This $401 Million Company Built by Two People Reveals the New Rules of AI Powered Marketing

In September 2024, Matthew Gallagher launched Medvi, a GLP-1 telehealth startup, from his home in Los Angeles with no employees, no venture capital, and no traditional marketing team. 

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.

The Part the Headlines Missed

Six weeks before the New York Times profile of Medvi, the FDA sent a warning letter for misbranding compounded drugs. The AI chatbot had fabricated drug prices and invented product lines. Gallagher honoured the fake prices, absorbing the cost. The story is not a clean victory lap. It is a precise map of where AI-powered marketing creates extraordinary leverage and where it generates extraordinary risk if the system it runs on has not been redesigned alongside the tools.

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.

Each stage is covered in its own chapter with a lead expert, a chart, a real AI example, and linked research. The table below shows how they connect.

Stage	Chapter	Lead Expert	AI Leverage Point	Core Metric
Awareness & Visibility	03	Aleyda Solis	Structure content for AI citation (GEO)	55% of searches show AI Overview
Demand Intelligence	02	Rand Fishkin	Research before tool selection	84% use AI; 17% trained
Content Engine	04	Ross Simmonds	One idea → 7 assets via AI	58% higher engagement
Attention & Social	05	Gary Vaynerchuk	Platform-native AI creative iteration	TikTok: +200% follower growth
Workflow Execution	06	Kieran Flanagan	AI agents: research → publish	16 hrs saved/marketer/week
Revenue & Conversion	07	Kipp Bodnar	AI lead scoring + CRM enrichment	1.5× revenue growth vs peers
Onboarding	08	Elena Verna	Personalised time-to-first-value path	Day-30 retention +60%
Retention & Lifecycle	09	Elena Verna	Churn signal detection 3-4 wks early	Expansion revenue +60-90%

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

LEAD EXPERT: Paul Roetzer, Founder, Marketing AI Institute

Why Paul: He built the institution that trained more marketers on AI strategy than anyone else on the planet. His framework for thinking about AI as a spectrum of adoption from assisted tasks to autonomous workflows is the clearest mental model available for understanding where any organisation actually sits in this transition.Founded the Marketing AI Institute in 2016, before most marketers had heard of GPT. Author of Marketing Artificial Intelligence (2022), the defining book on AI marketing strategy. Host of the Marketing AI Show podcast with 400+ episodes. His 2025 finding that only 17% of marketing professionals have received comprehensive AI training is one of the most cited statistics in this playbook.

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.”

REAL AI EXAMPLE: Redesigning from the OS up

A B2B SaaS company ran a 90-day AI audit. They mapped every recurring marketing task against three questions: can AI do this as well? Can it do it faster? Does human judgment at this step change the outcome? Result: 14 of 22 recurring tasks were fully automated, 6 were AI-assisted with human review, and only 2 required human-first execution. Weekly marketing output tripled. The CMO’s role shifted from task management to strategic direction within one quarter.

Sources: ALM Corp AI Marketing Report 2026 · LoopEx Digital AI Marketing Statistics Q1 2026 · McKinsey State of AI 2025

CHAPTER 02

Start With Demand, Not Tools

LEAD EXPERT: Rand Fishkin, Founder, SparkToro (former CEO, Moz)

Why Rand: In an era where AI makes it trivially easy to produce content at scale, Fishkin is the most important voice arguing that starting with tools is the wrong order of operations. His work on audience intelligence — who your buyers actually are, what they actually read, and which sources actually influence them — is the pre-condition that most AI marketing frameworks skip entirely.

Co-founded Moz in 2004 and grew it to the leading SEO software company in the world. Founded SparkToro in 2018 to solve the problem he saw most clearly: marketers do not know enough about their audiences before they produce. His 2024 analysis showing that dark social and unmeasured channels account for the majority of B2B influence is cited in this chapter.

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.

