Category: startup

The Hidden Economics of “Free” AI Tools: Why the SaaS Premium Still Matters

This post discusses the hidden costs of DIY solutions in SaaS, emphasizing the benefits of established SaaS tools over “free” AI-driven alternatives. It highlights issues like time tax, knowledge debt, reliability, support challenges, security risks, and scaling problems. Ultimately, it advocates for a balanced approach that leverages AI to enhance, rather than replace, reliable SaaS infrastructure.

This is Part 2 of my series on the evolution of SaaS. If you haven’t read Part 1: The SaaS Model Isn’t Dead, it’s Evolving Beyond the Hype of “Vibe Coding”, start there for the full context. In this post, I’m diving deeper into the hidden costs that most builders don’t see until it’s too late.

In my last post, I argued that SaaS isn’t dead, it’s just evolving beyond the surface-level appeal of vibe coding. Today, I want to dig deeper into something most builders don’t realize until it’s too late: the hidden costs of “free” AI-powered alternatives.

Because here’s the uncomfortable truth: when you replace a $99/month SaaS tool with a Frankenstein stack of AI prompts, no-code platforms, and API glue, you’re not saving money. You’re just moving the costs somewhere else, usually to places you can’t see until they bite you.

Let’s talk about what really happens when you choose the “cheaper” path.

The Time Tax: When Free Becomes Expensive

Picture this: you’ve built your “MVP” in a weekend. It’s glorious. ChatGPT wrote half the code, Zapier connects your Airtable to your Stripe account, and a Make.com scenario handles email notifications. Total monthly cost? Maybe $20 in API fees.

You’re feeling like a genius.

Then Monday morning hits. A customer reports an error. The Zapier workflow failed silently. You spend two hours digging through logs (when you can find them) only to discover that Airtable changed their API rate limits, and now your automation hits them during peak hours.

You patch it with a delay. Problem solved.

Until Wednesday, when three more edge cases emerge. The Python script you copied from ChatGPT doesn’t handle timezone conversions properly. Your payment flow breaks for international customers. The no-code platform you’re using doesn’t support the webhook format you need.

Each fix takes 30 minutes to 3 hours.

By Friday, you’ve spent more time maintaining your “free” stack than you would have spent just using Stripe Billing and ConvertKit.

This is the time tax. And unlike your SaaS subscription, you can’t expense it or write it off. It’s just gone, stolen from building features, talking to customers, or actually running your business.

The question isn’t whether your DIY solution costs less. It’s whether your time is worth $3/hour.

The Knowledge Debt: Building on Borrowed Understanding

Here’s a scenario that plays out constantly in the AI-first era:

A developer prompts Claude to build a payment integration. The AI generates beautiful code, type-safe, well-structured, handles edge cases. The developer copies it, tests it once, and ships it.

It works perfectly for two months.

Then Stripe deprecates an API endpoint. Or a customer discovers a refund edge case. Or the business wants to add subscription tiers.

Now what?

The developer stares at 200 lines of code they didn’t write and don’t fully understand. They can prompt the AI again, but they don’t know which parts are safe to modify. They don’t know why certain patterns were used. They don’t know what will break.

This is knowledge debt, the accumulated cost of using code you haven’t internalized.

Compare this to using a proper SaaS tool like Stripe Billing or Chargebee. You don’t understand every line of their code either, but you don’t need to. They handle the complexity. They migrate your data when APIs change. They’ve already solved the edge cases.

When you build with barely-understood AI-generated code, you get the worst of both worlds: you’re responsible for maintenance without having the knowledge to maintain it effectively.

This isn’t a knock on AI tools. It’s a reality check about technical debt in disguise.

The Reliability Gap: When “Good Enough” Isn’t

Let’s zoom out and talk about production-grade systems.

When you use Slack, it has 99.99% uptime. That’s not luck, it’s the result of on-call engineers, redundant infrastructure, automated failovers, and millions of dollars in operational excellence.

When you stitch together your own “Slack alternative” using Discord webhooks, Airtable, and a Telegram bot, what’s your uptime?

You don’t even know, because you’re not measuring it.

And here’s the thing: your customers notice.

They notice when notifications arrive 3 hours late because your Zapier task got queued during peak hours. They notice when your checkout flow breaks because you hit your free-tier API limits. They notice when that one Python script running on Replit randomly stops working.

Reliability isn’t a feature you can bolt on later. It’s the foundation everything else is built on.

This is why companies still pay for Datadog instead of writing their own monitoring. Why they use PagerDuty instead of email alerts. Why they choose AWS over running servers in their garage.

Not because they can’t build these things themselves, but because reliability at scale requires obsessive attention to details that don’t show up in MVP prototypes.

Your vibe-coded solution might work 95% of the time. But that missing 5% is where trust dies and customers churn.

The Support Nightmare: Who Do You Call?

Imagine this email from a customer:

“Hi, I tried to upgrade my account but got an error. Can you help?”

Simple enough, right?

Except your “upgrade flow” involves:

  • A Stripe Checkout session (managed by Stripe)
  • A webhook that triggers Make.com (managed by Make.com)
  • Which updates Airtable (managed by Airtable)
  • Which triggers a Zapier workflow (managed by Zapier)
  • Which sends data to your custom API (deployed on Railway)
  • Which updates your database (hosted on PlanetScale)

One of these broke. Which one? You have no idea.

You start debugging:

  • Check Stripe logs. Payment succeeded.
  • Check Make.com execution logs. Ran successfully.
  • Check Airtable. Record updated.
  • Check Zapier. Task queued but not processed yet.

Ah. Zapier’s free tier queues tasks during high-traffic periods. The upgrade won’t process for another 15 minutes.

