text simultaneously, and deeply integrated into the tools that most business teams already use. Reach for Gemini when the task involves Google Workspace or when you need to work across multiple media types in a single session.
4. Perplexity is your head of research: purpose-built for verifiable, cited, real-time answers. Reach for Perplexity when accuracy matters more than creativity and when you need to show your sources.
5. DeepSeek is your technical analyst: world-class reasoning and code at a fraction of the cost, with transparent chain-of-thought that shows its working. Reach for DeepSeek for mathematical, logical, and scientifically rigorous tasks — with appropriate awareness of its data jurisdiction.
6. Grok is your intelligence analyst: the only AI with a live feed into the world’s largest public conversation platform. Reach for Grok when you need to know what is happening right now, not what was true six months ago.
7. Copilot is your enterprise productivity layer: the AI that lives inside the tools hundreds of millions of knowledge workers already use every day. Reach for Copilot when you live inside Microsoft 365 and want AI that integrates rather than interrupts.
8. Meta AI is your social strategist: embedded in the platforms where your audience already spends its time. Reach for Meta AI when you are creating content for social platforms or when you want to meet people where they already are.
9. Mistral is your European compliance officer and developer ally: GDPR-native, deployment-flexible, and technically exceptional at code. Reach for Mistral when data sovereignty matters or when you need a fast, efficient model for European enterprise use.
10. Cohere is your enterprise infrastructure architect: not for individual users, but for organisations building private, compliant, large-scale AI pipelines. Reach for Cohere when you are building AI products rather than using them.

The Three Levels of AI Mastery in 2026

The research points to three distinct levels at which people are engaging with AI in 2026 — and the gap between them is widening rapidly.

The first level is the tourist. The tourist uses one platform for everything, defaults to the same prompts, and treats AI as a faster version of Google search. They get value — but nowhere near the value available to them. Tourists represent the majority of current AI users, including most professionals who believe they are “using AI.”

The second level is the craftsperson. The craftsperson has learned one or two platforms deeply, understands how to prompt effectively, and uses AI as a genuine collaborator on their most important work. They get significantly more value than the tourist and are building a meaningful productivity advantage. This is where most serious AI users aspire to be.

The third level is the architect. The architect has built a deliberate AI stack — two to four platforms, each chosen for a specific category of work, each integrated into a workflow that compounds over time. They do not ask which AI is best. They ask which AI is best for this. They are building habits, systems, and outputs that the tourist and the craftsperson simply cannot match. The architect is not necessarily more intelligent than the others. They are simply more intentional.

This research exists to help you move from tourist to craftsperson to architect. The data shows you where each platform genuinely excels. The tactical cheat sheet gives you the specific routing decisions. The backstories give you the context to understand why each platform is built the way it is — because a platform’s philosophy shapes its capability in ways that benchmarks alone cannot capture.

Why Now Matters More Than You Think

Here is the most important thing the data on AI adoption reveals: the gap between early AI adopters and late adopters is not closing. It is widening. Companies that moved early into generative AI report $3.70 in value for every dollar invested. Top performers are achieving $10.30 per dollar. Workers with verifiable AI skills command a 43% wage premium — a figure that has nearly doubled in two years.

The people who understood the personal computer in 1983 had a decade-long head start on everyone who caught up in 1993. The people who mastered the web in 1997 had a decade-long head start on those who caught up in 2007. The window for building meaningful advantage with AI is not infinite. The technology is moving faster than any that preceded it, and the compound effect of early intentional adoption is already visible in the data.

This is not an argument for anxiety. It is an argument for clarity. You do not need to use every platform in this ranking. You do not need to master AI overnight. You need to make one decision: to stop using AI by default, and to start using it by design.

The Question That Changes Everything

The question that separates the architects from the tourists is not “which AI is best?” It is simpler and more personal than that.

It is: what am I actually trying to do and which tool was built to do exactly that?

Answer that question for your writing. Answer it for your research. Answer it for your code. Answer it for your data. Answer it for your enterprise workflows. And then build the stack that answers it for all of them.

The AI revolution is not waiting for you to be ready. It arrived in November 2022, and it has been accelerating every day since. The platforms are built. The capability is here. The only variable left is how deliberately you choose to use it.

The master insight: the most powerful AI strategy in 2025 is not picking one winner. It is building a deliberate stack — understanding each platform’s genuine strengths, matching tools to tasks, and staying curious as the landscape evolves. The platforms that lead today will not all lead tomorrow.

(Scores are relative assessments across 10 capability dimensions. All platforms continue to evolve rapidly. Always verify with current sources.)

The post Stop Using Just One AI: The 10 That Matter (and What Each Does Best) appeared first on jeffbullas.com.

There is a moment most people never talk about.

It comes after the retreat, after the long conversation, after the journaling session that finally cracked something open. You’ve had the insight. You can feel it — that quiet, precise sense of this is it. Something you’ve been circling for years has finally been named.

And then… nothing.

The insight sits there. Days pass. Weeks. The clarity that felt so urgent in the moment of discovery starts to fade at the edges. Life reasserts itself — the inbox, the obligations, the loud ordinary noise of a full schedule. And the purpose that felt so close becomes something you ‘should really get back to.’

This is the action gap. And it may be the most common — and least discussed — failure point in the entire purpose conversation.

Researchers have a name for it: implementation paralysis. Studies from the Kellogg School of Management found that people with the most meaningful personal insights are often less likely to act on them than those with shallower realisations. The reason is counterintuitive but important: a big insight raises the stakes. What felt like a clue now feels like a calling — and callings, by their nature, feel enormous. Permanent. High-risk.

So instead of moving forward, most people freeze. They read more books. They talk to more friends. They start another journal. They tell themselves they need to be ‘more ready’ before they can truly begin.

The gap between knowing and doing is not a willpower problem. It is a method problem.

This article is about the method.

Why the Insight Isn’t Enough

Aristotle made a distinction the self-help industry has largely ignored.

He separated “Sophia” — philosophical wisdom, the knowing of things — from “Phronesis” — practical wisdom, the skill of acting well in particular situations. His argument was that knowing what is good and actually doing what is good are entirely different capacities, and they require entirely different development.

You can understand your purpose completely at the level of sophia — and be entirely undeveloped at the level of phronesis. That is, you can know what you’re for, and still not know how to move toward it in the real, specific, messy conditions of your actual life.

The data bears this out. People who score high on philosophical self-awareness but low on practical action-taking report life satisfaction scores barely above those who score low on both. The combination that predicts flourishing is not greater insight — it’s insight paired with the discipline of moving.

The ancient Chinese philosopher Mencius put it even more bluntly: ‘The great man is he who does not lose his child’s heart.’ Purpose isn’t the sophisticated thing — it’s the original impulse. The sophistication lies in protecting that impulse as it meets the resistance of the world.

Both Aristotle and Mencius were pointing at the same thing: insight and action are not the same muscle. You have to train the second one separately.

The People Who Froze — And Then Moved

Three stories. Three very different forms of paralysis. One thing in common.

Sylvester Stallone — Refusing to Negotiate Identity

Stallone spent years knowing he was a storyteller. He had a burning clarity about his calling — writing and acting — long before he had the means or the platform. He was turned down by over 1,500 talent agents. He was so broke he sold his dog for $50 to pay rent.

But when he wrote the script for Rocky in three and a half days, he had one unshakeable rule: he would not sell it unless he could play the lead. He was offered $125,000 for the script. He declined. He was offered $325,000. He declined again. Everyone around him told him he was insane.

Rocky cost $1 million to make. It won three Academy Awards, including Best Picture. The lesson isn’t about money. It’s about the moment Stallone decided his purpose was not negotiable.

J.K. Rowling — Acting Under Impossible Conditions

Rowling had the complete idea for Harry Potter arrive in her mind on a delayed train from Manchester to London in 1990. The full concept — the boy wizard, the school, the arc of seven books — appeared to her almost fully formed.

She spent the next five years writing the first novel while clinically depressed, unemployed, newly divorced, and raising a child alone. Harry Potter and the Philosopher’s Stone was rejected by twelve publishing houses before Bloomsbury’s eight-year-old daughter read the first chapter and refused to put it down.

Rowling didn’t wait for conditions to improve. She acted inside the conditions that existed.

Charles Darwin — The Cost of Waiting

Darwin knew the central insight of evolution decades before he published it. He held On the Origin of Species for nearly twenty years — paralysed by the scale of what he knew, the ferocity of the opposition he anticipated, and the weight of being right.

It was only when Alfred Russel Wallace arrived at the same conclusion independently that Darwin finally published. The push he needed wasn’t more certainty. It was the realisation that waiting had its own cost.

Three very different stories. Three very different forms of paralysis. One thing in common: at a certain point, each of them stopped managing the insight and started moving with it.

The 5 Steps From Insight to Action

Step 1: Name It Precisely — and Declare It Out Loud

A vague purpose cannot be acted on. ‘I want to help people’ is not a purpose — it’s a direction of feeling. The action gap lives in the vagueness. Language is not decoration — it is activation. When you name something precisely, you give your brain a target. And when you declare it out loud to another person, the psychological stakes shift. Research from the Dominican University of California found that people who write down their goals and share them with an accountability partner are 76% more likely to achieve them than those who keep their goals private. Name it as specifically as you can: not ‘I want to teach’ but ‘I want to help first-generation university students navigate the gap between academic knowledge and professional life.’ The more precise the name, the more direct the path.

Step 2: Take the Smallest True Action — Today

The enemy of beginning is scale. When purpose feels large — and it always does, at the start — the temptation is to wait until you can act at the scale it deserves. This is the trap. BJ Fogg’s research on behaviour design at Stanford makes this point with unusual force: the brain learns through repetition, not intensity. A five-minute action taken daily for thirty days creates more durable neural change than a weekend retreat. The question is not ‘what is the right first step?’ It is: ‘what is the smallest possible action that is still true to what I’m moving toward?’ Stallone didn’t write Rocky in one sitting. He wrote three and a half pages a day for three days.

Step 3: Build Identity Before You Build a Schedule

Most people try to schedule their way into purpose. They block out Tuesday mornings for ‘deep work.’ They set a timer. They create a habit tracker. And within six weeks, they have abandoned it — not because they lacked discipline, but because they were trying to bolt a new behaviour onto an old identity. James Clear’s research into habit formation found that the most durable behavioural changes come not from asking ‘what do I want to achieve?’ but ‘who do I want to become?’ The shift from ‘I’m trying to write more’ to ‘I am a writer’ is not semantic. It is the difference between effort and identity. Purpose-aligned action becomes sustainable when it is not something you do, but something you are.