REAL AI EXAMPLE: Demand-first before content creation

SparkToro analysis of a fintech brand’s target audience revealed their buyers spent 3× more time reading niche accounting software review sites than LinkedIn or Twitter. The brand had invested 80% of its content budget on LinkedIn and Twitter. After redirecting to sponsored content on the review platforms their buyers actually read, qualified inbound leads increased 140% in 60 days. Zero new content was created. Only the distribution changed.

Sources: Salesforce State of Marketing 2025 · SparkToro Audience Research · Rand Fishkin, SparkToro Blog

CHAPTER 03

Visibility Is the New Traffic

LEAD EXPERT: Aleyda Solis, International SEO Consultant, Founder at Orainti

Why Aleyda: She is the practitioner who has done more than anyone to translate the abstract shift from SEO to GEO into actionable frameworks for working marketers. While most SEO commentators were still debating whether AI Overviews were a threat or an opportunity, Solis was already publishing systematic methodologies for how brands could structure content to be cited by AI answer engines.

Founder of Orainti, an international SEO consultancy. Speaker at over 100 conferences in 20+ countries. Creator of the SEOFOMO newsletter, read by over 25,000 SEO professionals weekly. Her framework for GEO — Generative Engine Optimisation — distinguishes between the 40% of brands actively optimising for AI citation and the 60% that are quietly becoming invisible to the fastest-growing traffic source in the ecosystem.

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.

REAL AI EXAMPLE: GEO audit into a content brief

A marketing agency ran a GEO audit for a cybersecurity client: they asked ChatGPT, Perplexity, and Google AI Mode the 20 questions their buyers most commonly search. The client appeared in only 3 of 20 AI answers — despite ranking on page one of Google for 14 of those 20 terms. The gap: AI engines were citing competitors with original research and specific attributed statistics. The agency restructured three existing posts with original survey data, clear headers, and cited claims. Within 6 weeks, AI citation presence rose from 3 to 14 of 20 prompts. AI-referred sessions increased 340%.

Sources: Frase AI SEO Agents Report 2026 · BrightEdge Organic Search Research 2026 · SE Ranking GEO Tools 2026

CHAPTER 04

Build a Content Engine, Not a Prompt Habit

LEAD EXPERT: Ross Simmonds, Founder & CEO, Foundation Inc.

Why Ross: The phrase “create once, distribute forever” is his. So is the discipline behind it. In a marketing landscape flooded with AI-generated content produced quickly and forgotten faster, Simmonds is the clearest voice on what a genuine content engine looks like versus what most teams are building: a prompt habit dressed up as a strategy.

Founder and CEO of Foundation Inc., working with companies including HubSpot, Shopify, and Intercom. Author of Create Once, Distribute Forever (2024). His research showing that over 50% of content investment is wasted on production for pieces that are never properly distributed is one of the most underreported findings in content marketing. Regular contributor to Harvard Business Review on B2B content strategy.

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.

REAL AI EXAMPLE: One article becomes seven assets in 45 minutes

A solo B2B consultant writes one 1,800-word thought leadership article per week. Using Claude, she extracts a LinkedIn post from the contrarian data point in section two, a 5-slide carousel from the framework, a newsletter opening from the story hook, and a 60-second video script from the key insight. Opus Clip then cuts the video into a YouTube Short and TikTok clip. Total repurposing time: 45 minutes. Previously, each asset took 2 hours. She produces the equivalent of 14 hours of content work in 45 minutes — without losing her voice, because the ideas and judgments are entirely hers.

Sources: Content Marketing Institute B2B Research 2025 · HubSpot State of Marketing 2025 · Ross Simmonds, Foundation Inc.

CHAPTER 05

Win Attention Where People Actually Are

LEAD EXPERT: Gary Vaynerchuk, Chairman at VaynerX, CEO at Vayner

Why Gary: His record is simple and unrepeatable. He called Twitter in 2007, Instagram in 2011, Snapchat in 2013, TikTok in 2017 — in every case before the majority of brands had arrived, and in every case he was right. In the AI era, his core argument is more relevant than ever: attention is the scarce resource, it lives on specific platforms before it migrates, and most organisations are always too late.