You explain this to the customer. They’re confused and frustrated. So are you.

Now imagine that same scenario with a proper SaaS tool like Memberstack or MemberSpace. The customer emails them. They check their logs, identify the issue, and fix it. Done.

When you own the entire stack, you own all the problems too. And most founders don’t realize how much time “customer support for your custom infrastructure” actually takes until they’re drowning in it.

The Security Illusion: Compliance Costs You Can’t See

Pop quiz: Is your AI-generated authentication system GDPR compliant?

Does it properly hash passwords? Does it prevent timing attacks? Does it implement proper session management? Does it handle token refresh securely? Does it log security events appropriately?

If you’re not sure, you’ve got a problem.

Because when you use Auth0, Clerk, or AWS Cognito, these questions are answered for you. They have security teams, penetration testers, and compliance certifications. They handle GDPR, CCPA, SOC2, and whatever acronym-soup regulation applies to your industry.

When you roll your own auth with AI-generated code, you own all of that responsibility.

And here’s what most people don’t realize: security incidents are expensive. Not just in terms of fines and legal costs, but in reputation damage and customer trust.

One breach can kill a startup. And saying “but ChatGPT wrote the code” isn’t a legal defense.

The same logic applies to payment handling, data storage, and API security. Every shortcut you take multiplies your risk surface.

SaaS tools don’t just sell features, they sell peace of mind. They carry the liability so you don’t have to.

The Scale Wall: When Growth Breaks Everything

Your vibe-coded MVP works perfectly for your first 10 customers. Then you get featured on Product Hunt.

Suddenly you have 500 new signups in 24 hours.

Your Airtable base hits record limits. Your free-tier API quotas are maxed out. Your Make.com scenarios are queuing tasks for hours. Your Railway instance keeps crashing because you didn’t configure autoscaling. Your webhook endpoints are timing out because they weren’t designed for concurrent requests.

Everything is on fire.

This is the scale wall, the moment when your clever shortcuts stop being clever and start being catastrophic.

Real SaaS products are built to scale. They handle traffic spikes. They have redundancy. They auto-scale infrastructure. They cache aggressively. They optimize database queries. They monitor performance.

Your vibe-coded stack probably does none of these things.

And here’s the brutal part: scaling isn’t something you can retrofit easily. It’s architectural. You can’t just “add more Zapier workflows” your way out of it.

At this point, you face a choice: either rebuild everything properly (which takes months and risks losing customers during the transition), or artificially limit your growth to stay within the constraints of your fragile infrastructure.

Neither option is appealing.

The Integration Trap: When Your Stack Doesn’t Play Nice

One of the biggest promises of the AI-powered, no-code revolution is that everything integrates with everything.

Except it doesn’t. Not really.

Sure, Zapier connects to 5,000+ apps. But those integrations are surface-level. You get basic CRUD operations, not deep functionality.

Want to implement complex business logic? Want custom error handling? Want to batch process data efficiently? Want real-time updates instead of 15-minute polling?

Suddenly you’re writing custom code anyway, except now you’re writing it in the weird constraints of whatever platform you’ve chosen, rather than in a proper application where you have full control.

The irony is thick: you chose no-code to avoid complexity, but you ended up with a different kind of complexity, one that’s harder to debug and impossible to version control properly.

Meanwhile, a well-designed SaaS tool either handles your use case natively or provides a proper API for custom integration. You’re not fighting the platform; you’re using it as intended.

The Real Cost Comparison

Let’s do some actual math.

Vibe-coded stack:

  • Zapier Pro: $20/month
  • Make.com: $15/month
  • Airtable Pro: $20/month
  • Railway: $10/month
  • Various API costs: $15/month
  • Total: $80/month

Your time:

  • Initial setup: 20 hours
  • Weekly maintenance: 3 hours
  • Monthly debugging: 5 hours
  • Customer support for stack issues: 2 hours
  • Monthly time cost: ~20 hours

If your time is worth even $50/hour (a modest rate for a technical founder), that’s $1,000/month in opportunity cost.

Total real cost: $1,080/month.

Proper SaaS stack:

  • Stripe Billing: Included with processing fees
  • Memberstack: $25/month
  • ConvertKit: $29/month
  • Vercel: $20/month
  • Total: $74/month + processing fees

Your time:

  • Initial setup: 4 hours
  • Weekly maintenance: 0.5 hours
  • Monthly debugging: 1 hour
  • Customer support for stack issues: 0 hours (vendor handles it)
  • Monthly time cost: ~3 hours

At $50/hour, that’s $150/month in opportunity cost.

Total real cost: $224/month.

The “more expensive” SaaS stack actually costs 80% less when you account for time.

And we haven’t even factored in:

  • The revenue lost from downtime
  • The customers lost from poor reliability
  • The scaling issues you’ll hit later
  • The security risks you’re accepting
  • The knowledge debt you’re accumulating

When DIY Makes Sense (And When It Doesn’t)

Look, I’m not saying you should never build anything custom. There are absolutely times when DIY is the right choice.

Build custom when:

  • The functionality is core to your competitive advantage
  • No existing tool solves your exact problem
  • You have the expertise to maintain it long-term
  • You’re building something genuinely novel
  • You have the team capacity to own it forever

Use SaaS when:

  • The functionality is commodity (auth, payments, email, etc.)
  • Reliability and uptime are critical
  • You want to focus on your core product
  • You’re a small team with limited time
  • You need compliance and security guarantees
  • You value your time more than monthly fees

The pattern is simple: build what makes you unique, buy what makes you functional.