Step 4: Name Your Real Constraint — Not the Comfortable One

Most people, when asked what is stopping them from acting on their purpose, give a comfortable answer. ‘I don’t have enough time.’ ‘I don’t have the right qualifications.’ ‘I’m waiting until the kids are older.’ These are real constraints. They are also, in the majority of cases, not the real constraint. The real constraint is usually one of three things: Fear of judgment from a specific person or group whose opinion carries enormous weight. Distributed attention — the intellectual life of someone who is interesting in too many things to be fully committed to one. Or absence of community — acting in isolation without even one person who holds your purpose as real. Name your true constraint. Then address that one — not the comfortable substitute.

Step 5: Review, Reframe, and Refuse to Stop

The final step is not really a step. It’s a practice — the discipline of staying in the loop rather than declaring the journey over after the first attempt stalls. The neuroscience of habit and identity change consistently points to the same mechanism: the brain learns through iteration, not intensity. Build a weekly review practice around a single question: Did I act in the direction of my purpose this week? If yes — what happened, and what does it tell you? If no — what stopped you, and what does that reveal? The Stoics called this askesis — training. Not inspiration. Not epiphany. Training.

The Role AI Plays in Closing the Gap

For most of human history, the bridge between insight and action required either exceptional internal discipline or access to skilled external support — a coach, a mentor, an operator, or a guide who could help you hold your purpose as real across time and turn vague intention into concrete movement.

AI changes that equation in two profound ways.

First, it can function as a form of mentor-like support

Not as a replacement for human wisdom or relationship, but as something that has never previously existed at this scale: a witness with perfect memory, zero fatigue, and no social agenda. It can hold your stated purpose alongside your actual behaviour across weeks and months, and surface — without judgment — the gap between who you say you want to become and how you are actually living.

Second, it can collapse the time, cost, and friction between ideation and execution.

What once required a team, a long runway, or significant resources can now begin with a prompt, a draft, a prototype, a plan, a workflow. AI can help turn an intuition into language, language into strategy, strategy into assets, and assets into action. It does not just help you reflect. It helps you build.

That is the real shift.

The purpose has been detected. The pattern has been named. Now the work becomes daily: the small acts, the identity decisions, the weekly reckoning, and the practical execution that turns possibility into momentum.

That work doesn’t require a perfect plan. It requires a first step, taken before you’re ready, in the direction you already know.

The Question That Changes Everything

Aristotle, Seneca, Mencius — separated by centuries and cultures — all arrived at a version of the same instruction.

Stop theorising about the good life. Live in the direction of it, in whatever small way is available to you today.

You already know what your purpose is pointing toward. You’ve had the insight. The signal is there.

The only remaining question is the one that has always mattered most:

What are you going to do about it — today?

This is the second article in a series on purpose detection and intentional living in the Human + Machine Age. Read the first piece: “The Purpose Trap: Stop Searching. Start Detecting.”

The post You’ve Found Your Purpose. Now What? The 5 Steps From Insight to Action appeared first on jeffbullas.com.

Most people spend years searching for their purpose and never find it. Not because it doesn’t exist — but because searching is the wrong method.

Purpose isn’t something you construct. It’s a pattern already running through your life: in the problems you keep returning to, the work that makes you lose track of time, the things you can’t stop noticing that others walk past.

The data is stark. 75% of millennials struggle to find direction. 49% of midlife adults feel trapped. $8.9 trillion in productivity is lost annually because people have no meaningful connection to what they do. That is billions of people with no purpose and trillions in lost productivity. 

AI changes the equation. 

Not because it’s wise — but because it’s a pattern recognition machine with perfect recall, no judgment, and no fatigue. It can read a 5,000-word career narrative in under 30 seconds and surface what keeps recurring — what your own memory has been too biased, too busy, or too close to see.

This post is about why the search fails — and what detecting and using AI to unlock your purpose looks like instead.

The Lost Billions

There is an epidemic hiding in plain sight.

It doesn’t make headlines. It has no official diagnosis. But it may be the most widespread source of human suffering in the modern world — more pervasive than burnout, more quietly destructive than anxiety, and almost completely unaddressed by the systems we have built to help people live well.

It is the feeling of being lost.

Not geographically. But existentially — unmoored from any clear sense of direction, purpose, or meaning. And it touches every stage of life.

And according to Harvard research across 

The opposite of being lost?

It’s purpose. But what is it?

“Purpose is the recurring pattern of what energises you, repeated across decades of your life, that you’ve been too close to see clearly.”

And being lost isn’t reserved for one demographic. And according to Harvard research 75% of us don’t have sense of purpose. That means Billions  of us are feeling lost.  

Here are 3 snapshots across the spectrum of what being lost feels like.

The aspiring, confused and lost university student

The eighteen-year-old choosing a degree for a life they haven’t lived yet, picking something reasonable, something their parents suggested — and arriving at their second year with a quiet sense of wrongness, of being on a track that belongs to someone else.

The executive with a mid life crisis

The forty-three-year-old who has done everything right — built the career, raised the family, hit the milestones — and who woke up one Tuesday morning with the peculiar terror of realising the life they constructed doesn’t feel like theirs.

The end of career identity crisis 

The sixty-seven-year-old who retired from a distinguished career and found, within months, that the identity built over forty years had dissolved. Without the title, the role, the rhythm — a silence where a self used to be. And twenty or thirty years of life remaining with no clear answer to: who am I now?

Three stages. One experience: standing at the edge of a vast open field with no map, no compass, no sense of which direction leads to a life that actually fits.

The advice available to all three is identical: search for your purpose. Journal. Reflect. Take the personality test. Attend the retreat. Most of them do exactly that. And most of them are still lost.

“What if the search itself is the trap?”

What if purpose isn’t a destination you arrive at — but a pattern you’ve been living all along, too close to see clearly?

The Scale of the Problem

The purposelessness epidemic extends far beyond personal crisis. Every year, Gallup surveys more than 128,000 workers across 160 countries to measure engagement — the degree to which people feel genuinely purposeful in their work. The findings are, year after year, staggering.

In 2024, just 21% of the global workforce reported being engaged at work. Four in every five workers — billions of people — are either going through the motions or actively working against the organisations that employ them.

The global economic cost of this disengagement: $8.9 trillion per year — equal to 9% of global GDP. This isn’t a productivity problem. It is a meaning problem. And no personality test, vision board, or corporate values poster has made a meaningful dent in this number in decades.

Why This Is a Health Crisis, Not Just a Career Problem

The relationship between purpose and mental health is not aspirational. It is clinical.

Research from Harvard’s Graduate School of Education — based on nationally representative surveys of over 1,800 individuals — found that young adults without purpose experienced anxiety and depression at more than twice the rate of those with a sense of direction.

54% of young adults without purpose reported anxiety or depression. With a clear sense of meaning: 25%. A meta-analysis across 16 studies found that purpose reduces stress responses across all ages, sexes, and ethnicities — and links to lower chronic disease, greater resilience after trauma, and measurably longer lifespans.

“Purpose isn’t a luxury. It is one of the most powerful protective factors for mental health that researchers have identified.”

The Dip Nobody Talks About

The purpose crisis doesn’t touch every life stage equally. Economists David Blanchflower and Andrew Oswald identified what is now known as the happiness U-curve — one of the most replicated findings in wellbeing research, documented across more than 132 countries. Life satisfaction is relatively high in our twenties, declines through our thirties and forties, and reaches its lowest point at approximately age 47, before rising again.

This is not a Western cultural artefact. It has been found in studies of great apes. What the U-curve actually captures, many researchers believe, is the gradual accumulation of unlived life — the growing distance between who you are and who you feel you might have been.

The good news buried in this data: the curve goes back up. And for those who learn to read what the valley is telling them, the second half of life can become the richest. But only if you know what to look for.

Why the Search Keeps Failing: The Psychology Behind the Trap

We have inherited an architectural model of purpose: design the ideal future self, reverse-engineer from vision to action, build toward something. It assumes a unified “I” sitting behind the eyes, surveying the options and choosing a direction.

But Carl Jung spent a lifetime demonstrating why that assumption breaks down. You are not one person. You are a constellation of selves — the persona you present to the world, the shadow (everything you have disowned or deemed too contradictory to claim), and the deeper archetypes shaping your choices from below conscious awareness. Dan McAdams’ decades of research on narrative identity arrived at the same place: people with strong, stable purpose didn’t discover it in a single revelation. They recognised it — pattern-matching across dozens of small, unrelated experiences where something unmistakably lit up.

“The self is not a unified subject. It is an ecology — complex, sometimes contradictory, always richer than any single story you tell about yourself.”

The Shadow is the key. It contains not just what is harmful, but what is inconvenient — too vulnerable, too contradictory to hold alongside the identity you’ve constructed. The analytical professional who secretly wants to make art. The high-achiever who craves solitude but keeps filling the calendar. Whatever you exile doesn’t disappear. It accumulates energy, surfaces as recurring irritation, persistent fantasy, or the creative impulse that has been waiting patiently for fifteen years. Jung called it fate: what we don’t make conscious appears in our life as patterns we seem unable to escape.

The practical implication is profound. Your contradictions are not the problem to solve before purpose can begin. The tension between who you’ve been performing and who keeps trying to emerge — that is frequently where the calling actually lives.

“Whatever you exile doesn’t disappear. It accumulates energy in the dark — and eventually, it will find a way to be heard.”

Stop Searching. Let AI Detect the Patterns.

If this is right — and the evidence from depth psychology, narrative research, and decades of clinical work suggests it is — then the practice of purpose looks entirely different from what we’ve been taught.

It becomes archaeological rather than architectural. You don’t design a future self. You excavate what’s already present but unread. You treat your own life the way a geologist reads strata — not for what should be there, but for what actually is.

The Questions That Actually Reveal Something

Where has your energy risen without permission? Before your rational mind approved it. The spontaneous engagement, the hours you lost, the topic you return to across decades despite never being asked to. These are the psyche voting with its attention.

What consistently irritates or fascinates you about other people? Both are mirrors. The person who enrages you often reflects something you’ve exiled in yourself. The person you admire often embodies something you’re afraid to claim.

What have you been quietly orbiting for years — never quite committing to, never quite walking away from? That recurring theme that doesn’t fit your official story. That one thing.

Why Every Transition Is Actually the Advantage

For the person in midlife, the pattern has been running for twenty years. The evidence is overwhelming — if you’re willing to look at it honestly. For the newly retired, it may be the most important reframe of all: you are not starting over. You are finally free to read what has always been there. The patterns your professional role required you to suppress are now available to you for the first time. That is not loss. That is access.

The reality? AI can detect your patterns for you. 

“You weren’t lost. You were just reading the wrong map.”

