Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, while everyone was panicking about the AI bubble, I made a counterintuitive choice: I deliberately avoided AI for two years while others rushed to integrate ChatGPT into everything. Not because I was a luddite, but because I've seen enough tech hype cycles to know that the best insights come after the dust settles.
Here's what happened: while my competitors were burning through budgets on expensive AI tools and half-baked implementations, I spent 6 months methodically testing what AI actually delivers versus what it promises. The result? I discovered that bubbles aren't problems to avoid—they're strategic opportunities to exploit.
Most startup founders are asking the wrong question. Instead of "Should I avoid AI because it's a bubble?" they should be asking "How can I use the bubble dynamics to my advantage?"
After generating 20,000 SEO articles across 4 languages using AI and implementing AI workflows for dozen of clients, I've learned something crucial: bubbles create market inefficiencies that smart operators can exploit. Here's what you'll discover in this playbook:
Why bubble timing is actually perfect for strategic AI adoption
How to separate AI hype from genuine business value
My 3-layer framework for testing AI tools without burning cash
Specific AI implementations that deliver ROI during market uncertainty
How to position your startup advantageously as others pull back
Reality Check
What every startup founder has heard about AI bubbles
The startup ecosystem is split into two camps right now. On one side, you have the AI maximalists preaching that "AI will change everything" and startups need to integrate AI or die. On the other side, you have the skeptics warning that AI is an overpriced bubble that will leave most companies broke and disappointed.
The conventional wisdom from VCs and accelerators usually sounds like this:
"Move fast or get left behind" - Integrate AI now or competitors will eat your lunch
"AI-first everything" - Rebuild your entire product around AI capabilities
"Bubble means opportunity" - Pour resources into AI while investment is hot
"Customer demand is there" - Everyone wants AI features, so build them
"Technical moats are everything" - Focus on complex AI implementations for differentiation
Meanwhile, the skeptics counter with equally predictable advice: avoid AI entirely, focus on fundamentals, wait for the bubble to pop, and don't get caught up in the hype.
Both sides miss the point. This binary thinking—either go all-in on AI or avoid it completely—ignores the nuanced reality of how bubbles actually work in practice. Bubbles aren't just about inflated valuations and eventual crashes. They're about resource misallocation and strategic timing.
The real opportunity isn't riding the bubble or avoiding it. It's understanding that bubbles create predictable market dynamics you can exploit regardless of when they pop.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Here's my situation: I run a freelance consultancy helping SaaS and e-commerce companies with growth. When ChatGPT exploded in late 2022, every client started asking about AI integration. The pressure was intense—incorporate AI into everything or lose clients to "more innovative" competitors.
But I'd seen this movie before. The no-code bubble, the blockchain bubble, the growth hacking bubble. The pattern is always the same: early excitement, massive investment, unrealistic expectations, then disappointment when results don't match the hype.
So I made a contrarian bet. While everyone rushed to implement AI solutions, I decided to wait and watch. For two full years, I deliberately avoided offering AI services while carefully studying what was actually working versus what was just marketing noise.
My clients were initially frustrated. "Why aren't you using AI?" they'd ask. "Our competitors are already automating everything with ChatGPT." The FOMO was real, and I lost a few projects to agencies promising AI-powered everything.
During this waiting period, I observed something fascinating: most AI implementations were failing spectacularly, but nobody was talking about it. Companies were spending thousands on AI tools that delivered minimal value. Content generated by AI was generic and required extensive human editing. Chatbots were frustrating customers more than helping them.
The breakthrough came six months ago when I finally started my AI experiments. By waiting, I had access to better tools, clearer use cases, and—most importantly—I could learn from everyone else's expensive mistakes. I could see which applications actually delivered ROI versus which were just expensive toys.
The client who convinced me to start was a B2C Shopify store with 3,000+ products. They needed SEO content across 8 languages—a perfect testing ground for AI's actual capabilities versus its promises.
Here's my playbook
What I ended up doing and the results.
My approach wasn't to avoid AI or go all-in. Instead, I developed what I call the "Bubble Timing Strategy"—using the hype cycle dynamics to gain strategic advantage.
Phase 1: Strategic Patience (Months 1-24)
While others were rushing to implement AI, I used this time for intelligence gathering. I tracked which startups were burning through cash on AI initiatives, monitored which tools were actually delivering results, and identified the gap between AI marketing and AI reality.
The key insight: AI is a pattern machine, not intelligence. Most people were using it like a magic 8-ball, asking random questions. But the real value comes from treating AI as digital labor that can DO tasks at scale, not just answer questions.
Phase 2: Selective Implementation (Months 25-30)
When I finally started implementing AI, I had three major advantages over early adopters:
First, I could learn from their failures. I knew which tools burned through API costs, which implementations required constant human intervention, and which use cases actually worked at scale.