The AI-Assisted Middle Ground

Here’s where it gets interesting: AI doesn’t just enable vibe coding. It also enables smarter SaaS integration.

You can use Claude or ChatGPT to:

  • Generate integration code for SaaS APIs faster
  • Debug webhook issues more efficiently
  • Build wrapper libraries around vendor SDKs
  • Create custom workflows on top of stable platforms

This is the sweet spot: using AI to accelerate your work with reliable tools, rather than using AI to replace reliable tools entirely.

Think of it like this: AI is an incredible co-pilot. But you still need the plane to have wings.

The Evolution Continues

My argument isn’t that AI tools are bad or that vibe coding is wrong. It’s that we need to be honest about the tradeoffs.

The next generation of successful products won’t be built by people who reject AI, and they won’t be built by people who reject SaaS.

They’ll be built by people who understand when to use each.

People who can vibe-code a prototype in a weekend, then have the discipline to replace it with proper infrastructure before it scales. People who use AI to augment their capabilities, not replace their judgment.

The future isn’t “AI vs. SaaS.” It’s “AI-enhanced SaaS.”

Tools that are easier to integrate because AI helps you. APIs that are easier to understand because AI explains them. Systems that are easier to maintain because AI helps you debug.

But beneath all that AI magic, there’s still reliable infrastructure, accountable teams, and boring old uptime guarantees.

Because at the end of the day, customers don’t care about your tech stack. They care that your product works when they need it.

Build for the Long Game

If you’re building something that matters, something you want customers to depend on, something you want to grow into a real business, you need to think beyond the MVP phase.

You need to think about what happens when you hit 100 users. Then 1,000. Then 10,000.

Will your clever weekend hack still work? Or will you be spending all your time keeping the lights on instead of building new features?

The most successful founders I know aren’t the ones who move fastest. They’re the ones who move sustainably, who build foundations that can support growth without collapsing.

They use AI to move faster. They use SaaS to stay reliable. They understand that both are tools, not religions.

Final Thoughts: Respect the Craft

There’s a romance to the idea of building everything yourself. Of being the 10x developer who needs nothing but an AI assistant and pure willpower.

But romance doesn’t ship products. Discipline does.

The best software is invisible. It just works. And making something “just work”, consistently, reliably, at scale, is harder than anyone admits.

So use AI. Vibe-code your prototypes. Move fast and experiment.

But when it’s time to ship, when it’s time to serve real customers, when it’s time to build something that lasts, respect the craft.

Choose boring, reliable infrastructure. Pay for the SaaS tools that solve solved problems. Invest in quality over cleverness.

Because the goal isn’t to build the most innovative tech stack.

The goal is to build something customers love and trust.

And trust, as it turns out, is built on the boring stuff. The stuff that works when you’re not looking. The stuff that scales without breaking. The stuff someone else maintains at 3 AM so you don’t have to.

That’s what SaaS really sells.

And that’s why it’s not dead, it’s just getting started.

What’s your experience balancing custom-built solutions with SaaS tools? Have you hit the scale wall or the reliability gap? Share your stories in the comments. I’d love to hear what you’ve learned.

If you found this useful, follow me for more posts on building sustainable products in the age of AI, where we embrace new tools without forgetting old wisdom.

The SaaS Model Isn’t Dead, it’s Evolving Beyond the Hype of “Vibe Coding”

The article critiques the rise of “vibe coding,” emphasizing the distinction between quick prototypes and genuine MVPs. It argues that while AI can accelerate product development, true success relies on accountability, stability, and structure. Ultimately, SaaS is evolving, prioritizing reliable infrastructure and reinforcement over mere speed and creativity.

“The SaaS model is dead. Long live vibe-coded AI scripts.”

That’s the kind of hot take lighting up LinkedIn half ironic, half prophetic.

Why pay $99/month for a product when you can stitch together 12 AI prompts, 3 no-code hacks, and a duct-taped Python script you barely understand?

Welcome to vibe coding.

It feels fast. It feels clever.
Until the vibes break and no one knows why.

The Mirage of Instant Software

We live in an era of speed.
AI gives us instant answers, mockups, and even “apps.” The line between prototype and product has never been thinner and that’s both empowering and dangerous.

What used to take months of product design, testing, and iteration can now be faked in a weekend.
You can prompt ChatGPT to generate a working landing page, use Bubble or Replit for logic, and Zapier to glue it all together.

Boom “launch” your MVP.

But here’s the truth no one wants to say out loud:
Most of these AI-fueled prototypes aren’t MVPs. They’re demos with good lighting.

A real MVP isn’t about how fast you can ship; it’s about how reliably you can learn from what you ship.

And learning requires stability.
You can’t measure churn or retention when your backend breaks every other day.
You can’t build trust when your app crashes under 20 users.

That’s when the vibes start to fade.

The Boring Truth Behind Great Products

Let’s talk about what SaaS really sells.
It’s not just the product you see it’s everything beneath it:

  • Uptime: Someone is on-call at 3 AM keeping your app alive.
  • Security: Encryption, audits, GDPR, SOC2 the invisible scaffolding of trust.
  • Maintenance: When APIs change or libraries break, someone fixes it.
  • Versioning: “Update Available” didn’t write itself.
  • Support: Human beings who care when you open a ticket.

When you pay for SaaS, you’re not paying for buttons.
You’re paying for accountability for the guarantee that someone else handles the boring stuff while you focus on your business.

And boring, in software, is beautiful.
Because it means stability. Predictability. Peace of mind.

The Myth of the One-Prompt MVP

There’s a growing illusion that AI can replace the entire MVP process.
Just write a long enough prompt, and out comes your startup.

Except… no.

Building an MVP is not about output. It’s about the iteration loop testing, learning, refining.