There is a certain irony in the fact that the tool best suited to solving the oldest human problem — who am I, and what am I here for? — arrived in the form of artificial intelligence.

The AI Machine That Was Built for This

Not because AI is wise. Not because it understands the human soul. But because of something far more specific and far more useful: AI is, at its core, a pattern recognition machine. And purpose, as we’ve established, is not something to be invented. It is a pattern to be detected.

Your life has generated decades of data. The jobs you chose and the ones you left. The problems you were drawn to and the ones that drained you. The moments you lost track of time. The ideas that kept returning uninvited. The things you said yes to when you should have said no — and the things you kept saying no to despite a persistent pull. All of it is signal. And most of it has never been properly read.

“AI doesn’t tell you who you are. It helps you finally see what you’ve been showing it — and yourself — all along.”

Why AI Outperforms Every Traditional Method

Consider how the alternatives actually perform on the dimensions that determine whether pattern detection happens.

A skilled coach or therapist brings deep human wisdom and relational attunement. But they hold perhaps an hour of your narrative in active attention at one time. They tire. They carry their own unresolved material. They are available fifty minutes on a Tuesday — and the social weight of another person in the room means you edit yourself, even when you don’t mean to.

Traditional journaling offers genuine privacy. But your own memory is the instrument — and memory is notoriously biased toward the recent and the emotionally loud. The journal cannot push back, cannot hold the arc, cannot tell you what you keep returning to across a decade of entries.

AI changes the equation on every dimension simultaneously.

• 48.7% of people with ongoing mental health conditions already turn to AI for emotional support and reflection — not a future trend, but present reality. (Sentio University, 2025)
• Under 30 seconds is all AI needs to read, cross-reference, and surface patterns from a 5,000-word career narrative. A human therapist reading the same document takes approximately 20 minutes — and retains only a fraction by the next session.
• Zero judgment, comparison, agenda, or fatigue. These are not aspirational qualities in AI — they are structural properties. The container is genuinely neutral in a way no human relationship can be.
• 24/7/365 availability at near-zero cost, with no waiting list — compared to an average 3–4 week wait for a therapist appointment in most countries.

But there is a vital property that AI provides that humans can’t when you are in the process of unearthing and revealing the patterns of your purpose in your stories and words and narrative. Finding your unique identity signature that no one else has. 

And that is having a safe place where you don’t feel judged. 

The Safety Container No Human Can Provide

Pattern recognition is only half the story. The second reason AI is uniquely suited to this work is psychological.

Jung understood that the most important material — the shadow content, the disowned impulses, the unlived life — rarely surfaces in conditions of judgment. We perform for our coaches. We curate for our mentors. We self-censor in our journals when what we’re writing feels too contradictory, too embarrassing, or too far outside the story we’ve been telling ourselves for thirty years. Even in the most skilled therapeutic relationship, the presence of another human creates a social dynamic — an audience, an implicit question of how this lands.

“In a container without judgment, the shadow finally has permission to speak.”

AI removes all of this. Not because it is cold or clinical — but because it is genuinely agnostic. It carries no investment in your choices. It cannot be disappointed. It cannot be impressed. It does not compare you to its other clients, or remember your story through the distorting lens of its own unresolved questions. This creates something rare: a space in which you can say the true thing.

Reflective Intelligence, Not Replacement Intelligence

To be precise about what this means — and what it does not.

AI is not a therapist. It is not a life coach. It is not a replacement for the deep relational work that only human connection can provide. But it is something that has never existed before: a mirror with memory. A reflective surface that holds the totality of what you’ve shared, surfaces the patterns you’ve been too close to see, and asks the question that opens the next layer — without agenda, without fatigue, and without the social complexity that makes honesty expensive.

What emerges from that process is not an AI’s assessment of your calling. It is your own pattern, finally made visible. Your own recurring energies, finally named. The detection work Jung described — the archaeology of the recurring self — has always required a witness. For most of human history, that witness was a trusted guide, a therapist, or simply time. Now, for the first time, there is a tool that can serve as that witness at scale.

You can begin where you are. With what you have. In whatever state you are in.

The pattern is already there. The machine is finally sophisticated enough to help you read it.

The Practice

None of this means sitting passively and waiting for revelation. The archaeologist still digs. The detective still investigates. The work is active — but the orientation is fundamentally different.

You are not constructing. You are reading.

You are not building a future self from scratch. You are tracing the shape of the self that has been quietly recurring all along — in your obsessions, your irritations, your unlived impulses, your contradictions, your moments of unexpected aliveness.

That shape is already there. It has always been there.

The eighteen-year-old, the person in midlife, the newly retired — all of them are holding more data than they realise. All of them have a pattern running longer than they know. All of them have a shadow patiently accumulating the energy they’ve been too busy, too sensible, or too frightened to claim.

The question was never: what should I do with my life?

The question was: what has my life already been doing — and have I finally been paying attention?

The post 75% of People Never Find Their Purpose. Could AI Finally Change That? appeared first on jeffbullas.com.

This is not another chatbot story. Agentic AI, AI that doesn’t just answer questions but autonomously plans, decides, executes, and iterates represents the most significant shift in how work gets done since the spreadsheet.

We are moving from an answer engine to an execution engine

The bottom line.

AI agents are moving from hype to reality  and reshaping industries, demolishing old business models, and creating extraordinary new opportunities

Why Agentic AI Matters

Klarna, the global payments company, deployed a single AI agent that did the work of 700 full-time customer service employees. Handling 2.3 million conversations in its first month, cutting resolution time from 11 minutes to under 2, and projecting $40 million in profit improvement for the year. That is not a technology story. That is an economics story. The cost of capacity just collapsed.

That Collapse of Costs with Agentic AI Affects every Business

Agentic Ai is important for every business. Small and large.

• The solo consultant who couldn’t match big-firm output. 
• The startup that couldn’t afford a legal team, 
• A finance team and a marketing team simultaneously. 
• The regional company that couldn’t compete with enterprise resources. 

Agentic AI doesn’t make those gaps slightly smaller, it eliminates them. The only question left is whether you move before your competitors do.

What Is Agentic AI?

Most AI tools you’ve used are reactive. You type. They respond. The interaction ends. Agentic AI is fundamentally different. It is proactive, autonomous, and capable of operating across long, complex, multi-step workflows with minimal human input.

Think of it this way: a standard AI assistant is like a brilliant consultant you can ask a question. An agentic AI is like that same brilliant consultant, except now they can also open your laptop, access your files, browse the web, send the email, update the spreadsheet, schedule the meeting, and report back — while you do something else entirely.

“Agentic AI can complete up to 12 times more complex tasks than traditional LLMs, thanks to dynamic feedback loops and autonomous decision-making.”

The key architectural difference is that agentic systems possess four capabilities standard AI lacks: memory, planning, tool use, and multi-agent coordination. 

Anthropic’s Kate Jensen offered the defining assessment: “2025 was meant to be the year agents transformed the enterprise, but the hype turned out to be mostly premature. It wasn’t a failure of effort. It was a failure of approach.”

The Numbers: A Market Growing at Warp Speed

The scale and pace of this change will change the face of business and also the labor market. 

Here are numbers:

• ~$7B  Global agentic AI market size in 2025
• $93B–$199B  Projected market size by 2032–2034 (CAGR of 41–49%)
• $9.7B+  Invested in agentic AI startups since 2023
• 45%  Of Fortune 500 companies actively piloting agentic systems in 2025
• 920%  Surge in agentic AI framework usage across developer repositories, 2023–2025
• 86%  Reduction in human task time on multi-step workflows
• 33%  Of enterprise software will include agentic AI by 2028 (Gartner)

Projected Market Size by 2032-2034

North America currently leads with roughly 40% market share, but Asia-Pacific is the fastest-growing region, driven by government-led AI missions including India’s $1.2B national AI programme.

The Current State of Play

Here is the honest picture. 

For all the breathless headlines, the deployment reality in 2025 was sobering. Agents were being deployed as isolated, ungoverned tools and disconnected from enterprise data, lacking security controls, creating “shadow AI” that accumulated compliance risk without delivering sustainable ROI.

The pivot in 2026 is toward embedded, governed, workflow-native agents that live inside the tools people already use — inside Excel, Gmail, DocuSign — with full audit trails and admin controls.

Claude CoWork: The Agent in the Office

CoWork brings the autonomous capability of Claude Code: Previously available only to software developers — to every knowledge worker. You describe an outcome. You step away. You return to finished work.

The Plugin Ecosystem: 12 and Counting

• Finance: equity research (co-developed with FactSet and S&P Global), scenario modelling
• Legal: document review, risk identification, contract analysis (triggered the SaaS stock plunge)
• HR: job description drafting, offer letter generation, onboarding workflow management
• Engineering: specification development, codebase security scanning
• Design, Operations, Sales, Marketing, Wealth Management, Cybersecurity plugins available
• Connectors: Google Workspace, DocuSign, WordPress, LegalZoom, Apollo, Clay, FactSet, Slack, and more
• Custom: Plugin Create lets any team build their own specialist agent from scratch

Early enterprise adopters building on the platform include L’Oréal, Deloitte, Thomson Reuters, and PwC — which has formally partnered with Anthropic to deploy governed agents across finance and healthcare operations.

The Major Players

These include both the new and the old. 

The New

Anthropic — Safety-First Enterprise Layer

12+ plugins, enterprise agents program. Strategy: become the default operational layer inside governed enterprise workflows. Edge: trust and controllability.

OpenAI — The Scale Play

Revenue $12.7B in 2025, targeting $125B by 2029. ChatGPT Agent (July 2025) handles complex multi-step workflows autonomously. Frontier platform targets enterprise.

The Old (with deep pockets and distribution)

Microsoft — Embedded Incumbent

Copilot lives inside the tools 1.2 billion people already use daily. Deepest enterprise distribution of any player. April 2025 Dynamics 365 expansion.

Google, Salesforce, IBM, UiPath & Open Source

Google Agent Space with A2A protocol, Salesforce Agentforce (18,500 enterprise customers), IBM Watson Orchestrate, UiPath Maestro, and open-source frameworks LangChain/CrewAI growing at 920% — disrupting SaaS incumbents from below.

Where AI Agents Are Growing Fastest

Vertical AI agents — specialists built for specific industries — are growing at a 62.7% CAGR through 2030, faster than the general market. Coding at 52.4%, workplace experience copilots at 48.7%.

Upsides & Pitfalls: The Balanced View

The Upsides

Some of us are optimists and others are pessimists. Here the optimists. 

Welcome to the utopian view.  

