Every Bubble Builds the Future: Why This AI Wave Works for You Now
Bubbles overbuild infrastructure; smart founders use today’s AI agents to automate the 70% and win the next decade.
Last week, I processed ten podcast guest inquiries in under 10 minutes.
A year ago, it took me 5-7 hours to do that, and I had time for one or two per week.
I didn’t get faster. I stopped working entirely.
Here’s what changed: I built an AI agent that does the research for me. I feed it a LinkedIn profile, and it pulls their background, scours their content on YouTube, Substack, and podcasts, and scores them on five criteria: founder relevance, originality, storytelling strength, topic overlap, and audience value.
If the score is 90 or above, the system sends an invitation link to book a recording. If it’s below 90, it declines with a customized email.
Result: Ten inquiries processed in an afternoon. Consistent quality. Five to seven hours of work I no longer do.
Here’s another example from last night — less glamorous, but more revealing.
My home NAS server had port forwarding issues. I opened the Perplexity Pro browser, logged into my NAS/router, and described the problem. The browser took over. It navigated through 28 diagnostics steps, found the misconfiguration, fixed it, and upgraded my router firmware. I didn’t click anything. I watched it work.
This isn’t the future. It’s Tuesday.
If you’re not experimenting with this yet, you’re at a disadvantage.
The Pattern: Infrastructure Outlives Expectations
Every technological bubble leaves a legacy.
Railroad speculators went broke laying too many tracks, but those rails connected a continent. Telecom companies bankrupted themselves overbuilding fiber, but that carries most of the world’s data today. Dot-com founders burned through cash chasing clicks, yet they built the consumer internet that supports today’s winners.
Bubbles are brutal teachers. They attract too much money too soon and build too much infrastructure too fast. They leave behind the roads the next generation will drive on.
The Fiber Nobody Wanted (Until People Began to Appreciate It)
In the late 1990s, telecom companies laid millions of miles of fiber optic cable across the U.S. The logic was sound: bandwidth demand would increase significantly. However, the timing was wrong: businesses were not ready.
By 2002, most of that fiber was unused — “dark fiber.” Companies that built it went bankrupt. Investors lost significant amounts of money.
But here’s what happened next:
When Netflix started streaming in 2007, they didn’t build new infrastructure. Instead, they leased capacity on “failed” fiber networks. The same was true for AWS in 2006 and Zoom during COVID.
The infrastructure survived the bubble. Later, the winners came.
The Pattern Across Bubbles:
Dot-com (1999): Pets.com, Webvan, and Kozmo tried to force behavior change too early and failed. They left behind e-commerce infrastructure, payment rails, and logistics networks. When consumer behavior evolved, Chewy (2011) and Instacart (2012) used the same infrastructure.
Telecom (2000): Overbuilt fiber networks bankrupted Global Crossing and WorldCom, but the dark fiber and data center footprint remained. Netflix, AWS, and Zoom leased capacity at a fraction of the cost.
The causal link: The infrastructure was right, but the timing was wrong. Winners used the leftovers when behavior aligned with capability.
Every wave overbuilds the next one’s needs.
Why This Bubble Is Different: From Tools to Teammates
For centuries, technology increased efficiency. The printing press produced more pages per hour, Google delivered more knowledge per query, and Excel enabled more analysis per minute.
AI multiplies autonomy. Past bubbles made tools to work faster. This one teaches those to work for us.
Past bubbles made tools:
Email didn’t write your responses. You did — more quickly.
Salesforce didn’t close your deals. You did — with improved tracking.
Figma didn’t design your product. You did — with real-time collaboration.
You got faster and more organized, and you still did the work.
This bubble creates teammates:
My podcast agent researches guests and writes tailored outreach.
Perplexity’s browser navigated 28 diagnostic steps and resolved my router issue.
Customer support AIs resolve routine tickets without additional escalation.
Sales agents qualify leads and prepare personalized emails.
Marketing agents repurpose long-form content into social posts and newsletters.
The difference is that you’re not working. You’re not working faster.
What “Autonomy” Means
Let’s be precise about what’s changing. AI doesn’t replace judgment. It handles 70% of patterned work so you can focus on the 30% that requires decisions.
Not autonomous: Strategic decisions, edge cases, relationship building, creative direction.
Now autonomous: Research, data gathering, first-draft generation, pattern matching, routine diagnostics, classification
The agent doesn’t decide who to invite on my podcast. It scores candidates against criteria I defined. I make the call on borderline cases — an 88 score with exceptional storytelling gets a yes. Agents handle the patterned work; humans handle the judgment calls.
The Trap Many Startups Will Encounter
The immediate reaction to AI hype is to add “AI features” to your product.
Don’t.
Most AI product features either solve problems customers lack, wrap ChatGPT in a less effective UI, or chase narrative instead of value.
Here’s what smart founders are doing instead: automating their manual work.