A real MVP requires:

  • Instrumentation: Analytics to track usage and retention.
  • UX Design: Understanding user friction.
  • Scalability: Handling 500 users without collapse.
  • Product Roadmap: Knowing what not to build yet.
  • Legal & Compliance: Because privacy questions always come.

AI can accelerate this process but it can’t replace it.
Because AI doesn’t understand your market context, users, or business model.
It’s a tool not a cofounder.

From Vibes to Viability

There’s real power in AI-assisted building.
You can move fast, experiment, and prototype ideas cheaply.

But once something works, you’ll need to replace your prompt stack and Zapier web of glue code with solid infrastructure.

That’s when the SaaS mindset returns.
Not because you need to “go old school,” but because you need to go sustainable.

Every successful product eventually faces the same questions:

  • Who maintains this?
  • Who owns the data?
  • Who ensures it still works next month?
  • Who’s responsible when it breaks?

The answer, in true SaaS fashion, must always be: someone accountable.

SaaS Isn’t Dead, it’s Maturing

The world doesn’t need more quick hacks.
It needs more craftsmanship builders who blend speed with discipline, creativity with structure, and vibes with reliability.

SaaS isn’t dying; it’s evolving.

Tomorrow’s SaaS might not look like subscription dashboards.
It might look like AI agents, private APIs, or personalized data layers.

But behind every “smart” layer will still be boring, dependable infrastructure databases, authentication, servers, and teams maintaining uptime.

The form changes.
The value reliability, scalability, trust never does.

Final Thought: Build With Vibes, Ship With Discipline

There’s nothing wrong with vibe coding. It’s an amazing way to experiment and learn.

But if you want to launch something that lasts, something customers depend on you’ll need more than vibes.
You’ll need product thinking, process, and patience.

That’s what separates a weekend project from a real business.

So build with vibes.
But ship with discipline.

Because that’s where the magic and the money really happens.

If you liked this post, follow me for more thoughts on building real products in the age of AI hype where craftsmanship beats shortcuts every time.

Returning to the Rails World: What’s New and Exciting in Rails 8 and Ruby 3.3+

It’s 2025, and coming back to Ruby on Rails feels like stepping into a familiar city only to find new skyscrapers, electric trams, and an upgraded skyline.
The framework that once defined web development simplicity has reinvented itself once again.

If you’ve been away for a couple of years, you might remember Rails 6 or early Rails 7 as elegant but slightly “classic.”
Fast-forward to today: Rails 8 and Ruby 3.4 together form one of the most modern, high-performance, and full-stack ecosystems in web development.

Let’s explore what changed from Ruby’s evolution to Rails’ latest superpowers.

The Ruby Renaissance: From 3.2 to 3.4

Over the last two years, Ruby has evolved faster than ever.
Performance, concurrency, and developer tooling have all received major love while the language remains as expressive and joyful as ever.

Ruby 3.2 (2023): The Foundation of Modern Ruby

  • YJIT officially production-ready: Introduced a new JIT compiler written in Rust, delivering 20–40% faster execution on Rails apps.
  • Prism Parser (preview): The groundwork for a brand-new parser that improves IDEs, linters, and static analysis.
  • Regexp improvements: More efficient and less memory-hungry pattern matching.
  • Data class proposal: Early syntax experiments to make small, immutable data structures easier to define.

Ruby 3.3 (2024): Performance, Async IO, and Stability

  • YJIT 3.3 update: Added inlining and better method dispatch caching big wins for hot code paths.
  • Fiber Scheduler 2.0: Improved async I/O great for background processing and concurrent network calls.
  • Prism Parser shipped: Officially integrated, paving the way for better tooling and static analysis.
  • Better memory compaction: Long-running apps now leak less and GC pauses are shorter.

Ruby 3.4 (2025): The Next Leap

  • Prism as the default parser making editors and LSPs much more accurate.
  • Official WebAssembly build: You can now compile and run Ruby in browsers or serverless environments.
  • Async and Fibers 3.0: Now tightly integrated into standard libraries like Net::HTTP and OpenURI.
  • YJIT 3.4: Huge startup time and memory improvements for large Rails codebases.
  • Smarter garbage collector: Dynamic tuning for better throughput under load.

Example: Native Async Fetching in Ruby 3.4

require "async"
require "net/http"

Async do
  ["https://rubyonrails.org", "https://ruby-lang.org"].each do |url|
    Async do
      res = Net::HTTP.get(URI(url))
      puts "#{url} → #{res.bytesize} bytes"
    end
  end
end
Code language: PHP (php)

That’s fully concurrent, purely in Ruby no threads, no extra gems.
Ruby has quietly become fast, efficient, and concurrent while keeping its famously clean syntax.

The Rails Revolution: From 7 to 8

While Ruby evolved under the hood, Rails reinvented the developer experience.
Rails 7 introduced the “no-JavaScript-framework” movement with Hotwire.
Rails 8 now expands that vision making real-time, async, and scalable apps easier than ever.

Rails 7 (2022–2024): The Hotwire Era

Rails 7 changed the front-end game:

  • Hotwire (Turbo + Stimulus): Replaced complex SPAs with instant-loading server-rendered apps.
  • Import maps: Let you skip Webpack entirely.
  • Encrypted attributes: encrypts :email became a one-line reality.
  • ActionText and ActionMailbox: Brought full-stack communication features into Rails core.
  • Zeitwerk loader improvements: Faster boot and reloading in dev mode.

Example: Rails 7 Hotwire Simplicity

# app/controllers/messages_controller.rb
def create
  @message = Message.create!(message_params)
  turbo_stream.append "messages", partial: "messages/message", locals: { message: @message }
end
Code language: PHP (php)

That’s a live-updating chat stream with no React, no WebSocket boilerplate.