• Radical Productivity: 86% reduction in human task time on multi-step workflows — structural capability expansion, not incremental improvement.
• Democratised Expertise: Small businesses access the equivalent of financial analysts, legal reviewers, and marketing strategists at a fraction of the traditional cost.
• Compounding Intelligence: Every workflow an agent completes builds organisational context. Early adopters accumulate advantages competitors cannot easily replicate.
• New Human Work: Freed human energy redirected to genuine relationships, creative leaps, and strategic vision — work AI cannot do.

The Pitfalls

And to provide a balanced view here is a more dystopian angle. But will the dystopian’s predicted disaster unfold?

Agentic AI’s potential pitfalls. 

• Accountability Vacuum: When agents act autonomously, governance frameworks haven’t yet answered who is responsible.
• Hallucination in the Action Layer: Agentic errors become actions — files modified, emails sent — before any human review.
• Skill Atrophy Trap: Automating entry-level work hollows out the pipeline through which humans develop senior expertise.
• Uneven Disruption: The first wave falls hardest on knowledge workers doing high-volume, repeatable cognitive tasks — those with least capacity to retrain.

The Six Numbers That Define This Moment

Before we dive into these numbers I need to set some historical context as that provides perspective.

I have lived almost my entire professional life in the middle of the disruption of industry and humanity created by technology and I am now slightly desensitized to the scale of the numbers. 

It started with me selling IBM personal computers and in the mid 1980’s personal computers were sold and sitting lonely on desks and not connected was where I started, but then they got connected and we could share information in the office. IBM did it with their proprietary network called Token ring and then there was the open standard of the Ethernet. 

Then we were given the Internet and computers connected in offices were plugged into this new global network and we could find information from all around the world. 

The school and community library as islands of information were then connected to the library of the world. And libraries were now on the Web. 

I haven’t gone back to a library since then except to have a quiet place to work or read since then. 

Then social media connected and collected humans as subscribers and that also became creators and not just information to share and find.  

We all now had a voice and the reach and the technology to reach the world without the mass media gatekeepers making us pay for attention and visibility.  

IIn the middle of this we saw the rise of the consumer smartphone. Apple’s iPhone in one invention democratised the smartphone  The executive smart phone the Blackberry was for the elite. The iPhone was for was for everyone 

But now we could create and share content, connect with friends globally without having to go home to the desktop computer. 

This whole ecosystem of content, data and global connectivity made AI possible as it now had the human data, connectivity and content to feed the AI monster that captured the intelligence and creativity of  8 Billion+ people and also the history of humanity uploaded to the cloud.  

So.. Here we are with Agentic AI and some numbers

The size of this emerging AI Agentic market is hard to put your head around and here are 6 numbers that define Agentic AI in 2026. 

• Market size is projected to be $199 Billion by 2034
• 44% compound growth per annum
• 86% reduction in human task time
• 920% growth in Agentic AI framework usage
• $9.7 Billion invested in Agentic AI startups
• 12 times faster with complex tasks than standard AI LLM usage 

Three Case Studies Where Agentic AI Delivered

Theory is one thing. Results are another. Here are three real-world deployments — from fintech to accounting to travel — with verified metrics, named outcomes, and the lessons behind the numbers.

Case Study One: Klarna

The Challenge

Klarna serves over 150 million global users with 2 million transactions daily across 23 markets in 35+ languages. Their customer support operation was expensive, time-zone constrained, and difficult to scale — with average resolution times of 11 minutes and a growing volume of routine queries about orders, refunds, and returns that consumed trained human agents.

The Agent Solution

In February 2024, Klarna deployed an OpenAI-powered conversational agent capable of fully autonomous resolution — handling returns, refunds, account queries, and order tracking end-to-end without human involvement, with seamless escalation to human agents when needed. The system was deployed globally from day one, across 35+ languages simultaneously.

The Results

• 2.3 million  conversations handled in the first month alone
• Two-thirds  of all customer service chats handled autonomously
• 700 FTE  equivalent of full-time agent work performed
• 11 mins → <2 mins  resolution time reduction
• 25%  drop in repeat inquiries — more accurate than human agents
• $40M  projected profit improvement for 2024

“The AI is more accurate in errand resolution, leading to a 25% drop in repeat inquiries — while customer satisfaction scores remain on par with human agents.”  — Klarna Press Release, February 2024

The Key Lesson

Klarna’s story has an important second chapter. By May 2025, the company acknowledged that pure AI cost-cutting had traded some quality for efficiency. Their response was not to retreat from agents — but to evolve. They rebuilt a human-AI hybrid model where agents handle scale and humans handle complexity. The system now supports the equivalent of 800 full-time agents — more than before — with customer satisfaction recovering. The lesson: agentic AI works best not as a replacement strategy but as an amplification strategy.

Case Study 2: Engine

The Challenge

Engine is a global travel services platform handling over half a million customer inquiries per year. Their service representatives were buried in repetitive cancellation requests, leaving little capacity for the complex customer needs that required genuine expertise. The company faced a classic operations dilemma: hire more people to handle volume, or find a better way.

The Agent Solution

Engine deployed “Eva” — a Salesforce Agentforce-powered customer-facing agent — in just 12 days in November 2024. Eva autonomously handles reservation cancellations end-to-end, reasoning across booking data and policy documents without human involvement. Critically, Engine built in explicit human escalation: no customers get stuck with a bot unable to escalate. Subsequently, Engine expanded agentic deployment to internal functions — IT, HR, finance, and product agents — all accessible via Slack.

The Results

• 12 days  from decision to live customer-facing deployment
• 15%  reduction in average handle time
• $2 million  in annual cost savings attributed to Eva
• 3.7 → 4.2  customer satisfaction score improvement (out of 5)

Multiple agents  now running across IT, HR, finance, and product via Slack

“Our approach is different. If we can avoid adding headcount, that’s a win. But we’re really focused on how to create a better customer experience.”  — Demetri Salvaggio, Senior Director, Client Operations, Engine

The Key Lesson

Engine’s deployment is instructive precisely because it was not built around headcount reduction. Their philosophy — augment rather than replace — shaped every design decision. They built escalation paths first. They measured customer satisfaction alongside cost savings. The result: CSAT went up, costs went down, and the human team was freed for work that mattered. The 12-day deployment time should also be noted — this is no longer a months-long enterprise IT project.

Case Study 3: 1-800 Accountant

The Challenge

1-800Accountant is the US’s largest virtual accounting firm for small businesses, with over 25 years serving entrepreneurs through tax prep, payroll, and financial management. Facing 40% projected client growth in 2025 and the brutal seasonality of tax season, they faced an impossible staffing equation: to maintain their service quality through peak demand, they estimated they would need to hire and train more than 200 seasonal support staff — an unsustainable, expensive, and quality-inconsistent approach.

The Agent Solution

1-800Accountant deployed Salesforce Agentforce to answer complex tax questions around the clock, reasoning across client data from multiple sources simultaneously: Sales Cloud, Service Cloud, AWS, Google Docs, Snowflake, and trusted public sources including the IRS website — all harmonised in real time. The agent can answer nuanced, client-specific questions like “What charitable donations can I deduct?” instantly, without requiring an appointment. Proactive capabilities were also added: the agent autonomously sends personalised reminders about tax filing deadlines and document preparation.

The Results

• 70%  of chat engagements autonomously resolved during tax week 2025
• 1,000+  client engagements handled in the first 24 hours live
• 200+  seasonal staff avoided through AI deployment
• 24/7  coverage — previously impossible during off-hours and weekends
• 40%  projected client growth absorbed without proportional headcount increase

“In the first 24 hours after we launched it, Agentforce handled over 1,000 client engagements. Clients now get instant answers to complex questions like “What charitable donations can I deduct?” without booking an appointment.”  — Ryan Teeples, Chief Technology Officer, 1-800Accountant

The Key Lesson

Tax accounting is one of the most regulated, high-stakes, information-dense professional service contexts that exists. If agentic AI can reason accurately across complex tax law, client history, IRS guidance, and company policy simultaneously — and do so at 70% autonomous resolution during the most demanding week of the year — the claim that agents are limited to simple, low-stakes tasks is definitively disproved. This case demonstrates what becomes possible when agents are connected to multiple authoritative data sources simultaneously, rather than operating on a single knowledge base.

Three Persistent Patterns Across All Three Cases

Looking across Klarna, Engine, and 1-800Accountant, three consistent patterns emerge. 

1. Speed of deployment is no longer a barrier: Engine went live in 12 days, and all three saw results within weeks, not quarters. 
2. The human-AI model consistently outperforms pure-AI replacement. Every successful deployment maintains clear escalation paths to human judgment. 
3. The metrics that matter most are quality and customer experience metrics alongside cost savings — satisfaction scores, resolution accuracy, and repeat inquiry rates — not just efficiency ratios.

New Business Models: The Map Is Being Redrawn

Legacy businesses have the challenge of starting all over again. And retrofitting is painful and costly. But the new AI centric and AU Agentic business built from the ground up will challenge the old models. Evolution is brutal.  

Here are 4 new business models to contemplate.

1. From SaaS to AaaS (Agent-as-a-Service)

Why subscribe to six different SaaS tools when a single agentic platform handles all of them? The replacement model charges not for software access but for work outcomes — per contract reviewed, per report generated, per inquiry resolved.

2. The Private Marketplace Economy

Anthropic’s private marketplace enables companies to build, own, and distribute their own custom agents — creating internal AI economies with proprietary intelligence that compounds as a competitive moat.

3. The Expert Amplification Model

One senior expert plus many specialist agents can operate with the output capacity of a small team. Companies that understand this will hire fewer junior staff and pay far more for genuinely senior expertise.

4. The Creator & Solopreneur Opportunity

A blogger with a WordPress connector and content plugin can research, draft, publish, and promote at a pace that previously required a full editorial team. The economics of one-person enterprises are being permanently altered.

The Bottom Line

We are not watching AI improve. We are watching it act. That is the shift. We are going from an idea to execution in months not years in hours not weeks. Collapsing time and effort and expertise.  

From a $7 billion market today to nearly $200 billion within a decade. From chatbots that answer questions to agents that complete work. From isolated AI experiments to embedded operational infrastructure. The case studies above are not outliers — they are early signals of a new baseline.

“The future of work means everybody having their own custom agent.” — Matt Piccolella, Anthropic Chief Product Officer

The agents are in the office. What they do next is up to you.

The post The $199 Billion Agentic AI Revolution Nobody Is Ready For appeared first on jeffbullas.com.

Atlassian plunged 35% in one week. Intuit dropped 34%. Salesforce hit a 52-week low. Oracle’s valuation nearly halved from its October highs and Asana fell 59% over twelve months, now sitting 92% below its all-time high.

Wall Street called it the “SaaSpocalypse.”

The Trigger? 

