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Tony Cooper
Founder, We Build Stores
26 years in digital marketing
Last week I promised to tell you why SQLite is probably enough for your business.
Short answer: it handles 304 database tables, 706 invoices, £120k+ revenue tracking, and 8 active clients without breaking a sweat.
You’d think running a business platform would require PostgreSQL, or MySQL at minimum. Enterprise infrastructure for professional operations. But SQLite - a single-file database - powers my entire SEO intelligence platform. Zero configuration. Zero maintenance. Zero performance issues.
For most businesses, it’s genuinely enough.
Here’s the thing. Database choice isn’t the interesting question anymore. Everyone’s solved that problem. Boring technology works.
The interesting question is this: What can’t AI do?
Because while everyone’s selling AI as magic, I’ve spent six months working with Claude Code every single day, building an entire business platform at 40-80x typical velocity. And I can tell you exactly where AI stops and human expertise becomes critical.
This is the honest list nobody publishes.
In This Issue
Why SQLite Handles ‘Enterprise’ Workloads Nobody Admits — 304 tables, thousands of records, zero complaints
What AI Actually Can’t Do (The Honest List) — Strategic thinking, client relationships, business judgment, knowing when to question
Strategic Thinking vs Pattern Execution — The critical gap between Dr. Ford mode and tactical implementation
When 26 Years of Expertise Beats 20 Years of Training Data — Pattern recognition fails at the boundaries
The Human Judgment That Compounds AI Velocity — Why 40x speed requires knowing what to build
Key Insight: AI amplifies excellent judgment and terrible judgment equally. The velocity multiplier works both ways. Success isn’t about AI capability - it’s about knowing when human expertise is non-negotiable.
SQLite is Probably Enough (The Quick Answer)
Let me get the promised SQLite discussion out of the way efficiently.
My platform runs on SQLite locally. Here’s what it actually handles:
- 304 database tables across the entire system
- 706 invoices tracking £120k+ revenue
- 8 active clients with complete SEO intelligence
- Audit runs with thousands of data points
- Financial tracking across multiple accounts
- Client success documents and work delivered
- Marketing campaigns and email logs
- Task management and time entries
Performance issues: None.
Maintenance overhead: Zero.
Configuration complexity: Literally none. It’s a single file.
When you actually need PostgreSQL:
- Multiple users writing simultaneously (I’m the only operator)
- Distributed systems requiring replication (single VPS works fine)
- Data volumes in hundreds of gigabytes (nowhere close)
- Complex geospatial queries (don’t need them)
- Full-text search at massive scale (SQLite FTS works for my needs)
The reality: SQLite handles “enterprise” workloads that most businesses never approach. The cargo cult around “needing PostgreSQL” is developers solving problems they don’t have.
That’s the SQLite answer. Simple databases work.
But here’s what I actually want to talk about: What AI can’t do.
What AI Actually Can’t Do (The List Nobody Publishes)
I work with Claude Code every single day. Six months of intensive AI-assisted development. Building features in hours instead of weeks. Genuine 40-80x velocity on technical implementation.
And here’s what it absolutely cannot do.
Make strategic business decisions.
Claude Code can build a feature in 45 minutes. It cannot tell me if that feature is worth building.
When I say “should I add client health scoring to the dashboard?” - AI will enthusiastically start implementing. It won’t ask whether this wins clients or serves existing ones. It won’t calculate the ROI vs spending that time on proposals. It won’t question if this is the highest-leverage activity available.
That strategic pause requires human judgment.
In November, I discovered I’d been building features when I should have been filling the 22 empty client slots. AI built everything I asked for brilliantly. It never questioned whether I was building the right things.
AI amplifies direction. It doesn’t provide it.
Maintain client relationships.
Claude Code can draft professional emails. It cannot build trust over coffee conversations.
Real client relationships require reading body language during discovery calls. Understanding unstated concerns. Knowing when to push back on bad ideas. Recognising when “yes” actually means “I’m worried.” Building rapport that survives mistakes.
I can use AI to prepare for client meetings. I cannot use AI to conduct them with genuine human connection.
26 years of client relationship experience beats any training data.
Know when the pattern is wrong.
This is the critical limitation nobody discusses.
AI works through pattern recognition. It’s seen Django implementations thousands of times. When patterns match proven examples, velocity is extraordinary.
But what happens when the proven pattern shouldn’t apply?
AI will suggest patterns it’s seen work many times before. Standard approaches. Proven implementations. And sometimes those patterns are completely wrong for your specific use case - but AI can’t recognise that the familiar pattern is the wrong solution.
That recognition requires understanding your specific constraints, your specific business context, your specific requirements in ways that pattern matching can’t capture.
AI gives you speed. Expertise gives you direction. You need both.
Recognise when to question vs execute.
I’ve developed a concept called “strategic pause triggers” - moments when you should stop and think rather than immediately build.
Client meeting preparation. What’s the objective beyond the meeting itself? New feature requests. Does this win/serve clients or avoid hard work? Time allocation decisions. Is this the highest-leverage activity? Technical architecture choices. Does boring technology actually apply here?
Claude Code will execute brilliantly on any request. It won’t pause and ask if you should be requesting something different.
That pause - the “wait, is this the right problem?” moment - requires human judgment developed over years of expensive mistakes.
Apply 26 years of hard-won expertise.
AI has 20 years of Django training data. I have 26 years of internet business experience.
Those are different things.
