You're Not Too Late to the AI Party (And The Data Proves It)

An AI Party... according to Gemini

This post was supposed to be how AI is finally delivering the long-promised platform economy. But that will have to wait. It's Thursday evening and I'm staring at my 10th blog celebration post on LinkedIn. The likes and the congratulations are lovely, but the tone of direct messages catch my eye. Different people, same fear: "I've missed the AI boat, haven't I?"

One is from an industry veteran who’s been working for 20 years. Another from a recent graduate wondering if they should have started learning AI at university. The sense that everyone else has already built their third AI startup while they're still figuring out ChatGPT.

I get it. Scroll through LinkedIn and it feels like everyone's already fluent in prompt engineering, deploying autonomous agents, or pivoting their entire business model around AI (and yes, I appreciate that my posts contribute to this picture). The FOMO is real.

But then Anthropic's Economic Index report landed in my inbox this week, and what I found upended those assumptions. We're not late to the party - we're arriving at exactly the moment when things are getting interesting. And unlike the early internet days, we have a clearer map of where this is heading.

With that, let's dive in.

The 9.7% Reality Check

Here's the number that made me do a double-take: According to the Census Bureau's Business Trends and Outlook Survey, only 9.7% of US businesses report using AI in their production processes as of August 2025.

Not experimenting with. Not piloting. Actually using in production.

Less than one in ten.

Let that sink in. After all the breathless headlines and vendor promises, after every conference keynote about AI transformation, we're still in single digits for real implementation.

McKinsey's data adds another layer: while 78% of organizations worldwide are using or piloting AI in some capacity, over 80% aren’t yet seeing an impact on the bottom line. The gap between playing with ChatGPT and achieving genuine business transformation is vast.

It's like everyone's bought a guitar but hardly anyone can play Stairway to Heaven yet. (Side note: this is how I first came to hear of that song. Not on the radio when I was younger but when my childhood friend was learning to play).

The Geography of Opportunity (And Its Shadows)

The Anthropic report revealed something fascinating about global AI adoption. The US leads in raw usage, followed by India, Brazil, Japan, South Korea, and the UK. But adjust for population, and the picture shifts dramatically - Israel tops the list, with the US dropping to 6th and UK to 12th.

And here's what’s underlying that picture: this uneven adoption isn't just about opportunity - it's about infrastructure, education, and economic realities. Lower-income countries face genuine barriers that enthusiasm alone won't overcome. A developer in such a country might be brilliant, but inconsistent electricity and expensive internet create real obstacles.

Yet paradoxically, users in lower-adoption countries who do access AI use it more intensively for work automation. They're not playing around with fun prompts - they're using AI to leapfrog traditional development stages, much like mobile banking revolutionized finance in Africa before the West caught on (think Kenya’s M-Pesa in the early 2000s).

The variation across industries too is staggering. In early August 2025, one in four businesses in the Information sector reported using AI - roughly ten times the rate for Accommodation and Food Services.

Ten times.

If you're in hospitality, retail, construction, or healthcare, you're not late - you're potentially among the first pioneers in your field. But let's be honest about why: regulatory hurdles, legacy systems, and skill gaps aren't just excuses - they're real challenges that early adopters in these sectors will need to navigate.

The Expertise Illusion (And the Closing Window)

The data shows we're adding an estimated 64.4 million new daily AI users in 2025 - the highest single-year increase ever recorded. This newbie phenomenon creates an interesting dynamic: while the field is wide open overall, certain niches are rapidly professionalizing.

Basic prompt engineering for coding? That window is narrowing fast. The early adopters have built serious expertise here. But prompt engineering for education? Scientific research? Supply chain optimization? Still vast frontiers.

As I noted in my blog on generational AI adoption, the best prompter discussed at dinner recently was a 60-year-old Classics graduate who'd started just eighteen months ago. Their secret? Deep domain expertise combined with fresh eyes.

And yet we're also seeing AI-linked layoffs at tech giants. The same technology creating opportunities is displacing others. This isn't just about learning new skills - it's about navigating a rapidly shifting employment landscape.

The Web3 Wildcard

Something unexpected is happening at the intersection of AI and blockchain. While I've been focused on enterprise adoption, an entirely different revolution is brewing. While most organisations are now experimenting with AI agents in workflows, some are experimenting with decentralised systems.

Emerging and startup projects like SyncAI (looking to make ‘on chain’ interactions seamless and intuitive) and ChainGPT (think ChatGPT for blockchain and crypto) are creating on-chain AI systems that promise scalability and verifiable outputs. This isn't just crypto hype - it's addressing real concerns about transparency and control. If you're starting fresh, this convergence might be where the biggest opportunities lie.