Chairman of VaynerX, the holding company that includes VaynerMedia — one of the largest social media agencies in the world. Author of seven New York Times bestselling books on social media and attention economics. VaynerMedia manages over $1 billion in annual media spend across TikTok, Instagram, YouTube, and LinkedIn, giving him unmatched real-world data on what actually performs versus what brands assume should perform.

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.

REAL AI EXAMPLE: 30 creative tests in 5 days

A DTC skincare brand was running 3 creative variants per paid social campaign and waiting 3 weeks for statistical significance. After switching to an AI-powered creative workflow using Midjourney for static creative and CapCut AI for short video, they moved to testing 30 variants simultaneously across TikTok and Instagram Reels — different hooks, visual treatments, and CTAs. The best-performing variant in the first 48 hours became the new benchmark, and 15 new challengers were generated. Cost per acquisition fell 38% in the first month. The team did not hire anyone. They changed the workflow.

Sources: Socialinsider Social Media Benchmarks 2026 · Emplifi Social Media Benchmarks 2026 · Influencer Marketing Factory Creator Economy Report 2026

CHAPTER 06

Turn AI Into a Workflow Advantage

LEAD EXPERT: Kieran Flanagan, Advisor, Former SVP Marketing, HubSpot

Why Kieran: He is one of the few senior marketing executives who has actually rebuilt a marketing function around AI from the inside of a major organisation — not as a pilot programme, but as a systemic redesign of how work gets done. His distinction between spot automation and workflow redesign is the most practically useful framework in this chapter.

Former SVP Marketing at HubSpot, leading the team responsible for growing marketing from $100M to $1B+ ARR. Co-host of the Marketing Against the Grain podcast. His writing on AI workflow design — specifically the idea that winning teams are redesigning workflows, not replacing individual tasks — has been cited by senior leaders at Salesforce, Intercom, and dozens of high-growth SaaS companies.

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.

The HubSpot Breeze Case Study

HubSpot’s 2025 Breeze AI update rebuilt core workflows around autonomous agents. Seventh Sense analyses each contact’s engagement history and delivers emails at the precise moment each subscriber is most likely to open them. Result: 35% average email open rate lift within 90 days. That is not a tool improvement. That is a workflow redesign.

REAL AI EXAMPLE: A 7-step pipeline running on two people

A growth-stage SaaS company replaced a 4-person content team with a 2-person editorial team plus an AI workflow stack. The pipeline: Claude drafts from briefs, Surfer SEO scores and optimises, a human editor reviews and approves, n8n publishes to WordPress and cross-posts to LinkedIn, Opus Clip generates video variants, and ActiveCampaign triggers the email nurture sequence on publish. Total human time per article: 90 minutes of strategic editing. Output increased from 4 articles per month to 16. CAC from organic fell 44% in the following quarter.

Sources: Salesforce State of Marketing 2025 · McKinsey AI Adoption Research 2025 · HubSpot Breeze AI Overview

CHAPTER 07

Connect Marketing to Revenue

LEAD EXPERT: Kipp Bodnar, CMO, HubSpot

Why Kipp: He sits at the intersection of marketing and revenue with more data than almost any other CMO in B2B. HubSpot processes marketing and sales data for hundreds of thousands of companies. His perspective on the gap between marketing activity and revenue outcome is informed not by theory but by the patterns he sees across that dataset every day.

CMO of HubSpot since 2012, overseeing growth to over $2.4 billion in annual revenue. Co-author of The B2B Social Media Book. His 2025 State of Marketing report — based on data from over 1,700 marketing professionals — is one of the most cited data sources on AI marketing adoption globally. Under his leadership, HubSpot’s Breeze AI update represented one of the most significant rebuilds of a major CRM around AI-native workflows.

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.