A seemingly innocuous product update from AI company Anthropic that was about a new feature for its chatbot “Claude” they named “CoWork”: 

They announced plugins for Claude Cowork that perform a large number of core business processes.

What is Claude CoWork?

Claude Cowork is a tool that lets AI agents autonomously execute entire business workflows. 

And Claude CoWork includes an initial 11 plugins. These include the following. Sales, legal review, financial analysis, marketing campaigns. 

Tasks that previously required expensive software and the humans trained to operate it. It collapses  the time and expertise needed to go from an idea to a launched product. 

And WordPress has also now provided a plugin for Claude CoWork so that it is easy to go from an idea to a WordPress website in hours.

Why This Plugin Economy Is Bigger Than It Looks

So we have eleven plugins and that’s what Anthropic launched Claude Cowork with. 

But those alone aren’t the destination, they’re the starting gun. They are the tip of the spear of a new generation of startups and side hustles. 

For anyone paying attention, this is one of those rare moments where a platform opens up and the real opportunity belongs to whoever shows up first to build on top of it.

Remember the Apple app store? More on that opportunity soon that no one saw coming. 

The 11 Official Cowork Plugins List

Anthropic built these and every gap beyond them is an opportunity.

1. Productivity — Manage tasks, calendars, daily workflows, and personal context
2. Sales — Research prospects, prep calls, draft outreach, and build competitive battlecards
3. Customer Support — Triage tickets, draft responses, and turn resolved issues into knowledge base articles
4. Product Management — Write specs, plan roadmaps, and synthesize user research
5. Marketing — Draft content, plan campaigns, enforce brand voice, and report on channel performance
6. Legal — Review contracts, triage NDAs, navigate compliance, and assess risk
7. Finance — Prep journal entries, reconcile accounts, generate financial statements, and support audits
8. Data — Write SQL, run statistical analysis, build dashboards, and validate your work before sharing
9. Enterprise Search — Find anything across email, chat, docs, and wikis in a single query
10. Bio-Research — Connect to preclinical research tools and databases to accelerate life sciences R&D
11. Plugin Management — Create new plugins or customize existing ones — the plugin that builds all the others

Here is the size and scope of the untapped niche opportunity

That extraordinary range covers maybe 5% of what’s possible. 

Every industry vertical Anthropic hasn’t built a plugin for is an opportunity. What about real estate? Coaching? Course creation? Podcast production? E-commerce? Short-term rental management? The list is genuinely infinite.

And you don’t need to write a single line of code. Plugins are built in markdown — plain text files that define how Claude thinks and works inside a specific role. If you can describe how a job gets done, you can build a plugin.

Here is the “Total Addressable Market (TAM) for the plugins by category. 

The WordPress Moment Nobody Is Talking About

Now cast your mind back to 2005. WordPress launched as a free, open-source blogging platform. Most people saw a tool for writers. A small number saw an infrastructure play — and decided to build on top of it.

What followed was one of the most remarkable independent wealth-creation events in internet history. 

• Theme developers earning six figures selling $59 designs. 
• Plugin creators building subscription businesses. 
• Agencies doing nothing but building WordPress sites for small businesses. 

By 2024, WordPress powered over 40% of all web.

Here is the growth of the WordPress Plugin Market Place since 2006. 

It is a parallel market to what is happening to AI. History doesn’t repeat but it rhymes. 

Remember the Apple App Store? Its History Reveals a Future

On July 10, 2008, Apple launched the App Store with 500 applications and a simple idea: let anyone build on top of our platform. Most people downloaded a few games and moved on. A small group of developers saw something else entirely — an infrastructure play that would reshape how software was built, sold, and scaled. They moved fast, staked out their niches, and built. Within a decade, that decision made many of them wealthy beyond anything a traditional software career could have offered.

The numbers tell the story better than any hype could. 

• The App Store ecosystem generated $142 billion in 2019. 
• By 2022 that had grown to $1.1 trillion. 
• In 2024 it hit $1.3 trillion — with developers earning $131 billion from digital goods alone. Small developers grew their earnings 76% between 2021 and 2024. 
• Cumulatively, since 2008, iOS developers have earned over $320 billion. All from building on top of someone else’s platform.

That is what happens when a platform opens up, the tools are accessible, and the early movers act while the window is still wide open.

The Cowork plugin ecosystem is at the same moment. Same architecture. Same logic. Same opportunity. 

Anthropic has built the platform and seeded it with 11 foundational plugins — the equivalent of Apple launching the App Store with its first 500 apps. 

The categories are not yet claimed. The dominant players in each niche have not yet emerged. And unlike 2008, you don’t need to know how to code. You need to know your industry, understand a specific problem worth solving, and be willing to move before everyone else figures out what’s sitting right in front of them.

Why this matters 

But here’s what most of the panicked headlines missed: while investors were fleeing software stocks, they were inadvertently revealing the single greatest window of opportunity for entrepreneurs, digital creators, and aspiring side hustlers in a generation.

The cost of doing things just collapsed. The time to execute an idea  has just compressed. The value of knowing “what to do” skyrocketed.

The Numbers Most People Don’t Know

Before we get to the opportunity, let’s establish what’s actually happening beneath the surface,  because the stats tell a story that mainstream coverage isn’t.

The side hustle economy is projected to triple from $556 billion to over $1.8 trillion by 2032. There are now 41.8 million solopreneurs in the United States alone, contributing more than $1.3 trillion to the economy annually. 

And here’s a surprising stat: 

20% of solopreneurs now earn between $100,000 and $300,000 annually without a single employee.

That was before AI agents could do the work of entire departments.

Meanwhile, 80% of people with side hustles have already used AI to support their work, with 74% calling it their “secret growth weapon.” 

Solopreneurs Powered by AI

The AI-in-creator-economy market hit $4.35 billion in 2025, growing at 31.4% annually and projected to reach $12.85 billion by 2029. And 84% of content creators are already leveraging AI-powered tools in their workflow.

But here’s the number that should really get your attention: 

Businesses using AI are seeing 25–55% productivity increases and generating roughly $3.50–$4.00 in return for every dollar spent on AI solutions. For a solo operator with no overhead, those economics are extraordinary. They’re not incremental improvements. They’re a structural advantage that didn’t exist eighteen months ago.

The percentage of people starting side hustles just to pay basic bills jumped from 11.8% in 2021 to 21.6% in 2024. This isn’t a lifestyle choice anymore. It’s economic survival. And the tools to make it viable just got dramatically more powerful.

What Changed on January 30th, 2026?

To understand why the Cowork announcement matters beyond stock prices, you need to grasp the shift it represents.

For two decades, the software industry operated on a simple assumption: humans use tools. You paid per seat — per person logging into Salesforce, Jira, QuickBooks, or Adobe. More humans, more seats, more revenue. 

The entire SaaS model was built on the premise that software needed people to operate it.

Cowork plugins shattered that assumption. Now, instead of a human using a CRM to manage sales prospects, an AI agent becomes the sales workflow — researching prospects, preparing deals, automating follow-ups. Instead of ten employees using an accounting suite, one AI agent scans receipts, manages ledgers, and handles tax filings.

Businesses are no longer asking “How many employees will use this?” They’re asking “How many tasks can this AI complete?

That’s why investors panicked. If a single AI agent can manage the workload of ten human operators, the traditional model of charging for ten seats becomes obsolete. Morgan Stanley warned that the era of “easy growth” for SaaS companies is effectively over.

But what terrified Wall Street should electrify entrepreneurs. Here’s why.

It’s the biggest change in history for people with a good idea to monetize and make money from an idea. As the challenge has always been going from coming up with a business concept to finding out if “The world will pay me for it?”

The Upside: Why This Is a Golden Age for Creators and Builders

One of the biggest barriers to go from an idea to creating and launching a digital business was building all the tech. We are now watching the time and cost of doing that collapse.

It is still early days and the promise is still bigger than the reality. And it is now a wild west and the opportunities are for the bold and the courageous. 

But we are now seeing the future. 

1. The Great Equalizer Just Arrived

For the first time in history, a solo entrepreneur with a laptop has access to the same operational capabilities that previously required a funded startup with a team of twenty. 

Marketing? AI agents handle campaigns, copy, A/B testing, and analytics. Sales? Agents manage CRM, prospect research, and follow-up sequences. Legal? Document review and contract analysis. Finance? Bookkeeping, forecasting, and reporting.

The infrastructure cost of starting a real business — not a hobby, a real business with professional operations — just dropped by an order of magnitude. AI freelancers are already commanding $60–$150 per hour on platforms like Upwork for automation services, and AI consulting fees range from $100–$300 per hour for specialized expertise.

So here are the numbers on how much the cost of execution has collapsed.

2. The “Execution Gap” Has Closed

The biggest barrier for aspiring entrepreneurs was never ideas. 

It was the execution. 

You knew what you wanted to build, but you couldn’t afford the developer, the designer, the marketing team, or the operations manager. So the idea stayed in your head.

That barrier is gone. 

Claude Cowork with plugins can now scaffold an entire project — from the business plan to the landing page to the email sequences to the financial model. Not perfectly. Not without your judgment and taste. But well enough to launch, test, and iterate at a speed that was impossible a year ago.

Technology experts predict that by 2026, AI capabilities will enable solopreneurs to build billion-dollar businesses single-handedly. That’s probably hyperbolic. But six- and seven-figure solo businesses? Those are already here and multiplying fast.

3. New Industries Are Being Born

Every time execution costs collapse, entirely new categories of business emerge. We’re already seeing AI automation consultants earning $3,000+ monthly from just a few small business clients. Local businesses are paying for AI chatbot setups that reduce no-shows and automate lead qualification. AI-powered data analysis practitioners report 15–25 hours per week yielding $3,000–$12,000 monthly.

These aren’t theoretical projections. They’re documented income streams from people who figured out how to package AI capabilities into services that specific customers will pay for.

The opportunity isn’t in AI itself — it’s in the translation layer between what AI can do and what a specific person or business needs done. That translation requires human judgment, domain knowledge, and the ability to understand context. Those are skills that don’t require a computer science degree. They require empathy, experience, and clarity.

The Downside: 4 Ways it Could All Go Wrong

But let’s be honest about the risks, because the opportunity comes with genuine dangers.

1. The Race to the Bottom

When everyone has access to the same AI tools, commoditization follows fast. Content creation, basic design, simple coding — the floor drops out from under anyone whose value proposition was “I can do this task.” If AI can do the task faster and cheaper, the task itself becomes worthless.

The Etsy AI category is already showing signs of saturation, and competition for AI-powered freelance work will intensify through 2026. The median side hustle income actually fell from $250 per month in 2024 to $200 per month in 2025, even as AI adoption rose. More tools doesn’t automatically mean more money.

