The Diary of a CEODaniel Priestley: Why plumbers may out-earn lawyers by 2029
Through commoditized content and trade-skill leverage, AI flips the work hierarchy; ecosystems and lived experience replace polish as the durable moat.
CHAPTERS
AI + robotics arrive together: why this shift feels bigger than past tech waves
Steven and Daniel frame AI and robotics as simultaneous disruptions to both “brain work” and physical labor. Daniel compares today’s mood—equal parts fear and excitement—to the transition from agriculture to the industrial age, but faster because of the internet’s instant distribution.
- •AI replaces cognitive output; robotics threatens physical labor at scale
- •Transition speed is unprecedented because AI deploys globally instantly
- •Mixed emotions: huge opportunity alongside deep insecurity
- •Historical analogy: agricultural → industrial era as a template for upheaval
Jevons Paradox and the surprise outcome: cheaper creation can mean more jobs
Daniel argues that when costs drop dramatically, demand and new categories of work can expand rather than collapse. He uses media (TV vs YouTube) and journalism (newspapers vs creators/Substack) to show how old jobs vanish but new, adjacent work explodes.
- •Jevons paradox: efficiency can increase total consumption and new work
- •YouTube reduced traditional production teams but created massive creator ecosystems
- •Job loss in legacy industries can coincide with larger job creation elsewhere
- •Lower costs unlock ‘unmet needs’ that weren’t viable to serve before
Algorithmic media replaces social media: attention becomes the constraint
They discuss the plateau in human attention and the explosion of content supply, especially AI-generated. Steven shares data showing growing variance in content performance, suggesting algorithms care less about follower count and more about interest-based ranking—marking the shift to “algorithmic media.”
- •Time spent online is plateauing; content supply is exploding
- •Interest algorithms reward ‘best today’ content over follower graphs
- •Performance variance is rising: past success matters less day-to-day
- •Creators need more than one-dimensional AdSense models to compete
The ‘above the fog’ strategy: defensible creator businesses are ecosystems
Daniel explains that pure content is becoming commoditized, but multi-dimensional ecosystems remain defensible. Real-world experiences, community, products, and services combine into a moat that AI slop can’t easily replicate.
- •Airport/fog metaphor: late entrants may struggle to ‘take off’ amid AI content
- •One-dimensional creator monetization is weakening; ecosystems are stronger
- •IRL events, community, and multi-format presence create defensibility
- •Value shifts from content units to bundled experiences and belonging
What entrepreneurs do that AI can’t (yet): framing, judgment, and market timing
Using an agriculture analogy, Daniel claims AI excels at ‘middle steps’ but not at choosing the right start, stopping point, or go-to-market nuance. Their debate touches on agentic AI’s potential, but Daniel emphasizes we haven’t seen fully autonomous value creation loops at scale—yet.
- •AI is strong at execution; humans still lead on intent, framing, and market sense
- •Entrepreneurs: decide what to build, when to ship, and how to sell
- •Agentic futures are plausible, but real-world evidence is still limited
- •Human coherence and taste still matter in product/market decisions
The AI financial crash thesis: data centers as the next bubble (2029 prediction)
Daniel’s bear case is financial, not technical: massive spend on short-lived data centers creates an unstable infrastructure cycle. He outlines a historical pattern where infrastructure capex above ~3% of GDP often precedes recessions, arguing AI compute has an unusually short replacement cycle that could trigger a 2029-style meltdown.
- •Data centers are ‘Walmart-sized’ GPU farms powering AI requests
- •Hardware cycles are ~3–4 years, unlike rail/roads that last decades
- •Capex is enormous relative to revenue; subscription economics may not support it
- •Private credit packaging into pension funds increases systemic risk
- •Prediction: potential major crash around 2029 (100 years after Great Depression)
The six-step entrepreneurial loop for the AI era (and how to validate fast)
Daniel lays out a repeatable ‘value creation loop’ entrepreneurs use: founder-opportunity fit, validation, product-market fit, go-to-market, scale, and exit. He emphasizes fast, cheap experiments—like waitlists—to avoid building what people don’t want.
- •Six steps: founder-opportunity fit → validation → product-market fit → go-to-market → scale → exit
- •Validation prevents ‘rookie’ all-in mistakes
- •Waitlists and small experiments reveal real demand quickly
- •Product-market fit is delivering on expectations, not just clicks
The new AI gold rush: micro-SaaS and ‘software + community’ business models
They argue AI drastically lowers the barrier to building software, enabling profitable niche SaaS with far fewer customers and smaller teams. The moat shifts away from the tool itself toward training, community, events, and an ecosystem around the software.
- •AI reduces development costs and team sizes dramatically
- •Profitable SaaS may need 500–1,000 customers, not 10,000+
- •Bespoke internal tools (e.g., ATS) become easy to build, commoditizing generic tools
- •Defensibility comes from bundled education, community, agents, and experiences
Jobs at risk and the pendulum swing: why plumbers may out-earn lawyers
Daniel shares a personal legal example where Claude replaced expensive legal work, illustrating disruption of time-based professional services. He predicts many white-collar roles will transform while trades rise due to supply shortages and AI’s limited ability to do physical, on-site work.