REAL AI EXAMPLE: From 12% to 27% lead-to-opportunity rate in one quarter

A B2B software company had a 12% lead-to-qualified-opportunity conversion rate and a 90-day average sales cycle. They implemented AI lead scoring combining page visit history, email engagement depth, firmographic fit, and intent data signals from G2 and Bombora. Leads scoring above 75 were auto-routed to senior AEs with a pre-populated context brief. Leads scoring 40–75 entered a 3-email AI-personalised nurture sequence before sales contact. Within one quarter, lead-to-opportunity conversion rose from 12% to 27%. Sales cycle shortened from 90 to 62 days. No new salespeople were hired.

Sources: PYMNTS: The One-Person Billion-Dollar Company Is Here · McKinsey AI Revenue Growth Research · HubSpot Annual Report 2025

CHAPTER 08

Onboarding Is Part of Marketing

LEAD EXPERT: Elena Verna, PLG Advisor, Former SVP Growth, Miro & SurveyMonkey

Why Elena: She is the person most responsible for making product-led growth (PLG) a mainstream framework for B2B SaaS. Her argument that onboarding is not a product problem but a marketing problem — because it is the moment where the promise made in acquisition is tested — reframes how most marketing teams think about their accountability.

Former SVP Growth at Miro and SurveyMonkey. Advisor to over 30 high-growth SaaS companies on PLG strategy. Her Reforge Growth Series course on PLG has been taken by over 10,000 practitioners. Her newsletter Growth Scoop covers the intersection of AI and PLG at a depth few practitioners match. Frequently cited as the most influential voice in PLG alongside Andrew Chen and Casey Winters.

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.

REAL AI EXAMPLE: Day-30 retention from 31% to 54% without changing the product

A project management SaaS had a day-30 retention rate of 31%. An audit revealed the problem was onboarding, not product: all new users received the same 5-email welcome sequence regardless of company size, role, or stated use case. After implementing AI segmentation at signup, three distinct onboarding paths were created: solo operators received 4 emails focused on templates; team managers received 5 emails focused on collaboration features; agencies received 6 emails focused on client reporting. Day-30 retention rose from 31% to 54% within 8 weeks. Zero product changes were made.

Sources: Appcues User Onboarding Benchmarks 2025 · Intercom Product Engagement Report 2025 · Elena Verna, PLG Benchmarks

CHAPTER 09

Retention Is the Real Test

LEAD EXPERT: Elena Verna, PLG Advisor, Retention & Lifecycle

Why Elena (again): Verna is included for both chapters because her framework treats activation and retention as stages in the same continuous system — not as separate team responsibilities with separate metrics. In the AI era, the tools for personalising retention interventions have improved dramatically, but her core argument remains: retention depends on product value and customer fit. AI can help you respond to problems faster. It cannot create value where none exists.

See Chapter 08 for full credentials. In the context of retention specifically, her most cited work is on net revenue retention as the single most predictive metric for SaaS health. Her framework distinguishing between activity retention (are customers logging in?) and value retention (are customers getting the outcome they came for?) is the lens through which this chapter analyses what AI-powered lifecycle marketing can and cannot solve.

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.

The NIB Health Funds Case Study

NIB Health Funds deployed an AI customer service layer that cut support costs by $22 million and reduced resolution times by 87%, with customer satisfaction scores reaching 84%. The freed capital was redirected toward lifecycle marketing programmes that had previously lacked capacity. Cost savings at the service layer fund growth investment at the lifecycle layer.

REAL AI EXAMPLE: Detecting churn 4 weeks before it happens

A subscription analytics company built a churn prediction model trained on 18 months of customer data. The model identified three leading indicators: login frequency below twice per week, failure to use two or more core features in any 14-day period, and zero email engagement for 21 days. When all three appeared simultaneously, it triggered a personalised retention sequence: a direct CSM outreach, an in-app prompt offering a 1:1 session, and a feature highlight email based on the customer’s original signup use case. Of customers who triggered the model and received the intervention, 61% did not churn. Without the model, the churn rate in that cohort had been 78%.

