The $199 Billion Agentic AI Revolution Nobody Is Ready For

Something seismic just happened. On February 25, 2026, Anthropic announced its Enterprise Agents Program. Deploying Claude-powered AI agents directly into the workflows of finance teams, HR departments, legal offices, and engineering desks. The initial Cowork plugin release three weeks earlier triggered a plunge in the stock prices of legal software providers. Not a small dip. A plunge. The market had spoken: AI agents are no longer a future concept. They are here, and they are eating software.

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

Agentic AI global market size projection 2024–2034

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 enterprise deployment gap: experimenting vs. in production

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.

Who’s building the agentic future: competitive landscape

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%.

Projected CAGR 2025–2030 by industry sector

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 real upsides and genuine pitfalls of agentic AI

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 

Six numbers that define the agent revolution

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.

3 real-world case studies: Klarna, Engine, 1-800Accountant

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.

* This article was originally published here

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AI Just Wiped Out $285 Billion: Why Are Entrepreneurs Celebrating?

On February 3rd, 2026, approximately $285 billion in market value evaporated from global software stocks in a single trading session. 

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.

Solopreneur Explosion — US solopreneurs (M) vs AI adoption rate (%)

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.

The Execution Cost Collapse — Annual cost: 2015 traditional team vs 2026 AI-powered solo

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.

Industries Most Vulnerable to AI Agent Disruption — Market value at risk ($B)

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.”

The post AI Just Wiped Out $285 Billion: Why Are Entrepreneurs Celebrating? appeared first on jeffbullas.com.

* This article was originally published here

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We Built Social Media Echo Chambers. Now We’re Building AI Yes-Men.

I’ve been running an experiment for the past few months: building an AI mentor that actively disagrees with me. It challenges my assumptions, questions my reasoning, and pushes me past procrastination into action. It’s programmed to be my intellectual sparring partner, not my digital cheerleader. 

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.

* This article was originally published here

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AI Lacks Curiosity. Here’s How to Make That Your Human Superpower

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.

Aravind Srinivas, CEO of Perplexity and Claude

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.

The post AI Lacks Curiosity. Here’s How to Make That Your Human Superpower appeared first on jeffbullas.com.

* This article was originally published here

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