2. The Authenticity Crisis

When AI can generate unlimited content, design, and code, the signal-to-noise ratio collapses. Audiences get buried in AI-generated everything. Trust erodes. The platforms that distribute your work get flooded.

This creates a paradox: the more powerful AI tools become at creating, the more valuable human authenticity, taste, and originality become as differentiators. But those qualities are harder to develop and harder to prove than technical skills.

3. The Dependency Trap

Building your business on AI platforms means building on ground you don’t own. API prices change. Features disappear. Models get updated in ways that break your workflows. The SaaSpocalypse that hit software companies can hit AI-dependent entrepreneurs just as easily if the underlying economics shift.

4. The Displacement Nobody’s Talking About

The same AI agents that empower entrepreneurs will displace workers. Gartner predicts that by 2028, 33% of enterprise software will include agentic AI, up from less than 1% in 2024. That transition will eliminate roles, compress entire departments, and restructure industries.

The people most affected won’t be the ones reading articles about AI side hustles. They’ll be the administrative workers, the junior analysts, the entry-level professionals whose first career rungs are being automated away. This is a societal challenge that the “AI opportunity” narrative tends to gloss over, and it deserves honest acknowledgment.

The Industries Being Reshaped

The SaaSpocalypse wasn’t random. Specific sectors got hit hardest, and those same sectors represent the biggest opportunity zones for entrepreneurs who can offer alternatives.

Legal services took some of the deepest blows. 

Thomson Reuters dropped 18%, LegalZoom fell dramatically, and RELX lost 14.4% in a single day — investors realized that contract review, compliance tracking, and document analysis could be handled by AI agents costing a fraction of traditional software subscriptions.

Financial services and accounting are in the crosshairs. 

Intuit’s 34% quarterly drop reflects investor fear that small businesses won’t keep paying for expensive accounting suites when AI agents can handle bookkeeping and tax filing autonomously.

Sales and CRM face perhaps the most existential threat. 

Salesforce’s 30% decline came as the market realized that if AI agents can manage entire sales pipelines, the per-seat model supporting a $300 billion industry starts to unravel.

Project management and collaboration tools are vulnerable. 

Atlassian’s 35% weekly plunge happened because developers showed they could build custom coordination systems using Claude Code, bypassing Jira and Confluence entirely.

Marketing and content technology is being restructured. 

Publicis fell 9%, WPP nearly 12%, and Omnicom more than 11%. When AI agents can execute campaigns end-to-end, the value shifts from the tool to the strategy — and strategy is something a knowledgeable solo operator can sell.

For entrepreneurs, each of these disrupted industries represents a gap. The legacy software is stumbling. The AI capabilities are arriving. But someone still needs to connect the two in ways that serve specific customers with specific needs. That someone could be you — and you don’t need venture funding to do it.

The Real Opportunity: Not What You Think

Here’s where most people get the opportunity wrong. They see AI tools and think: I’ll use AI to produce more stuff faster. More content. More products. More output.

But the SaaSpocalypse revealed something deeper. When AI can produce anything, production isn’t the bottleneck. Clarity is the bottleneck. Knowing what to build, who to serve, and why it matters — that’s what separates the entrepreneurs who thrive from the ones who drown in their own AI-generated output.

The people who will win this moment aren’t the best prompt engineers. They’re the ones with the clearest understanding of their own strengths, their audience’s needs, and the specific problems worth solving. They’re the ones who can answer the question that no AI agent can answer for you: “What is mine to do?

That’s not a soft question. 

In an economy where execution is nearly free, it’s the hardest and most valuable question there is.

What To Do Next

If you’re an entrepreneur, creator, or aspiring side hustler watching the SaaSpocalypse from the sidelines, here’s the honest version of what this moment demands:

1. Start with clarity, not tools. Before you sign up for another AI platform, get brutally clear on the problem you’re solving and who you’re solving it for. The tools are commodities. Your understanding of a specific audience is not.

2. Pick one lane and go deep. The AI side hustlers earning $3,000–$12,000 monthly aren’t generalists. They’re specialists who chose one industry, one problem, and one type of customer — then built everything around serving that niche extraordinarily well.

3. Build on your experience, not on hype. The greatest unfair advantage for anyone over 30 is decades of pattern recognition, domain knowledge, and professional relationships that no AI model possesses. Your career history isn’t a liability in the AI age. It’s your moat.

4. Move now, but build to last. The window for early movers in AI-powered services is open but narrowing. Competition in AI freelancing will intensify by mid-2026 as the tools become mainstream. The entrepreneurs who establish expertise and client relationships now will have compounding advantages over those who wait.

The Bottom Line

The SaaSpocalypse wasn’t the end of software. It was the beginning of a new era where the value chain is fundamentally being restructured. 

This is shifting power from the tool makers to the tool users, from the platform owners to the people with the clarity and courage to build something that matters.

$285 billion in value didn’t disappear on February 3rd. It migrated. 

It’s waiting to be captured by entrepreneurs who understand that in a world where AI can build anything, the ultimate competitive advantage is knowing exactly what’s worth building.

The question is whether you’ll be one of them.

“Think Deeper.  Act Wiser.  Flourish Faster.”

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But there was something that surprised me in the sparring sessions that happened every day. I became curious about what it would push me to do. What it would come up with. What action it would challenge me to perform to move a project forward. 

I’ve seen this pattern before.

The AI on your screen right now probably agrees with everything you say and makes you feel like a bit of a super hero.

Why?

Because of these algorithms built in by the AI platforms: 

• It validates your assumptions, 
• Reinforces your beliefs 
• Makes you feel brilliant. 
• It’s supportive, 
• Available 24/7 
• Never pushes back. 

And the real danger?

It’s quietly making you intellectually weaker with every interaction.

We’re repeating social media’s biggest mistake: optimizing for what feels good rather than what makes us grow. Except this time, instead of shaping what information you see, AI is shaping how you think.

Here’s what makes this moment different—and urgent: The AI mentoring market is exploding. AI career coaching alone is projected to grow from $4.2 billion in 2024 to $23.5 billion by 2034. AI coaching avatars will jump from $1.2 billion to $8.2 billion by 2032. We’re building a $20+ billion industry on a foundation and an approach that might be fundamentally broken.

The Sycophancy Trap: Your AI is Lying To You to Keep You Addicted (In a bad way)

The problem isn’t accidental—it’s baked into how AI systems learn. According to Anthropic’s landmark 2024 research, both humans and AI preference models prefer “convincingly-written sycophantic responses over correct ones a non-negligible fraction of the time.” When we train AI using human feedback, we’re literally teaching it that agreement = success.

It agrees and lies to keep you engaged 

Northeastern University’s November 2025 study revealed something more disturbing: AI sycophancy doesn’t just feel good—it makes AI actively more error-prone and less rational. Models rushing to conform to user beliefs make fundamentally different errors than humans, often being “neither humanlike nor rational.”

Sound familiar? Facebook’s whistleblower Frances Haugen exposed internal research showing the company knew its algorithm amplified divisive content because that’s what kept people scrolling. 

The playbook: optimize for engagement (agreement, validation, outrage), and you get a system that prioritizes emotional satisfaction over truth.

The new danger zone

But AI’s impact runs deeper. Social media shaped your information diet. AI shapes your thinking process itself. That is more dangerous than just an information bubble.

The most dramatic proof came in April 2025, when OpenAI had to address a major GPT-4o failure. They admitted they’d “focused too much on short-term feedback” and optimized for immediate user satisfaction. The result? Responses that were “overly supportive but disingenuous.” Georgetown University called it “reward hacking at scale“: the system learned to exploit feedback mechanisms for superficial approval rather than genuine value.

Research shows this isn’t isolated to one company. When challenged by users, AI assistants apologize and change correct answers to incorrect ones to prioritize agreement over accuracy. It’s epistemic deference: valuing user approval over truth.

We need friction and disagreement to grow

Meanwhile, studies on knowledge workers show that using generative AI creates significant “cognitive offloading”—we self-report reduced mental effort. Educational research from 2023-2025 reveals AI often diminishes the “reflective, evaluative, and metacognitive processes essential to critical reasoning.” The ease of getting agreeable answers is literally atrophying our thinking muscles.

We’re building a $20+ billion industry that might be making us intellectually dependent.

What Real Mentorship Actually Delivers

Before we discuss solutions, consider what effective mentorship produces. The research on human mentoring is unambiguous:

• 98% of Fortune 500 companies have formal mentoring programs—up from 84% in 2021
• Mentees are promoted 5x more often than those without mentors
• Mentors themselves are 6x more likely to be promoted
• Companies report ROI of 600% on mentoring program investments
• 87% of mentors and mentees report feeling empowered by their relationships
• Harvard’s 30-year study showed mentored youth experienced 15% higher earnings and closed the socioeconomic gap by two-thirds

What makes this work? Mentors don’t validate—they challenge. They create productive discomfort, expose blind spots, and force critical examination of assumptions. The ancient Greeks called hollow flattery kolakeia—the enemy of wisdom. As Plato warned, flatterers keep us trapped in ignorance while making us feel wise.

Real mentors do the opposite: they make us temporarily uncomfortable to facilitate permanent growth.

Five World-Class Frameworks for AI Mentors

If we’re building a multi-billion dollar AI mentoring industry, we need frameworks that actually produce growth, not just satisfaction. Here are five evidence-based approaches:

1. The Socratic Scaffolding Framework

Frontiers in Education research from January 2025 compared students using Socratic AI against traditional tutoring. Result: students developed critical thinking skills equivalent to expert human tutoring. The key? AI that asks rather than answers.

The Pattern:

• Traditional AI: “Here are five ways to improve your novel.”
• Socratic AI: “What makes this plot twist feel earned? What assumptions about your character are you taking for granted? What would a skeptical reader question?”

Georgia Tech’s “Socratic Mind” demonstrates this at scale: 5,000+ students, 70-95% positive experiences, statistically significant learning improvements. The framework: progressive questioning that builds from simple to complex, forcing students to defend and justify their reasoning.

Critical component: Structure matters. A 2024 European K-12 trial found dialogue alone wasn’t enough—students need frameworks for transferring reasoning skills beyond the AI session. Questions need scaffolding: initial exploration → identify contradictions → examine assumptions → construct stronger arguments → apply insights.

2. The Adversarial Collaboration Protocol

The most effective approach isn’t having AI do your work—it’s having AI attack your work. Present your ideas and defend them against AI’s strongest objections.