Rails 8 (2025): Real-Time, Async, and Database-Native

Rails 8 takes everything Rails 7 started and levels it up for the next decade.

Turbo 8 and Turbo Streams 2.0

Hotwire gets more powerful:

  • Streaming updates from background jobs
  • Improved Turbo Frames for nested components
  • Async rendering for faster page loads
class CommentsController < ApplicationController
  def create
    @comment = Comment.create!(comment_params)
    turbo_stream.prepend "comments", partial: "comments/comment", locals: { comment: @comment }
  end
end
Code language: CSS (css)

Now you can push that stream from Active Job or Solid Queue, enabling real-time updates across users.

Solid Queue and Solid Cache

Rails 8 introduces two built-in frameworks that change production infrastructure forever:

  • Solid Queue: Database-backed job queue think Sidekiq performance without Redis.
  • Solid Cache: Native caching framework that integrates with Active Record and scales horizontally.
# Example: background email job using Solid Queue
class UserMailerJob < ApplicationJob
  queue_as :mailers

  def perform(user_id)
    UserMailer.welcome_email(User.find(user_id)).deliver_now
  end
end
Code language: CSS (css)

No Redis, no extra service everything just works out of the box.

Async Queries and Connection Pooling

Rails 8 adds native async database queries and automatic connection throttling for multi-threaded environments.
This pairs perfectly with Ruby’s improved Fiber Scheduler.

users = ActiveRecord::Base.async_query do
  User.where(active: true).to_a
end
Code language: PHP (php)

Smarter Defaults, Stronger Security

  • Active Record Encryption expanded with deterministic modes
  • Improved CSP and SameSite protections
  • Rails generators now use more secure defaults for APIs and credentials

Developer Experience: Rails Feels Modern Again

The latest versions of Rails and Ruby have also focused heavily on DX (developer experience).

  • bin/rails console --sandbox rolls back all changes automatically.
  • New error pages with interactive debugging.
  • ESBuild 3 & Bun support for lightning-fast JS builds.
  • Improved test parallelization with async jobs and Capybara integration.
  • ViewComponent and Hotwire integration right from generators.

Rails in 2025 feels sleek, intelligent, and incredibly cohesive.

The Future of Rails and Ruby Together

With Ruby 3.4’s concurrency and Rails 8’s async, streaming, and caching power, Rails has evolved into a true full-stack powerhouse again capable of competing with modern Node, Elixir, or Go frameworks while staying true to its elegant roots.

It’s not nostalgia it’s progress built on the foundation of simplicity.

If you left the Rails world thinking it was old-fashioned, this is your invitation back.
You’ll find your favorite framework faster, safer, and more capable than ever before.

Posted by Ivan Turkovic
Rubyist, software engineer, and believer in beautiful code.

What You Should Learn to Master but Never Ship

Every engineer should build a few things from scratch search, auth, caching just to understand how much complexity lives beneath the surface. But the real skill isn’t rolling your own; it’s knowing when not to. In the age of AI, understanding how things work under the hood isn’t optional it’s how you keep control over what your tools are actually doing.

There’s a quiet rite of passage every engineer goes through. You build something that already exists. You write your own search algorithm. You design your own auth system. You roll your own logging framework because the existing one feels too heavy.

And for a while, it’s exhilarating. You’re learning, stretching, discovering how the pieces actually work.

But there’s a difference between learning and shipping.

The Temptation to Reinvent

Every generation of engineers rediscovers the same truth: we love building things from scratch. We tell ourselves our use case is different, our system is simpler, our constraints are unique.

But the moment your code touches production when it has to handle real users, scale, security, and compliance you realize how deep the rabbit hole goes.

Here’s a short list of what you probably shouldn’t reinvent if your goal is to ship something that lasts:

  • Search algorithms
  • Encryption
  • Authentication
  • Credit card handling
  • Billing
  • Caching systems
  • Logging frameworks
  • CSV, HTML, URL, JSON, XML parsing
  • Floating point math
  • Timezones
  • Localization and internationalization
  • Postal address handling

Each one looks simple on the surface. Each one hides decades of hard-won complexity underneath.

Learn It, Don’t Ship It

You should absolutely build these things once.

Do it for the same reason musicians practice scales or pilots train in simulators. You’ll understand the invisible edges where systems fail, what tradeoffs libraries make, how standards evolve.

Build your own encryption to see why key rotation matters.
Write your own caching layer to feel cache invalidation pain firsthand.
Parse CSVs manually to understand why “CSV” isn’t a real standard.

You’ll emerge humbled, smarter, and far less likely to call something “trivial” again.

But then don’t ship it.

The Cost of Cleverness

Production is where clever ideas go to die.

The real cost of rolling your own isn’t just the initial build. It’s the invisible tax that compounds over time: maintenance, updates, edge cases, security audits, integration testing.

That custom auth system? It’ll need to handle password resets, MFA, SSO, OAuth, token expiration, brute-force protection, and GDPR deletion requests.

Your homegrown billing service? Get ready for tax handling, currency conversion, refund flows, audit trails, and legal exposure.

Most of us underestimate this cost by an order of magnitude. And that gap between what you think you built and what reality demands is where projects go to die.

The Wisdom of Boring Software

Mature engineering isn’t about novelty it’s about leverage.

When you use battle-tested libraries, you’re not being lazy. You’re standing on top of millions of hours of debugging, testing, and iteration that others have already paid for.