Second, the tools had matured significantly. The AI solutions available in 2024 were dramatically better than what existed in 2022, but they cost the same or less due to competition.
Third, my clients had "AI fatigue." They'd been burned by previous AI implementations, so they were ready for someone who promised practical results rather than revolutionary transformation.
My 3-Layer AI Testing Framework:
Layer 1: Content Generation at Scale
I started with the Shopify client who needed content for 3,000+ products across 8 languages. Instead of promising AI magic, I built a systematic workflow: product data export, custom knowledge base integration, brand voice development, and automated API workflows. The result: we went from 300 monthly visitors to 5,000+ in three months.
Layer 2: Process Automation
Next, I focused on repetitive tasks that AI could handle reliably. For a B2B SaaS client, I automated their HubSpot-to-Slack project creation workflow. The AI didn't need to be perfect—it just needed to save hours of manual work.
Layer 3: Strategic Analysis
Finally, I used AI for pattern recognition in large datasets. I fed AI my entire site's performance data to identify which page types converted best—insights I'd missed after months of manual analysis.
The Bubble Advantage Strategy:
Here's where the bubble timing became crucial. While competitors were scaling back their AI investments due to disappointing results, I was just getting started with proven use cases. This created several strategic advantages:
- Reduced competition: Fewer agencies were offering AI services after the initial disappointment
- Lower expectations: Clients wanted practical improvements, not revolutionary change
- Better pricing power: I could charge premium rates for AI services that actually worked
- Access to talent: AI specialists were available as other companies reduced their AI teams
The key realization: bubbles don't just create opportunities during the hype phase. They create different opportunities during the skepticism phase.
Timing Strategy
Wait for tools to mature and competitors to burn out before implementing proven AI use cases
Learning Advantage
Study others' expensive failures to identify what actually works versus marketing promises
Selective Implementation
Focus on AI as digital labor for scale rather than magical solutions for everything
Market Positioning
Enter the market when expectations are realistic and competition has decreased
The results from this bubble timing strategy have been significant, though not in the way most people expected from AI.
Business Impact:
Rather than revolutionary transformation, I achieved steady, measurable improvements. The Shopify client's traffic increase from <300 to 5,000+ monthly visitors was solid growth, not hockey-stick growth. But it was sustainable and cost-effective.
More importantly, my positioning in the market improved dramatically. While competitors were dealing with AI implementation failures and client disappointment, I was delivering practical results. This led to higher-quality client referrals and better project margins.
Cost Advantages:
By waiting, I avoided the expensive mistakes that early adopters made. I didn't waste money on tools that promised everything and delivered little. I didn't burn client relationships on failed AI experiments. Instead, I entered the market with proven workflows and realistic expectations.
The Unexpected Outcome:
The biggest surprise wasn't the AI results—it was how bubble dynamics affected client relationships. Clients who'd been burned by previous AI implementations were grateful for honest, practical approaches. This created stronger partnerships than I'd had during previous hype cycles.
By treating the bubble as a strategic timing opportunity rather than a threat to avoid or trend to chase, I gained sustainable competitive advantages that continue to pay off months later.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Looking back at this experience, here are the key lessons that apply beyond just AI bubbles:
1. Bubbles create multiple opportunity windows
Don't just think about riding the hype. The skepticism phase often offers better opportunities with less competition and more realistic expectations.
2. Strategic patience beats FOMO
Waiting 24 months allowed me to implement better solutions at lower costs with higher success rates. The fear of "missing out" was more expensive than actually missing out.
3. Intelligence gathering during hype phases is invaluable
Use bubble periods to study what's working versus what's just marketing. This research becomes your competitive advantage later.
4. Technology maturity matters more than timing
Tools available in 2024 were dramatically better than 2022 versions, but everyone was focused on being "first" rather than being "right."
5. Market positioning is everything
Being known for practical results during a skepticism phase is more valuable than being an early adopter during hype phases.
6. Clients prefer honesty over innovation theater
After being burned by overpromising, clients valued straightforward assessments of what AI could and couldn't do.
7. The real opportunity is process improvement, not transformation
AI's value comes from doing specific tasks better, not revolutionizing entire businesses. Managing expectations around this leads to better outcomes.
What I'd do differently: I would have started smaller experiments earlier, around month 18, to gather more data. Complete avoidance was too conservative.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups navigating the AI bubble:
Start with internal process automation before customer-facing features
Focus on review automation and content generation for proven ROI
Use AI for competitive analysis and market research
Implement gradually to avoid overwhelming your team
For your Ecommerce store
For e-commerce stores during AI uncertainty:
Prioritize product description generation and SEO content at scale
Automate customer service responses for common queries
Use AI for inventory forecasting and pricing optimization
Start with proven use cases rather than experimental features