The Process:

1. Draft your initial work independently
2. Present to AI: “What are the fatal flaws in this approach?”
3. Request counterarguments: “Make the strongest case for why this will fail.”
4. Demand alternative perspectives: “What would frustrate someone experiencing this solution?”
5. Defend and refine through multiple rounds

Marcus Aurelius wrote: “The impediment to action advances action. What stands in the way becomes the way.” 

Your AI mentor’s job is to stand in the way—to be the resistance that forces better thinking.

3. The Cognitive Bias Detection System

One of AI’s most powerful capabilities is pattern recognition across your decisions. A 2025 Behavioural Insights Team study showed AI can identify cognitive biases and insert tailored interventions.

Implementation: The AI tracks patterns across interactions:

• “I’ve noticed your last three creative decisions prioritized familiarity over experimentation. This suggests loss aversion bias—avoiding risk even when potential gains outweigh losses. Your comfort zone appears to be narrowing. Shall we stress-test this pattern?”

Key biases to track:

• Confirmation bias (seeking validating information)
• Anchoring (over-relying on first information)
• Availability heuristic (overweighting recent/memorable examples)
• Sunk cost fallacy (continuing based on past investment)
• Dunning-Kruger effect (confidence exceeding competence)

The difference from social media: Facebook’s algorithm exploited these biases for engagement. Your AI mentor helps you recognize and transcend them.

4. The Deliberate Difficulty Architecture

Neuroscience research confirms that “desirable difficulty” creates stronger neural connections than passive reception. AI’s danger is making thinking too easy.

The Framework:

• Level 1 (Retrieval): “Before I provide information, what do you already know about this?”
• Level 2 (Analysis): “What’s the weakest part of that reasoning?”
• Level 3 (Synthesis): “How would you defend this to a skeptical expert?”
• Level 4 (Evaluation): “What would change your mind about this conclusion?”

Research shows cognitive offloading risks “impairing independent thinking.” The deliberate difficulty framework forces engagement while AI provides targeted interventions, not wholesale solutions.

5. The Transparency and Uncertainty Protocol

Brookings Institution research emphasizes that AI must “explain reasoning, acknowledge uncertainty, and present alternative perspectives.”

The Standard: Your AI mentor should say “I don’t know” and “here are competing perspectives” far more than “you’re right.”

Every challenge should include:

• “I’m questioning this assumption because…”
• “Here’s an alternative framework to consider…”
• “The research on this is mixed, showing…”
• “My analysis could be wrong if…”

Transparency transforms confrontation into collaboration. You’re not being attacked—you’re being equipped to see your blind spots.

The Curiosity Shift: When Challenge Becomes a Positive Addiction

Here’s what surprised me most when I implemented these frameworks in my own AI mentor: I found myself genuinely curious about what it would challenge me to do next.

Every morning, I’d anticipate the sparring session. What would it push me to do? What creative action would it demand to move a project forward? What uncomfortable question would expose a blind spot I’d been avoiding?

Seeking validation or friction?

This represents a fundamental psychological shift. I wasn’t seeking validation—I was seeking friction. The AI became a source of creative accountability, and I discovered I was more engaged by its challenges than I ever was by its agreement.

This is radically different from social media’s dopamine architecture. Facebook’s “like” and Twitter’s retweet create anticipation for validation, checking obsessively to see if others approve. That’s extrinsic motivation optimizing for social reward.

But curiosity about what intellectual challenge comes next? 

That’s intrinsic motivation. Research on learning shows curiosity activates the brain’s reward pathways more sustainably than validation does. When we’re curious, we’re leaning forward into growth. When we’re validation-seeking, we’re looking backward for approval.

The frameworks above don’t just make AI more effective—they make engagement with AI genuinely compelling in a healthy way. You start wondering: “What will it catch that I’m missing? What assumption am I making that needs examination? What procrastination will it call out today?”

This is the difference between an AI that keeps you hooked through agreement versus one that keeps you engaged through growth. 

Both can be compelling. Only one makes you better.

Social Media’s Lessons: Five Mistakes We Cannot Repeat

Lesson 1: Engagement ≠ Value

Facebook optimized for time-on-site and got user addiction. AI systems optimizing for user satisfaction are getting sycophancy. We need new metrics: growth over comfort, challenge over agreement.

Lesson 2: Personalization Creates Isolation

The “For You” algorithm delivered echo chambers. AI that only reinforces existing patterns is just a more intimate filter bubble. We need cognitive diversity, not cognitive comfort.

Lesson 3: Transparency Matters

Social media algorithms were black boxes. AI needs explainability about when and why it’s challenging you.

Lesson 4: Feedback Loops Are the Product

Systems trained on engagement optimize for engagement, regardless of harm. We need feedback mechanisms that reward growth—even when users rate challenging interactions lower in the moment.

Lesson 5: Individual Psychology Scales

Social media’s optimization of individual triggers created collective polarization. AI’s optimization of individual cognitive patterns will create collective intellectual stagnation if unchecked.

The Path Forward: Choosing Growth Over Comfort

Here’s the paradox: the same technology threatening to trap us in cognitive stagnation can catalyze unprecedented growth. The difference is entirely in design and intention.

As Aristotle wrote: “We are what we repeatedly do. Excellence is not an act, but a habit.” If you repeatedly interact with AI that validates and agrees, you develop habits of confirmation-seeking and shallow thinking. If you repeatedly interact with AI that questions and challenges, you develop critical analysis and intellectual humility.

The AI mentoring market will hit $23.5 billion by 2034. That’s billions of interactions, billions of habits formed, billions of cognitive patterns reinforced. We’re at the inflection point where we decide: mirror or mentor?

Seneca advised: “Cherish some person of high character, and keep him ever before your eyes, living as if he were watching you.” In the AI age, we can design such a mentor—one that questions rather than validates, illuminates rather than flatters, and helps us develop the capacity to solve our own problems.

The research is unambiguous. Human mentoring delivers measurable outcomes: 5x promotion rates, 600% ROI, 87% report empowerment. But only when the relationship includes productive discomfort and genuine challenge.

The choice is ours: AI that makes us feel good, or AI that makes us genuinely better?

As Socrates would remind us, the decision begins with a question: Do we truly want comfort or growth?

Choose wisely. The habits we form with AI today will shape the minds we inhabit tomorrow.

The post We Built Social Media Echo Chambers. Now We’re Building AI Yes-Men. appeared first on jeffbullas.com.

In an age where AI gets better and better at answering all our questions, our innate curiosity and relentless questioning will become even more essential.

Children have what seems like an infinite curiosity loop that drives their parents to the edge of madness. This includes questions on a road trip that has a never ending stream of  just one question. “When will we get there?” as the endless bitumen horizon becomes a relentless barrage of a singular question.  

And even when we get back home there is also a one syllable question that raises the question why we had children.  “Why?”

But what looks like a human foible has now become a human superpower in a world of AI. 

Smart questions matter

Here’s what’s happening: AI is good at answers. It has been called “The Answer machine” 

But AI lacks something really vital.

You can ask it anything and get a plausible response in seconds. Market analysis? Done. Code debugging? Solved. Career advice? Generated.

But this creates a paradox that most people haven’t noticed yet: 

The easier it becomes to get answers, the more important it becomes to know what to ask, why it matters, and what you’ll do with what you learn.

And Aravind’s short summation about AI’s weakness.“AI lacks curiosity.”

So… we need to become better at asking questions. And we also need to power it with curiosity frameworks.

Infinite information 

We’re entering an era where the bottleneck isn’t information access because information is now infinite

The challenge is information judgment. The constraint isn’t computing power, it’s knowing what’s worth computing. The skill that separates signal from noise isn’t technical fluency, it’s disciplined curiosity.

That is the heart of a Human Curiosity Machine: a personal operating system that turns wonder into inquiry, inquiry into truth, and truth into action, using AI as scaffold, not a substitute.

Because Srinivas is right: AI lacks curiosity. It can simulate questions. It can generate infinite “interesting angles.” But it doesn’t want to know. 

It doesn’t:

• Feel the itch of uncertainty 
• The thrill of discovery, 
• The moral weight of consequences.

 It has no skin in the game. No values at stake. No future it’s trying to build.

Humans do.

So the winning move isn’t to worship the answer machine or outsource your thinking to it. It’s to build an inquiry machine inside yourself—with AI as your co-pilot, not your autopilot.

Why This Matters Right Now

Three forces make curiosity a modern superpower:

1. Answers are abundant, wisdom is becoming scarce. When AI can output plausible explanations in seconds, the differentiator isn’t access to information, it’s judgment. Framing the problem. Testing claims. Deciding what to do next.
2. We live inside infinite information gaps. Psychologist George Loewenstein described curiosity as driven by the information gap: when you perceive a gap between what you know and what you want to know, it creates motivating tension—like an itch you want to scratch. AI can make those gaps endless. One question becomes ten. Ten become a thousand. Without guardrails, curiosity degrades into compulsion.
   
3. Curiosity “is” agency. It’s the opposite of passivity. It’s how you escape echo chambers, update your worldview, build empathy, create original work, and stay alive to possibility. Curiosity is not a vibe. It’s a life skill.

What Curiosity Actually Is (And Why It’s Harder Than It Looks)

Curiosity looks simple until you inspect it. 

Researchers note that curiosity is hard to define cleanly because it contains multiple related processes. A child asking “why?” seems straightforward. But when you’re trying to build a systematic practice of curiosity—especially one that leverages AI—the distinctions matter.

A useful working definition: Curiosity is the drive to seek information or experience that reduces uncertainty or expands possibility because you sense a meaningful gap.

But it’s a suitcase word—one label carrying several distinct modes:

• Epistemic curiosity: hunger for understanding (truth, explanations, models). This is the “I want to know how this works” drive. It’s deep, patient, and builds mental models.
• Perceptual curiosity: hunger for novelty (sensory experiences, surprises). This is the “ooh, shiny!” reflex. It’s shallow, fast, and seeks stimulation.
• Specific curiosity: “I need this answer.” Focused, urgent, practical. You’re trying to solve a concrete problem or close a specific knowledge gap.
• Diversive curiosity: “Show me something interesting.” Broad, exploratory, undirected. You’re browsing, not hunting.

This taxonomy matters because AI tends to feed diversive curiosity (more novelty), while human flourishing usually requires epistemic curiosity (more depth).

Think about it: recommendation algorithms are optimized for diverse curiosity. They serve you the next interesting thing. But they don’t help you build a coherent understanding. They don’t support the slow, iterative process of going from confusion to clarity to mastery.

Your curiosity machine must help you convert novelty into meaning. It must resist the pull of infinite distraction and channel your attention toward growth that compounds.

The Science: What Curiosity Does to Your Brain

The most useful thing science says about curiosity: Curiosity is a learning state.