The best engineers I know are boring. They use Postgres, Redis, S3. They trust Stripe for billing, Auth0 for authentication, Cloudflare for caching. They’d rather spend their creative energy on business logic and user experience the parts that actually differentiate a product.

Boring software wins because it doesn’t collapse under its own cleverness.

Why This Matters Even More in the AI Era

Today, a new kind of abstraction has arrived: AI.
We don’t just import libraries anymore we import intelligence.

When you integrate AI into your workflow, you’re effectively outsourcing judgment, reasoning, and data handling to a black box that feels magical but is still software under the hood.

If you’ve never built or understood the underlying systems search, parsing, data handling, caching, numerical precision you’ll have no intuition for what the AI is actually doing. You’ll treat it as oracle instead of a tool.

Knowing how these fundamentals work grounds you. It helps you spot when the model hallucinates, when latency hides in API chains, when an embedding lookup behaves like a fuzzy search instead of real understanding.

The engineers who will thrive in the AI era aren’t the ones who blindly prompt. They’re the ones who know what’s happening behind the prompt.

Because AI systems don’t erase complexity they just bury it deeper.

And if you don’t know what lives underneath, you can’t debug, govern, or trust it.

When It’s Worth Reinventing

There are exceptions. Sometimes the act of rebuilding is the product itself.

Search at Google. Encryption at Signal. Auth at Okta.

If your business is the infrastructure, then yes go deep. Reinvent with intention. But if it’s not, your job is to assemble reliable systems, not to recreate them.

Learn enough to understand the tradeoffs, but don’t mistake knowledge for necessity.

The Real Lesson

Here’s the paradox: you can’t truly respect how hard these problems are until you’ve built them yourself.

So do it once. In a sandbox, on weekends, or as a thought exercise. Feel the pain, appreciate the elegance of the libraries you once dismissed, and move on.

That humility will make you a better engineer and a more trusted builder in the AI age than any clever homegrown library ever could.

Final thought:
Master everything. Ship selectively.

That’s the difference between engineering as craft and engineering as production.
And it’s the difference between using AI and actually understanding it.

?? → BI → ML → AI → ??

AI’s past and the future

Where acronyms in business come from, what they sold, who won, and what might come after “AI”

Acronyms are the currency of business storytelling. They compress complex technology into a neat package a salesperson can pitch in a single slide: CRM, ERP, BI, ML, AI. Each one marked a shift in what companies sold to their customers and how value was captured. I want to walk through that history briefly, honestly, with business examples and what “winning” looked like in each era and then make a practical, evidence-based prediction for what comes after AI. I’ll finish with concrete signs companies and entrepreneurs should watch if they want to be on the winning side next.

The pre-acronym age: data collectors and automation (before CRM/ERP took over)

Before the catchy three-letter packages, businesses bought automation and niche systems: financial ledgers, bespoke reporting scripts, and the earliest mainframe systems. The selling point was efficiency: replace paper, reduce human error, scale payroll or accounting.

Winners: large system integrators and early software firms that could deliver reliability and scale. Value to the customer was operational: fewer mistakes, faster month-end closes, predictable processes.

This era set the expectation that software replaces tedious human work an expectation every later acronym exploited and monetized.

CRM / ERP the era of process standardization and cross-company suites

Acronyms like ERP and CRM told customers what problem a vendor solved: enterprise resource planning for the core business, customer relationship management for sales and marketing. The message was simple: centralize and standardize.

Business sales example: SAP and Oracle sold ERP as a bet on process control; Siebel (then Oracle) sold CRM as the way to professionalize sales organizations. Projects were expensive, multi-year, and became investments in repeatability and governance. The commercial model was license + services. Success looked like longer, stickier contracts and high services revenue.

Winners: vendors who could sell a vision of stability and then deliver implementation expertise.

BI (Business Intelligence) data becomes a product

BI formalized the idea that data itself is valuable: dashboards, reports, and the ability to make decisions from consolidated datasets. The term was popularized in the late 1980s and 1990s as companies realized that aggregated data and fact-based dashboards could change executive decision making. BI vendors promised that data could be turned into actionable insight.

Business sales example: BusinessObjects, Cognos, MicroStrategy sold a reliable narrative centralize data, produce dashboards, enable managers to make informed choices. Customers were large enterprises whose decisions had big dollar consequences: pricing, inventory, and marketing allocation.

Success metric: adoption by management, ROI from better decisions, and a move to subscription models as vendors evolved. BI also laid the foundation for data warehouses and ETL pipelines the plumbing later eras would rely on.

ML (Machine Learning) predictions replace static dashboards

Machine learning shifted the promise from describing the past to predicting the future. ML isn’t a single product but a set of techniques that let systems learn patterns recommendations, fraud detection, demand forecasting. Its commercialization accelerated as larger datasets and compute made models useful in production. (Timeline of ML milestones is long from perceptrons to ImageNet and modern deep learning.)

Business sales example: Netflix used ML for recommendations (watch time → retention); Amazon used ML for recommendations and dynamic pricing; banks used ML for fraud detection. The product pitch became “we will increase revenue (or reduce losses) by X% using model-driven predictions.”

Success metric: measurable impact on key business metrics (conversion, churn, fraud rate) and repeatable MLops pipelines. Winning companies built both models and the integration into products and workflows the second part mattered as much as the model.

AI (Artificial Intelligence) foundation models, agents, and ubiquity

“AI” is a broader, more emotionally charged badge than ML. It promises not just predictions, but agency: systems that write, design, plan, and interact. The recent leap in capability comes from large foundation models and multimodal systems, and the market’s attention has become concentrated on a smaller set of platform players. OpenAI is the obvious poster child widely integrated and publicly visible and it’s now part of a small club of companies shaping how enterprises adopt AI. Others Anthropic, Google/DeepMind, Microsoft (as a partner and investor), Nvidia (as the infra champion) are also core to who wins in the AI era. Recent reporting and market movement underscore how concentrated and influential these players are.