Sources: NIB Health Funds AI Customer Service Case Study · Profitwell Retention Benchmarks 2025 · Bain & Company Customer Value Research

CHAPTER 10

Measure Signal, Not Activity

LEAD EXPERT: Christopher Penn, Co-Founder & Chief Data Scientist, Trust Insights

Why Christopher: He is the most rigorous voice at the intersection of marketing, data science, and AI. In an era where AI can generate more reports and dashboards than any team can act on, Penn’s argument that most marketing analytics is measuring the wrong things is the corrective most marketing teams need.

Co-founder and Chief Data Scientist at Trust Insights, advising over 200 companies on AI-driven measurement strategy. Host of the Marketing Over Coffee podcast for 18+ years. Author of seven books on marketing data and AI, including AI For Marketers (2023). Published in Harvard Business Review and MIT Sloan Management Review. Named one of the 50 Most Influential People in Sales Lead Management, multiple years running.

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.

REAL AI EXAMPLE: Finding the two metrics that actually predicted revenue

A content-led B2B company was tracking 23 marketing KPIs weekly. None correlated reliably with pipeline. A correlation analysis on 18 months of data identified two leading indicators that predicted qualified pipeline 6 weeks in advance with 78% accuracy: average scroll depth on pillar content pages above 65%, and newsletter reply rate above 3.2%. All other metrics were either lagging indicators or noise. The marketing team dropped 19 of their 23 KPIs, focused investment on improving those two signals, and saw pipeline predictability improve dramatically within 90 days.

Sources: Forrester Marketing Survey 2025 · Trust Insights Marketing Analytics Research · HubSpot State of Marketing 2025

CHAPTER 11

Tool Stacks by Stage

LEAD EXPERT: Paul Roetzer, Marketing AI Institute, Tool Stack Curator

Why Paul (again): The Marketing AI Institute runs the most rigorous ongoing evaluation of AI marketing tools available to practitioners. Unlike most tool reviews written by people who tested tools in isolation, his team evaluates tools in the context of real marketing systems — how they integrate, where they hallucinate, and whether they solve the problem that was actually the bottleneck.

See Chapter 01 for full credentials. The Marketing AI Institute’s annual AI Marketing Benchmark Report — based on surveys of over 1,200 marketing professionals — is the most comprehensive data source on which tools are actually being used at scale and with what results. Their MAICON conference brings together practitioners from over 40 countries annually to share implementation case studies that do not appear in vendor marketing materials.

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.

BEGINNER Under $100/mo	INTERMEDIATE$300–600/mo	ADVANCED $1,500+/mo
Claude or ChatGPT — content, copyPerplexity — researchCanva AI — visualsBeehiiv / ConvertKit — emailBuffer or Later — scheduling Master prompting before adding tools.	Claude — copy and ideationSurfer SEO or Frase — SEO/GEOMidjourney — visual creativeActiveCampaign — email automationOpus Clip — video repurposingn8n or Make — workflow automation What a 3-person team did 5 years ago.	Claude + Jasper — content engineHubSpot Breeze — CRM + agentsGoodie AI — GEO monitoringSeventh Sense — email timingRunway + Descript — videon8n — agentic pipelines Full agentic marketing stack.

CHAPTER 12

The New Marketing Moat

LEAD EXPERT: Paul Roetzer, Marketing AI Institute, The Long View

Why Paul (closing): Roetzer is the author of the argument that runs through this entire playbook. His core thesis — that AI changes the cost of execution but not the fundamentals of trust, relevance, distinctiveness, and judgment — is not a consolation prize for the sceptics. It is a strategic framework for identifying where the durable competitive advantages will actually live in an AI-saturated marketing landscape.

See Chapters 01 and 11 for full credentials. His closing argument is informed by five years of tracking what has actually happened to organisations that adopted AI early versus those that waited — and specifically by the consistent finding that tool sophistication does not correlate with marketing outcome. What correlates is the combination of genuine expertise, authentic audience relationships, and the discipline to use AI to amplify the signal rather than manufacture the noise.

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.”