Before building AI for your customers, use it to eliminate internal bottlenecks. You’ll learn and move faster and understand AI’s value before selling it.
Who’s Advancing
Sales teams report that early adopters confirm 50-70% of flagged leads as qualified after human review.
Customer success: Ticket triage, health score monitoring, renewal risk alerts. Teams are seeing 40-60% of tier-1 tickets auto-resolved with quality scores above 90%. One mid-market B2B team cut its backlog in half in six weeks with agent triage and human escalation for challenging cases.
Product: User interview synthesis, feature request clustering, changelog generation
Marketing: Content repurposing (turning long-form into social posts, newsletters), SEO research, competitor monitoring.
Finance: Expense categorization, invoice matching, budget variance reports
These aren’t “AI companies.” They’re normal startups using agents to eliminate repetitive tasks. They’re tracking error rates under 10%, human review on exceptions, and measurable time savings.
Your First Agent
Don’t automate everything. Start with one task that: (1) happens weekly, (2) follows a pattern, (3) takes 1-3 hours, (4) doesn’t require judgment.
Friday Lead Cleanup
Manual process: Review CRM for cold leads, check LinkedIn for updates, send re-engagement emails.
Automated version (n8n, Zapier, or Make.com):
Pull “cold” leads from the CRM.
Feed LinkedIn profiles to Clay or Perplexity.
Check for trigger events (new job, funding, relevant post).
Generate personalized re-engagement email.
Draft in inbox for review (don’t auto-send initially).
Time saved: 2-3 hours per week
Add guidelines:
Before any customer-facing action, human review is required.
Spot-check 10% of output randomly. If error rate exceeds 5%, pause and assess.
Track your quality score by rating agent output 1-10 compared to your manual work.
If the agent fails, document rollback to manual process.
Day 7 — Measure: Track time saved, quality score, issues, and lessons learned. If underperforming: check prompt specificity, verify data quality, add human review, and change one variable at a time.
Once this runs reliably for a month, automate another workflow.
What’s the outcome of this bubble?
The AI boom will burst. Hype always outruns utility. Valuations will crash. “AI-first” companies will shut down.
But when the bubble pops, the infrastructure will be there.
What will endure:
Agent frameworks (LangChain, AutoGPT, Crew AI)
Model APIs (OpenAI, Anthropic, open-source alternatives)
Automation platforms (n8n, Zapier, Make)
Knowledge of AI’s capabilities.
What will disappear:
Products add AI for trends, not job-solving.
Agencies use prompt engineering as strategy consulting.
The idea that AI replaces entire jobs instead of specific tasks.
The Dark Fiber of AI: What to Construct Now
When the AI bubble bursts, most “AI features” will vanish. Certain infrastructure will remain valuable, and companies building it now will have a significant advantage.
What today’s “dark fiber” looks like:
Prompt libraries: Documented, versioned prompts for common tasks that are not scattered across Slack or individual ChatGPT histories. When a better model arrives, deploy it immediately.
Evaluation systems: Automated tests that check agent output quality before reaching customers. Does the research include sources? Is the email tone appropriate? Are the facts accurate? Build the QA now, enhance the agents later.
Data pipelines: Clean, structured data agents can use — customer context, product documentation, process docs, historical decisions. Most companies have this data; it’s confined in silos.
Permission layers: Document clear rules on what agents can do autonomously (draft email, categorize ticket) vs. what requires human approval (send email, close ticket, issue refund).
Audit trails: Logs of agent actions, timing, and outcomes. Quick diagnosis for issues and insight for successes.
These aren’t sexy. But they separate teams that scale agent usage from those that abandon it after the first hallucination incident.
Build this infrastructure now, even if your agents are basic. When better models arrive, you’ll have the framework to deploy them instantly.
This is your dark fiber. Lay it while it’s affordable.
Final Thought
You have a choice:
Wait for “AI maturity” and “best practices,” or start messy, learn quickly, and build an unfair advantage while competitors analyze.
The fiber was laid in 1999. Netflix used it in 2007. Eight years of benefit.
The infrastructure is being laid now. You can help build it or lease it from someone else.
Your move: This week, choose one manual task and automate it.
In six months, you’ll either be the founder who figured this out early or the one questioning why everyone else is moving faster.
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Very good.
The real opportunity with AI starts by focusing on quality — what makes you unique, where you have real expertise, and what you know better than others.
If that foundation is clear, and you combine it with a collaborative mindset and a concrete AI strategy and action plan, AI becomes a way to scale what already works, not replace it.
This is good stuff! Good reminder that even though the bubble will burst, you don't have to be one of the people that are hit by the burst but can actually become successful because of it.
Tangentially, your reference to using AI to improve your operations led me to think of Slack. A company that originally was a video game company that built a chat app to use internally. Later pivoting to the internal chat tool being the actual product that they sold.