1 Year in the Trenches: The Hard Truths About AI Implementation

We’ve spent a full year not in the research lab, but on the ground, taking AI proofs-of-concept and making them usable products—from automated testing to accessibility solutions. This journey has revealed that the current AI hype cycle is often focused on the wrong things.

Here’s what I’ve learned about what truly separates successful AI adoption from failed pilot projects.

AI is a Tool or a Feature, Not a Viable Product

The biggest misunderstanding in the current market is the attempt to sell AI as "the product." Companies are slapping "AI" on their marketing, often resulting in generic "✨" buttons and simple API calls.

The truth is, AI is not a viable product in itself; it's either a feature or a powerful underlying tool.

The best AI applications work beneath the surface to enhance the core value proposition. Think of Amazon's AI: you don't see a chatbot on the homepage; you see AI powering demand forecasting, product ranking, and fraud detection. The goal isn't to build a new ChatGPT; it’s to build a tool that helps users get to "the thing" they actually want, faster and more accurately.

The Best Use of AI is to Trivialized Hard Problems

The true power of Large Language Models (LLMs) isn't in flashy new apps, but in their ability to trivialize problems that were once extremely difficult or costly to solve.

For example, a project to create context-aware communication cards for nonverbal people once required a year of academic research to achieve a 55% accuracy rate. When replicated with ChatGPT 3.5, the accuracy instantly jumped to 82% with just a weekend of focused work.

This is the LLM's superpower: instantly solving complex, niche problems that used to require dedicated teams and months or years of effort.

The Real Boom is in Internal Tools, Not New Startups

Skeptics often ask why we aren't seeing a massive "Startup Boom" like during the dot-com era. The reason is simple: coding is rarely the hardest part of building a startup.

What we are seeing is an enormous boom in internal tools and neglected features. Projects that were once relegated to the "Nice to have" bucket because of a lack of engineering capacity are now being built by engineering managers in a few hours using LLMs. AI is finally making previously non-viable projects incredibly helpful and productive by eliminating the time-suck of boilerplate coding.

The Truth About Model Maturity and the "AI = Agile" Rule

We are likely nearing the top of the S-curve for LLM capability—meaning the jump from one major model (like GPT-4 to GPT-5) will be less dramatic. However, this is good news:

1. Reverse FOMO is Dead: You don't need to wait for the "perfect" model; what we have now is powerful enough for most practical applications.

2. Focus Shifts to Speed and Cost: Future innovations will be centered on creating cheaper, faster, and open models that can run efficiently on local devices.

It’s crucial to ignore those who try to mystify AI. Implementing it is simple; you just need to start using tools like Claude Code for small tasks. You'll naturally learn better prompting and optimization as you hit bottlenecks.

Treat AI like the next Agile: it’s a powerful but limited tool that speeds things up. Don't let people preach that your problem is solved by "more AI" when the solution is often outside the tool's limits.

The AI Impact: Senior Upskilling, Junior Problem

This new wave of AI creates a problem for less experienced developers. LLMs often fix problems but miss the root cause.

Recognizing when an AI-suggested fix is overly complex and a simpler solution exists requires senior-level skills—valuing simplicity and having knowledge gained from past bugs. Juniors who rely on LLMs to do their problem-solving risk not developing this critical skill, which hurts their code reviewing and debugging abilities. Consequently, some companies have halted junior hiring entirely.

Ultimately, while we may be in a bubble fueled by VC money, the result is a flood of great, free tools that boost productivity. The key is to leverage them now to trivialized the tedious, hard problems and focus your unique senior talent on the strategic challenges AI can't yet solve.