REAL AI EXAMPLE: The audience relationship no competitor can copy

A 15-year-old industry newsletter with 30,000 subscribers and a 42% open rate was acquired for 11× revenue. The acquirer’s internal analysis cited one primary asset: the audience relationship. No AI tool had built it. No competitor could replicate it in 12 months regardless of their tool stack. What built it was 15 years of consistent, valuable, non-generic content sent directly to people who had explicitly asked to receive it. That relationship was valued at a premium over the content archive, the domain authority, and the existing advertiser relationships. The moat was not the content. It was the trust.

Source: Jeff Bullas, jeffbullas.com · April 2026i

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

* This article was originally published here

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Knowing You Will Die Makes You More Creative

The productivity industry is worth over $80 billion.

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.

Chart 1: People reminded of their own death show significantly higher creative output, meaning-seeking,drive to leave a legacy, investment in relationships, and depth of curiosity. (Source: Greenberg, Pyszczynski & Solomon (1986–2022), 500+ studies)

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.

Chart 2: The scale of the evidence. Over 500 studies. 40+ countries. 38% average rise in creative output. (Source: Terror Management Theory research corpus, 1986–2022.)

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.

Chart 3: History’s greatest creative periods consistently coincide with heightened mortality awareness.Illustrative index based on cultural output research (Simonton, 1988; Murray, Human Accomplishment, 2003).

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.

Chart 4: Five evidence-based ways that mortality awareness changes the quality and direction of your work. (Source: Applied Terror Management Theory research.)

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.

* This article was originally published here

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81,000 People Turned to AI for Personal Transformation: Why?

In December 2025, Anthropic did something genuinely unprecedented. They used AI itself, a version of Claude prompted as an interviewer to hold open-ended conversations with 80,508 people across 159 countries in 70 languages. 

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”

Fig. 1: What 80,508 people most want from AI, ranked by share of respondents.  (Source: Anthropic, March 2026)

“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.

Fig. 2: How people defined personal transformation. Sub-category breakdown from open-ended responses. (Source: Anthropic, March 2026)

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.

Fig. 3: Where AI has already delivered on people’s visions. Based on open-ended responses from 80,508 participants. (Source: Anthropic, March 2026)

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.

✦  AI PROMPT TO TRY “I’m going to share five experiences from my life where I felt most alive, engaged, and in flow. After I share them, I want you to identify the patterns, recurring themes, and values that seem to show up across all five. Don’t analyse each one separately — look for what connects them.”

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.

✦  AI PROMPT TO TRY “Play devil’s advocate. What assumptions am I making about myself that might not be true? What am I not seeing about my own patterns?”

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.

✦  AI PROMPT TO TRY “Based on everything I’ve shared with you, describe me back to myself as if you were writing a character sketch. Include my strengths, recurring blind spots, the fears that seem to shape my decisions, and the values that seem non-negotiable. Be honest — not flattering.”

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)

✦  AI PROMPT TO TRY “Here is a story I tell myself about why I can’t [do the thing you want to do]. I want you to help me find the alternative narrative — one that’s equally true but opens possibility rather than closing it.”

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.

✦  AI PROMPT TO TRY “This is my weekly review. Here’s what I said I would focus on last week: [X]. Here’s what I actually did: [Y]. Help me understand the gap — not to judge it, but to learn from it. What does this pattern reveal about what I actually value versus what I think I value?”

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.

✦  AI PROMPT TO TRY “Based on what we’ve explored about my patterns and values, help me design one specific 30-day behaviour experiment — small enough to actually attempt, meaningful enough to matter — that tests the new narrative I’m trying to build about myself.”

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.

Fig. 4: The top concerns people raised about AI (multi-label: respondents could name multiple). Avg respondent named 2.3 concerns. (Source: Anthropic, March 2026)

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.”

Fig. 5: The “Light and Shade” tensions: every AI benefit has a corresponding concern, often within the same person. (Source: Anthropic, March 2026)

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.

* This article was originally published here

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