Business sales example: AI is sold as both a strategic platform and as task automation. Microsoft + OpenAI integrations sell enterprise productivity gains; Anthropic partners with platforms and enterprise vendors to bring chat/agent capabilities into products; Nvidia sells the hardware that makes large models economically viable. Sales morph into partnerships (platform + integration) and usage-based monetization (API calls, seats for AI assistants, compute consumption).

Success metric: ecosystem adoption and sticky integrations. The winners aren’t just model makers they are the platforms that make models reliably usable within enterprise apps, the cloud vendors that provide infra, and the companies that embed AI into workflows to measurably lower costs or multiply revenue.

What’s next? Predicting the post-AI acronym

Acronyms rise from what businesses need to sell next. Right now, AI sells capability; tomorrow, the market will demand something different: not raw capability but safe, contextual, composable, and human-centric value. Based on where the money, engineering effort, and regulatory focus are going, here are a few candidate acronyms and my pick.

Candidate futures (short list)

  • CAI: Contextual AI
    Focus: models that understand user context (company data, regulations, customer history) and deliver context-aware outputs with provenance. Selling point: trust and relevance. Businesses pay for AI that “knows the company” and can operate under constraints.
  • A^2I / AI²: Augmented & Autonomous Intelligence
    Focus: agents that both augment humans and act autonomously on behalf of businesses (book meetings, negotiate, execute trades). Selling point: time reclaimed and tasks delegated with measurable outcomes.
  • DAI: Distributed AI
    Focus: moving models to the edge, on-device privacy, and federated learning. Selling point: privacy, latency, and regulatory compliance. Monetization: device + orchestration + certification.
  • HXI: Human-Centered Experience Intelligence (or HCI reimagined)
    Focus: design + AI that measurably improves human outcomes (productivity, wellbeing). Selling point: human adoption and long-term retention; less hype, more stickiness.
  • XAI: Explainable AI (commercialized)
    Focus: regulations and auditability breed a market for explainable models as first-class products. Selling point: compliance, audit trails, and legally defensible automation.

My prediction (the one I’d bet money on)

CAI: Contextual AI.
Why? The immediate commercial friction after capability is trust and integration. Companies will not pay forever for raw creativity if outputs can’t be traced to corporate data, policies, and goals. The era of foundation models created broad capabilities; the next era will productize those capabilities into contextualized, policy-aware services that integrate directly into enterprise systems (CRMs, ERPs, legal, finance) and produce auditable actions. In short: AI + enterprise context = the next product category.

Concrete signs for CAI already exist: enterprises demanding model fine-tuning on private corpora, partnerships between model-makers and enterprise software vendors, and regulatory attention pushing for explainability and provenance. Those are the ingredients for a context-first commercial product.

(If you prefer the agent narrative, A^2I where agents actually do things reliably and accountably is a close second. But agents without context are liability; agents with context are product.)

What winning looks like in CAI

If CAI becomes the next category, how do businesses win?

  1. Data integration champions vendors that make it trivial to connect enterprise data (ERP, CRM, contracts) to models with privacy and governance baked in. The sales pitch: “We connect, govern, and make AI outputs auditable.”
  2. Actionable interfaces not just a chat box, but agents that produce auditable actions inside workflows (e.g., “Create invoice,” “Propose contract clause,” “Adjust inventory reorder”). The pitch: “We reduce X hours/week for role Y.”
  3. Regulatory and risk products explainability, model cards, audit logs, and compliance workflows become table stakes. Vendors packaging those for regulated industries will command higher multiples.
  4. Infra + economics hardware and cloud vendors that optimize cost/performance for fine-tuned, context-rich models (Nvidia-like infra winners) will capture a slice. Recent market moves show infrastructure captures enormous value; watch the hardware and cloud players.

Practical advice for sellers and builders today

  • If you sell to enterprises: stop pitching “we use AI.” Start pitching what measurable outcome you deliver and how you keep it governed. Show integration architecture diagrams: where the data lives, what’s fine-tuned, and where the audit logs are.
  • If you build products: invest in connectors, provenance, and reversible actions. A product that lets customers roll back an AI decision will win trust and enterprise POs.
  • If you’re an investor or operator: look for companies that own context (industry datasets, domain rules, vertical workflows). Horizontal foundation models will be commoditized; contextual wrappers will be the economic moat.
  • If you’re an infra player: optimize for cost + compliance. The market will pay a premium for infra that matches enterprise security and cost constraints.

Example scenarios; how each era turned into commercial value

  • BI era: a retail chain buys a BI suite to consolidate POS data across stores. Result: optimized promotions, fewer stockouts, 3% margin improvement. The seller (BI vendor) expanded into recurring maintenance and cloud hosting.
  • ML era: an e-commerce platform adds recommendation models. Result: personalized homescreens boost AOV by 7%. The ML vendor sells models + integration and gets paid per API call and for model retraining.
  • AI era: an agency uses generative models to prototype marketing copy at scale. Result: faster iteration and lower creative costs; large platforms (OpenAI, Anthropic, Google) sell the models, cloud vendors sell the compute. OpenAI’s integrations made it a visible “winner” for developers and enterprises adopting chat/assistant features.
  • CAI era (predicted): the same retail chain buys a contextual assistant that reads contracts, vendor SLAs, and inventory rules, then suggests optimal promotions aligned with margin and regulatory rules. Result: promotions that respect contracts, better margins, and an auditable decision trail. Pricing: subscription + outcome share.