The Enterprise Paradox

Here's a counter-trend that should give everyone pause: recent Business Trends and Outlook Survey cuts suggest a softening in AI usage among 250+ firms after rapid gains. After the initial rush, many are pulling back, reassessing, dealing with integration nightmares and unmet ROI expectations.

This creates an interesting dynamic. While Fortune 500s struggle with legacy system integration and change management, smaller firms are moving faster. The David vs. Goliath narrative in AI isn't just possible - it's happening.

One Evolution Roadmap

Let me share my own journey to illustrate the progression available to anyone starting now. When I began in LLM usage:

2023: Basic prompting for drafting and summaries (where many people stop)

2024: Analysis and research assistance (my security reports blog era)

2025: Creative applications and vibe coding (building the Butterlion game); leaning in to the latest capabilities (quantitative analysis, scenario planning, deep domain research, collaborative thought partner); image generation; using projects in LLMs for repeatable tasks (regular research tasks, knowledge-base required for context, advisory board setup)

Now: Integrated workflows via tools / MCP (AI connected to Google Drive, true "second brain" setup with master prompts and markdown files); using bespoke functions or agents in Claude Code; and multi-media outputs (video creation).

Each stage felt like discovering the instrument had another octave I'd never noticed. And I'm still continuously finding new ones.

Why Starting Now Is Actually Perfect Timing

After processing all this data and feedback, here's my refined take on why starting now might be better than starting two years ago:

The tools are infinitely better. Early adopters dealt with GPT-3.5's hallucinations and DALL-E 2's nightmare fingers. Today's models are more reliable, capable, and user-friendly.

The patterns are clearer. We know what works. The path from beginner to competent user is well-mapped with courses, communities, and resources that didn't exist a year ago.

The real problems have emerged. We're past the "AI will solve everything" phase. We understand where it truly adds value versus hype.

Fresh perspectives are gold. Veterans develop blind spots. A beginner's "naive" questions might unlock applications others are missing.

The hybrid opportunities are multiplying. It's not just about AI anymore - it's AI + domain expertise, AI + Web3, AI + whatever unique knowledge you bring.

Your Next Move

If you’ve been tinkering and haven’t yet dived in, don't try to boil the ocean. Here's a 1 week map to get started:

Day 1-2: Pick ONE specific problem in your work or personal life that annoys you. Something repetitive, error-prone, or time-consuming.

Day 3-4: Experiment with solving it using Claude, Grok, Gemini or ChatGPT. Don't aim for perfection - aim for "better than before."

Day 5-7: Document what worked and what didn't. Share it with one friend or colleague. Their reaction will tell you if you're onto something.

For me, it started with analysing industry and security reports. For you, it might be customer feedback analysis, lesson planning, contract review, or inventory forecasting. The domain doesn't matter. What matters is starting with real problems, not toy examples.

Get online - whether it’s finding people to follow on X or hunting down posts on LinkedIn. The learning curve is less steep when you're climbing with others.

The Bottom Line

The data is unequivocal: we're in the first act of a multi-year (multi-decade?) transformation. The party isn't just still going - the support act hasn't even taken the stage. But unlike those early days of breathless optimism, we now understand both the promise and the perils.

Yes, there's massive opportunity. Yes, entire industries remain untouched. Yes, your unique perspective and domain expertise matter more than your technical skills.

But also: yes, there will be displacement. Yes, some windows are closing. Yes, the learning curve is real.

The question isn't whether you're too late. The question is whether you're ready to commit to continuous learning in a landscape that shifts daily. Because ultimately as Armand Ruiz, VP of AI Platform @ IBM posted on LinkedIn this week, technology drives strategy and we all need to understand how this works.

Until next time, you'll find me exploring the vast unmapped territories where AI meets my particular corner of banking and risk - because even after all these blogs, I'm still discovering new continents...

Resources and Further Reading

Primary Sources:

• Anthropic Economic Index (Sep 2025). Anthropic

• U.S. Census BTOS. Census.gov

• Gallup (Jun 2025). For employee-level use doubling. Gallup.com

• McKinsey State of AI 2025. McKinsey & Company

• Stanford HAI AI Index 2025. Stanford HAI

• Bick, Blandin, Deming (2024/2025). St Louis Fed

• OECD (2025) Governing with AI. For public-sector use typologies. OECD