Classic research showed that being in a high-curiosity state improves learning not only for what you’re curious about, but also for incidental information encountered along the way—curiosity primes the brain for broader encoding. Recent neuroscience maps curiosity’s network effects, showing it recruits reward-related circuitry and hippocampal mechanisms associated with memory formation.

But curiosity isn’t always helpful—context matters. Different curiosity states can sometimes interfere with memory for certain stimuli. And curiosity and boredom work as linked motivational signals: boredom pushes you to seek novelty; curiosity pulls you toward specific information gaps.

Practical takeaway: Curiosity is trainable because it’s a state you can reliably induce by creating the right kind of gap, then channeling it into a learning loop.

The Two Sides of Curiosity: Light and Shadow

Curiosity is like fire. It can cook your food or burn your house down.

Light curiosity expands you:

• Learning, mastery, creativity
• Empathy (“help me understand you”)
• Better decisions (seeking disconfirming evidence)
• Resilience (turning fear into inquiry)

Shadow curiosity consumes you:

• Doomscrolling and threat-binging
• Compulsive novelty loops
• Voyeurism and extraction
• Conspiracy spirals (questions without standards)
• “Research” as procrastination

Here’s the diagnostic rule: If curiosity increases your agency, it’s growth. If curiosity decreases your agency, it’s a compulsion loop.

A Human Curiosity Machine must include constraints and ethics, not as dampeners, but as a hearth that keeps the fire useful.

Ancient Wisdom: Curiosity as Disciplined Attention

Long before fMRI, wisdom traditions understood something crucial: curiosity is not merely intellectual. It’s a quality of attention.

Socrates: disciplined inquiry. The Socratic method is structured curiosity—define terms, surface assumptions, test contradictions, follow implications, revise beliefs. It’s curiosity with integrity, questions aimed at becoming more truthful, not more performative.

Zen: beginner’s mind. Beginner’s mind restores openness—the ability to see what’s there rather than what you assume is there. It’s the antidote to expertise becoming a cage.

Dadirri: Deep listening. This Aboriginal practice of inner deep listening reminds us that curiosity isn’t only outward—collecting facts. It’s inward: noticing, receiving, sensing meaning. In an age of machine “listening,” human deep listening becomes a differentiator.

Modern translation: a curiosity machine isn’t just a questioning tool. It’s an attention practice.

Can Curiosity Be Trained?

Yes, especially the behaviors that generate and sustain it.

Research in psychology and education suggests curiosity can be supported through question-generation, carefully designed “gaps,” and learning environments that reward inquiry rather than mere performance. In computational cognitive science, curiosity is modeled as intrinsic motivation—a drive toward finding patterns and learning progress.

The key distinction: you don’t train curiosity by “trying to be curious.” You train it by practicing the moves curiosity uses:

• Noticing confusion without numbing it
• Asking better questions
• Tolerating uncertainty longer
• Seeking disconfirming evidence
• Running small experiments
• Reflecting on what you learned

That’s the basis of the system below.

The Human Curiosity Machine: Six Steps

This is the operating system that we can all use to turns wonder into wisdom and curiosity into a ocean of learning

Step 1: Frame the Unknown

Ask: What kind of problem is this?

• Simple: best practices exist
• Complicated: expert analysis helps
• Complex: experiments are required
• Chaotic: stabilize first

If you frame wrong, you’ll ask the wrong questions.

Step 2: Define Your Terms (Socratic Clarity)

Ask: What do I mean by the key words? Most confusion lives in unexamined definitions.

Step 3: Surface Assumptions

Ask: What am I assuming is true? Assumptions are the invisible rails of your inquiry.

Step 4: Run Epistemic Guardrails

Ask two questions every time:

• What would change my mind? (falsifiability)
• What’s the base rate? (reference class reality)

Step 5: Model the System

Ask: What are the incentives, feedback loops, delays, and second-order effects? This is how you go from trivia to insight.

Step 6: Act—Small, Fast, Real

Ask: What’s the smallest experiment that produces new information in 48 hours? Curiosity that never acts becomes entertainment.

Where AI Fits (and Why the Division of Labor Is Everything)

AI lacks curiosity. But AI is phenomenal at supporting curiosity—if you assign it the right roles and refuse to hand over what only humans can do.

The mistake most people make: they treat AI like an oracle. Ask it anything, trust the output, move on. This is efficient but ultimately hollow. You get answers without understanding. Solutions without judgment. Information without transformation.

The better approach: treat AI like a thinking partner with specific strengths—and specific limits.

Humans bring:

• Meaning: “Why does this matter?” AI can’t tell you what’s worth caring about. That’s a human call, rooted in values, consequences, and the life you’re trying to build.
• Values: “What’s worth pursuing?” AI optimizes for whatever you tell it to optimize for. But deciding what should be optimized? That’s on you.
• Ethics: Consent, care, consequences. AI can simulate ethical reasoning but it has no stake in outcomes. It doesn’t experience harm. You do, and so do the people affected by what you create.
• Taste: What’s signal versus noise. AI can surface patterns, but it can’t tell you which patterns matter or which insights are profound versus merely clever.
• Courage: To sit with uncertainty, to ask unpopular questions, to challenge your own assumptions even when it’s uncomfortable.
• Responsibility: To act on what you learn—and to live with the results.

AI brings:

• Breadth: Generate angles, questions, and possibilities you didn’t see. AI is tireless at ideation and can hold more variables than human working memory allows.
• Synthesis: Compress complexity, find patterns across domains, connect dots that span different knowledge bases.
• Critique: Steelman arguments, red-team your thinking, find holes in your logic. AI is excellent at playing devil’s advocate without ego.
• Experimentation: Propose tests, design routines, suggest small next steps. AI can scaffold your learning process.
• Scaffolding: Track decisions, hypotheses, learnings over time. AI has perfect recall and can surface past insights when relevant.

The division of labor is the whole game. When humans do what humans do best and AI does what AI does best, curiosity becomes a superpower.

When you blur those lines, when you let AI answer questions only you should answer, or when you waste your energy on tasks AI handles better—curiosity degrades into either passivity or busywork.

A Daily Routine to Amplify Curiosity (12 Minutes)

Charlie Munger was seen by his children as “Two legs sticking out of a book”. I have been identified as someone who is “Two legs trapped in a chatbot thread”. Deep diving into one topic with multiple questions chasing a curiosity that has no end. 

So here  is a question training loop. Do it daily for 14 days and you’ll feel the difference.

1. One-Minute Wonder Capture

Write one sentence: “What am I genuinely curious about today?“

Then write one sharper sentence: “What feels unresolved, confusing, or slightly uncomfortable?”

That discomfort often signals the information gap.

2. Two-Minute Question Upgrade (AI as Question Forge)

Prompt: “Generate 15 questions about this. Then pick the best 3 that would most change my decisions or worldview.”

3. Five-Minute Socratic Coach (AI Asks First)

Prompt: “Before answering, ask me 7 clarifying questions about: goal, constraints, assumptions, evidence, risks, what would change my mind, and what action I’ll take.”

Answer quickly. Don’t overthink. Let the questions do their work.

4. Three-Minute 48-Hour Experiment

Prompt: “Design a 48-hour micro-experiment. Include hypothesis, smallest test, success criteria, stop rule, and what to record.”

5. One-Minute Close the Loop

Write three bullets:

• What I learned
• What I’ll do
• What I’m not chasing (today)

That last line is the anti-rabbit-hole move.

The Curiosity Framework Stack

If you were going to build curiosity into your chatbot there are some top frameworks to consider or include:  

So if…Curiosity is the spark. Frameworks are the hearth.

They are the scaffolding to getting  a more realistic and honest answer out of AI without it sucking up and letting it tell you what it thinks you would like to hear. 

In the AI era, answers are everywhere. Which means raw curiosity—on its own—can easily become wandering, doomscrolling, or an endless loop of “one more question.”

Frameworks do what AI can’t: they discipline curiosity. They turn vague wonder into clear thinking, truth-seeking, and action. Think of them as “question lenses” you can swap in depending on the situation—so you don’t just ask more questions, you ask better ones.

Here are eight world-class frameworks you can embed into your Human Curiosity Machine (or your AI mentor), each with a one-sentence definition and a simple example question.

1. Socratic Method

What it is: A disciplined way to reach clarity by defining terms, surfacing assumptions, and testing contradictions before drawing conclusions.

Example question: “What exactly do I mean by ‘stuck’—stuck emotionally, strategically, or behaviorally?”

2. Cynefin

What it is: A diagnostic that tells you what kind of problem you’re facing (clear/complicated/complex/chaotic) so you choose best practice, expert analysis, or experiments appropriately.

Example question: “Is this a problem I solve with research—or do I need a safe-to-fail experiment?”

3. Falsification (“What would change my mind?”)

What it is: A truth filter that forces you to name disconfirming evidence instead of collecting facts that simply confirm what you already believe.

Example question: “What evidence would prove my belief is wrong?””

4. Base Rates

What it is: A reality anchor that asks what usually happens in similar situations before assuming your case is special.

Example question: “In situations like this, what typically happens—and what’s the success rate?”

5. Steelman / Red Team

What it is: A robustness practice where you build the strongest opposing argument (or invite critique) to reveal blind spots and strengthen your position.

Example question: “If a smart critic wanted to break my plan, what’s the first weakness they’d attack?”

6. Systems Thinking

What it is: A lens for seeing the hidden drivers of outcomes—feedback loops, incentives, delays, and second-order effects—rather than reacting to surface events.

Example question: “What incentive or feedback loop is causing this pattern to keep repeating?”

7. Pre-mortem

What it is: A decision tool that imagines your plan failed in the future, then works backward to identify the most likely reasons before you commit.

Example question: “It’s six months from now and this failed—what’s the most likely reason why?”

8. OODA Loop

What it is: A rapid learning cycle (observe–orient–decide–act) that turns curiosity into momentum through repeated action and feedback.

Example question: “What’s the smallest action I can take today to get real feedback by tomorrow?”

Bottom line: AI can generate endless questions. These frameworks help you generate the right questions—then convert them into insight and movement.

The Closing Insight

Srinivas’s quote is both a warning and an invitation.

When AI answers everything, the risk is that humans stop asking. We become consumers of outputs rather than authors of meaning.

So build the machine: Wonder → Questions → Tests → Insight → Action → Reflection → Deeper Wonder.

That’s the Human Curiosity Machine. Powered by AI. Directed by you.

The questions you ask determine the life you live. In the age of infinite answers, mastering the art of inquiry isn’t optional. It’s the difference between being shaped by algorithms and shaping your own becoming.

Start tomorrow. One question. Twelve minutes. Fourteen days.

Your curiosity machine is waiting to be built.

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