- •Legal work: contract/regurgitation is easily automated; roles must evolve
- •High-risk roles: drivers, customer service, admin, bookkeeping, SDRs, warehouse tasks
- •VIP/white-glove human service may grow as automation makes it affordable
- •Trade shortage + demand increases trades’ earnings potential
- •University loan incentives created labor-market distortions away from trades
Where do displaced workers go? Bottom-up adaptation vs top-down planning
Steven worries about transition speed and mass displacement; Daniel argues markets self-organize if people have transparency, skills, and incentives. He criticizes government-driven “market distortions” (e.g., student loans) and frames excessive state spending/tax as eroding innovation and prompting talent flight.
- •Concern: job creation may lag job destruction due to rapid rollout
- •Daniel’s view: educate and reveal price signals; people reallocate themselves
- •Market distortion example: student loans decoupled education from labor demand
- •High government spend/tax reduces incentives for productive risk-taking
- •Wealth/talent outflows worsen public finance burdens for remaining residents
UK & New York case studies: taxation, pessimism, and the flight of producers
They discuss rising youth unemployment, millionaire outflows from the UK, and business formation slowing in New York. Daniel argues a small share of taxpayers fund a large share of public spending, so their departure shifts costs to everyone and can accelerate decline.
- •UK youth unemployment and political scapegoating distract from economic shifts
- •Millionaire net outflows accelerate year over year
- •High producers fund disproportionate public budgets; exits create revenue gaps
- •NYC example: proposed property tax increases amid projected shortfalls
- •Individual takeaway: build income not tied to one geography
Bear case beyond finance: inequality, ‘Engels pause,’ and governance risks
Daniel introduces the ‘Engels pause’—a period when tech gains concentrate wealth—warning AI could intensify inequality rapidly. Steven cites Anthropic’s CEO on societal immaturity, totalitarian misuse, and AI’s accelerating capability, raising the possibility that old career advice may no longer apply.
- •Engels pause: tech-driven productivity can concentrate wealth for decades
- •Governance risk: AI tools can enable control/tyranny as well as defense
- •Rapid acceleration could invalidate current success playbooks
- •Demographics/wealth concentration (older cohorts) adds strain to transition
Should society adopt UBI or an AI wealth fund? Meaning, motivation, and ownership
They explore UBI as a transitional mechanism during deflationary AI productivity gains, while noting evidence that unconditional payments can reduce work. Daniel speculates governments may end up owning data center infrastructure after bailouts, funding redistribution—yet both agree humans still need purpose and meaningful struggle.
- •AI-driven deflation could justify stimulus/UBI during transition
- •Studies suggest UBI can reduce hours worked; purpose still matters
- •Possible future: state ownership of compute infrastructure after bailouts
- •Society shouldn’t romanticize ‘shitty jobs’; opportunity is redesigning life systems
- •Open question: how to preserve motivation and meaning
Personal resilience toolkit: personal brand, entrepreneurship, and ‘play with AI’
Daniel’s practical advice: don’t be invisible—build a small personal brand; learn entrepreneurial thinking; and actively experiment with AI tools. Steven adds an employer lens: candidates who tinker with AI and demonstrate ‘figure-it-out’ agency are increasingly valuable.
- •Personal brand goal: 2,000–20,000 people who know what you do
- •Adopt an entrepreneurial mindset: problems as opportunities
- •Hands-on AI experimentation is now a career differentiator
- •Use AI for hard problems, not just search; bring data/docs for analysis
- •AI can surface hidden opportunities (e.g., sales-call insights and scripts)
Writing, reflection, and being ‘wide’: the human edge is lived experience
They argue writing and reflection are becoming more important as a proxy for understanding and for asking better questions. Daniel recommends ‘pause, reflect, document’ away from screens, while Steven argues future innovation belongs to generalists who combine diverse reference points and share irreplaceably human stories.
- •Writing clarifies thinking; good questions drive AI leverage
- •‘Pause, reflect, document’ with pen/paper improves insight and direction
- •Relatable beats impressive; build from personal playbooks and lived experience
- •Generalism and cross-domain exposure fuel innovation via new combinations
- •Defensible value: relationships, community, and real-world human connection
Lifestyle businesses vs ‘passive income’: small teams, portfolios, and fulfillment
Daniel reframes success away from building huge companies, predicting small, lean teams (2–20) will thrive. He critiques the passive-income narrative, arguing most people want fun, flexible, creative work and can transition via side hustles or ‘apprenticeships’ rather than risky leaps.
- •Easier than ever to build a small, great business; harder to build a giant one
- •Lifestyle business focus: fun, freedom, fulfillment with manageable responsibility
- •Passive income is really asset income; build assets or invest surplus
- •Transition path: side hustle/apprenticeship → small team stages → stable business
- •Know your ‘enough’ and develop a portfolio of interests beyond work
Closing: fear, boom-bust reality, survivorship bias, and relationships as legacy
Daniel reflects on repeated boom-bust cycles, the fear of failing to provide, and why he kept going. The conversation ends with a meditation on mortality and relationships—voice notes, small gestures, and family formation—as the true enduring value beyond career graphs.
- •Entrepreneurship is rarely linear; often boom-bust or long ‘nothingness’ then exponential
- •Survivorship bias: not every persistence story ends in success—adaptation matters
- •Daniel credits failure as compounding learning that later stabilizes outcomes
- •A loved one’s stroke reframes priorities toward relationships and presence
- •Legacy is often relational: small acknowledgments and shared moments