Training data shows what worked in documented scenarios. Experience shows what failed in ways nobody documented. Why clients say yes when they mean no. Which shortcuts create long-term problems. When breaking best practices is correct. How business constraints override technical perfection.
That pattern recognition isn’t in AI training data. It’s developed through years of expensive lessons that taught me when “technically possible” doesn’t mean “strategically wise.”
The Dr. Ford Reality
In my collaboration with Claude Code, I’ve developed two distinct modes.
Tactical Execution Mode: Build this feature, fix this bug, implement this integration. AI works at 40-80x typical velocity. Extraordinary.
Strategic Advisory Mode (Dr. Ford): Apply multi-lens analysis, question assumptions, challenge direction, identify blind spots. AI provides frameworks, but human judgment makes the call.
Here’s the pattern I discovered: When I skip strategic thinking and jump straight to tactical execution, I build the wrong things efficiently.
Six months of git commits showed this evolution clearly. Early on: “Build X” → Claude builds X brilliantly → Realise X wasn’t the right solution.
Now: Strategic pause → Multi-lens analysis → Validate direction → THEN build with AI velocity.
The velocity only matters when you’re building the right thing.
When Boring Technology is Actually Wrong
Everyone’s excited about my “boring technology wins” philosophy. Django, SQLite, Astro, Tailwind - proven patterns that AI knows deeply.
But here’s what nobody asks: When is boring technology the wrong choice?
Real-time collaborative editing. SQLite’s single-writer limitation actually matters there. Actual millions of users - not the imagined millions everyone claims they’ll get, the real ones. Complex geospatial analysis where specialized databases exist for good reasons. High-frequency trading systems where microseconds matter and SQLite adds latency. Distributed teams editing simultaneously where PostgreSQL’s concurrency model wins.
The judgment required: Knowing when your constraints actually violate boring technology’s strengths.
AI can tell you the tradeoffs. It cannot tell you which tradeoffs matter for your specific situation with your specific constraints with your specific goals.
That requires human expertise applied to business reality.
The Velocity Multiplier Works Both Ways
Here’s the uncomfortable truth about AI assistance:
If you have excellent judgment: AI multiplies that into extraordinary results.
If you have poor judgment: AI helps you build the wrong things very efficiently.
40x velocity on correct strategic direction = competitive advantage.
40x velocity on poor strategic direction = expensive mistakes delivered quickly.
The multiplier is neutral. Your judgment determines the outcome.
What This Actually Means
Everyone’s focused on AI capabilities. Asking “what can AI do?”
The critical question is: “What must humans still do?”
Based on six months of intensive AI-assisted development, here’s what I’ve learned.
AI excels at pattern recognition and application. Implementing proven solutions quickly. Technical execution at 40-80x speed. Generating code following established patterns. Researching and synthesising documentation.
Humans are required for strategic business judgment. Client relationship building. Knowing when patterns don’t apply. Recognising “wrong problem, right solution” scenarios. Applying hard-won experience to novel situations. Making the strategic pause before building.
The winning combination: Human strategic judgment + AI tactical execution.
Not “AI replaces expertise.” Not “AI is just autocomplete.”
AI amplifies expertise. But only if expertise exists.
The 26-Year Advantage
Why does my AI-assisted development work so well?
Not because I’m an exceptional coder. I can read code, modify intelligently, understand what’s happening. But I’m not writing complex systems from scratch.
Because I’ve made every expensive business mistake over 26 years.
I know what clients actually want vs what they ask for. I recognise scope creep before it starts. I understand which shortcuts create technical debt and which are pragmatic. I can tell when “add this feature” means “I’m avoiding harder work.”
That pattern recognition isn’t in AI training data. It’s developed through years of expensive lessons.
AI gives me 40x execution speed. Experience gives me strategic direction. Together: sustainable competitive advantage.
Separately: expensive mistakes delivered efficiently or good judgment executed slowly.
The Honest Assessment
If someone asks “should I use AI for my business?” - here’s my honest answer.
Yes, if you have strong strategic judgment to direct it. If you can recognise when to pause vs execute. If you know when proven patterns don’t apply. If you have expertise worth amplifying.
No, if you’re hoping AI will provide business judgment. If you expect it to replace client relationships. If you think pattern recognition equals strategic thinking. If you want AI to tell you what to build.
AI is the most powerful tool I’ve ever used. It’s not a replacement for expertise. It’s an amplifier.
And amplifiers make everything louder - including mistakes.
Try This Instead
Next time you’re about to ask AI to build something, pause.
Ask yourself: Is this the right problem to solve? What’s the business objective beyond task completion? Does this win/serve clients or avoid harder work? Am I building or displacement-activity building? What would 26 years of experience say about this?
Only after strategic validation → Apply AI velocity.
You’ll know it’s working when you build less but achieve more.
Because the right thing built in hours beats the wrong thing built in minutes.
P.S. - Next Week: The one-day-per-week client delivery model. How 8 clients get exceptional service without burnout, and why AI assistance enables the compression.
P.P.S. - The Strategic Framework: Want the multi-lens decision framework I use before AI builds anything? The checklist that determines if you’re solving the right problem? Reply with “STRATEGIC” and I’ll send you the systematic pause protocol that prevents building wrong things efficiently.
Tony Cooper We Build Stores - Where 26 Years of Experience Delivers in One Hour What 26 Hours of Not Knowing Cannot
tony.cooper@webuildstores.co.uk 07963 242210
This Week: SQLite is probably enough. AI definitely isn’t. Human judgment + AI velocity = competitive advantage. Either alone is insufficient.
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