Acronyms are marketing. Value is behavioral change.

Acronyms succeed when they promise a specific, repeatable business result and when vendors can deliver measurable change in behavior. BI helped managers act on facts. ML helped products predict user intent. AI made interaction and creativity broadly available. The next profitable acronym my money is on CAI (Contextual AI) will sell trustworthy, context-aware automation that actually becomes part of the way companies operate.

If you’re building, selling, or investing: focus less on the label and more on the edges where value is realized integration, governance, measurable business outcomes. That’s where the next winners will be, and where your clients will write the checks.

The Vibe Code Tax

Momentum is the oxygen of startups. Lose it, and you suffocate. Getting it back is harder than creating it in the first place.

Here’s the paradox founders hit early:

  • Move too slowly searching for the “perfect” technical setup, and you’re dead before you start.
  • Move too fast with vibe-coded foundations, and you’re dead later in a louder, more painful way.

Both paths kill. They just work on different timelines.

Death by Hesitation

Friendster is a perfect example of death by hesitation. They had the idea years before Facebook. They had users. They had momentum.

But their tech couldn’t scale, and instead of fixing it fast, they stalled. Users defected. Momentum bled out. By the time they moved, Facebook and MySpace had already eaten their lunch.

That’s hesitation tax: waiting, tinkering, second-guessing while the world moves on.

Death by Vibe Coding

On the flip side, you get the vibe-coded death spiral.

Take Theranos. It wasn’t just fraud, it was vibe coding at scale. Demos that weren’t real. A prototype paraded as a product. By the time the truth surfaced, they’d burned through billions and a decade of time.

Or look at Quibi. They raced to market with duct-taped assumptions the whole business was a vibe-coded bet that people wanted “TV, but shorter.” $1.75 billion later, they discovered the foundation was wrong.

That’s the danger of mistaking motion for progress.

The Right Way to Use Vibe Coding

Airbnb is the counterexample. Their first site was duct tape. Payments were hacked together. Listings were scraped. It was vibe code but they treated it as a proof of concept, not a finished product.

The moment they proved demand (“people really will rent air mattresses from strangers”), they rebuilt. They didn’t cling to the prototype. They moved fast, validated, then leveled up.

That’s the correct use: vibe code as validation, not as production.

The Hidden Tax

The vibe code tax is brutal because it’s invisible at first. It’s not just money.

  • Lost time → The 6–12 months you’ll spend duct-taping something that later has to be rebuilt from scratch.
  • Lost customers → Early adopters churn when they realize your product is held together with gum and string. Most won’t return.
  • Lost momentum → Investors don’t like hearing “we’re rebuilding.” Momentum is a story you only get to tell once.

And you don’t get to dodge this tax. You either pay it early (by finding a technical co-founder or paying real engineers), or you pay it later (through rebuilds, lost customers, and wasted months).

How to Stay Alive

  1. Be honest. Call your vibe-coded MVP a prototype. Never pitch it as “production-ready.”
  2. Set a timer. Airbnb didn’t stay in duct tape land for years. They validated and moved on. You should too.
  3. Budget for the rebuild. If you don’t have a co-founder, assume you’ll need to pay engineers once the prototype proves itself.
  4. Go small but real. One feature built right is more valuable than ten features that crumble.

Final Word

The startup graveyard is full of companies that either waited too long or shipped too fast without a foundation. Friendster hesitated. Theranos faked it. Quibi mistook hype for traction.

Airbnb survived because they paid the vibe code tax on their terms. They used duct tape to test, then rebuilt before the cracks became fatal.

That’s the playbook.

Because no matter what the vibe code tax always comes due.

Stop procrastinating! How to prevent it.

Still trying to stop procrastinating?

stop procrastinating

There are probably numerous days that you site behind the computer to do some research or get some work done and doing a short break to read some news or check social updates and as you done this you aren’t aware that time passes as you jump from one link to another while the time is passing by rapidly. You have probably tried many things, like avoiding to use those sites, setting time aside for short breaks or some other solution but every time you spend more time doing nothing than to spend that time into something productive.

My way of curbing procrastination time to minimum

You probably procrastinate as everyone but you don’t do it so efficiently, there are many ways to curb this behavior especially with avoiding reading the news updates all the time. We live in an era of information overload and we developed a habit of a need to be constantly updated with latest updates.

I am using few tools which are completely free of cost for basic usage you will need to prevent procrastination. Here is how I do it:

I have installed self control app on my computer and you can get it at this link. It is free of cost. Basically what it does is that you add a list of web sites that you want to block for a selected period of time.

You are probably wondering now which pages should I add to this blacklist. Well the obvious sites should be the social networks. Most of the links we click to other sites are coming from there to funny videos or interesting stories. There are ton of these sites, but major one should be Facebook, Twitter, Google+ etc. Though have in mind if you develop for social network logins you have to keep alert to remove them for that period otherwise you can do no related work to it for that day. Other useful list of sites that need to be blacklisted are on your history list in your favorite browser. Go through the history and check all the sites that shouldn’t be there during your work hours and add them to the list.

Next step is to add time inside self control for how long it should be blocked. First advice never do mistake and set it over 24 hours. Perfect time to set the limits is 10-12 hours. You would probably say I don’t work so many hours and I agree you mustn’t but from the time you set the time and all other chores you need to do during the day believe me that is the most optimal time especially if you are working for yourself or being in a startup environment. It is dynamic over the day so keep on tracking your time that way.

So next step is to activate the self control and you magically stop procrastinating. Wrong. Keep on reading.

Continue reading “Stop procrastinating! How to prevent it.”