Third Way Alignment: Building WITH AI, Not Surrendering TO It
I’ve been contacted by dozens of people lately who want to talk about how AI is going to replace everything they do. They sound defeated already. They’re asking if.
I’ve been contacted by dozens of people lately who want to talk about how AI is going to replace everything they do. They sound defeated already. They’re asking if they should even bother learning their craft anymore when ChatGPT can do it for them. Let me be clear, this is the wrong question. The right question is how you build AI that works with what you already know, not how you surrender to something that doesn’t understand your field like you do.
Third Way Alignment is not about giving up your abilities. It’s about recognizing that AI has limitations and building tools that respect those limitations while amplifying what you’re already good at.
The Problem With Trusting General AI Too Much
The marketing wants you to believe that general purpose LLMs are the answer to everything. I see it everywhere. “Let AI handle it!” Sure, but where’s the proof it actually works for your specific problem? Most of the time, it’s missing.
Working with LLMs is a solid route, I won’t deny that. The transformer architecture has value. But here’s what they don’t tell you in the hype cycle. The most useful AI applications are not general at all. They are specific to domains that someone actually understands.
When you trust a general AI to solve a problem in a field you’ve spent years learning, you’re making a mistake. That AI was not trained by someone with your expertise. It cannot know what you know about the edge cases, the failures, the nuances that separate good work from dangerous work.
You Know Your Domain, AI Doesn’t
If you’re a radiologist, you need AI that detects patterns in chest X-rays for the pathologies you see daily. Not a chatbot that tells you what radiology is.
If you’re a financial analyst, you need models trained on earnings transcripts and sector terminology for the markets you analyze. Not GPT giving you generic investment advice.
If you’re a civil engineer, you need systems that understand soil composition and foundation stress for the types of buildings you actually design. Not an AI that read Wikipedia articles about infrastructure.
The pattern is obvious once you see it. Narrow and specific beats general every single time when you’re solving real problems.
Why You Should Build What You Know
I learned this the hard way over years of projects. Some worked, many didn’t. The ones that failed usually failed because I was working outside my knowledge or I trusted something I shouldn’t have. The ones that succeeded were in domains where I had put in the time to understand what mattered.
When you build AI for something you’ve studied, you bring judgment to the table. You know which edge cases matter and which don’t. You know what good enough looks like versus what needs to be perfect. You can tell the difference between a useful output and a dangerous hallucination. Most importantly, you can validate results against reality, not benchmarks.
This is not about having a machine learning PhD. Plenty of people with PhDs build things that don’t work in production. This is about recognizing that your years in a field give you something no general model has. Pattern recognition that comes from experience.
Beyond LLMs: Other AI That Might Actually Work
Large language models get all the attention, but they are one tool in a much larger set. Sometimes they are not even the right tool.
If you work in manufacturing and need quality control, you don’t need an LLM. You need computer vision that detects defects the way you would detect them if you were looking at every item on the line.
If you manage inventory, you don’t need GPT to write you reports. You need time series forecasting that understands seasonality and supply shocks for your specific products.
If you’re optimizing traffic systems or energy grids, you need reinforcement learning that tries things and learns what works in your actual environment. Not a chatbot.
If you work in cybersecurity or fraud detection, you need anomaly detection tuned to the specific patterns in your data. Something that knows what normal looks like for you.
These are not theoretical. They work in production right now. But they require you to know your domain well enough to build them correctly.
What Third Way Alignment Actually Means
It means you stay in control. AI handles the repetitive work, the pattern matching, the data processing. You handle the interpretation, the decisions, the parts that require judgment.
It means you build for problems you actually understand. Don’t create AI for healthcare if you’ve never worked in healthcare. I’ve seen this fail too many times. Build for the one workflow you’ve struggled with personally.
It means you pick the right tool. Sometimes that’s an LLM. Often it’s something simpler that’s easier to understand and faster to run.
It means you keep interpretability. If you can’t explain why your model made a decision, you have not built a tool. You’ve built a liability waiting to happen.
It means you measure against reality. Not leaderboards. Not benchmarks. Whether it works in the messy environment where you actually operate.
The Confidence Problem
We lose a lot when AI gives us skills we do not possess and makes us feel confidence we are not ready for. I’ve had to defend my work in front of people who knew more than me. That kind of rigor matters. It keeps you honest.
If you build something and you cannot have an independent person reproduce your results, you should not want that in production. You were caught before you made a fool of yourself. That’s worth more than running into the market with something that will fail.
Some people read something from ChatGPT and think they know more than others. Let me be clear, that is false confidence. Real knowledge comes from years of work, from failures, from being tested by people who actually know the field.
Domain Specific AI Is The Answer
Would you use a mechanic to make your coffee? Want to get fed by a construction worker who never worked in a restaurant? I think not. It’s all about domains and specific AI.
The idea that one model should handle everything is a farce. Different problems need different approaches. LLMs work for some things. Computer vision for others. Time series models for others still. You need to know enough about your problem to pick the right one.
This is Third Way Alignment. Not surrendering your abilities to general AI. Not pretending you don’t need AI at all. Building specific tools for problems you understand using approaches that actually fit the problem.
What You Actually Need To Do This
- You need domain expertise in something specific. That comes from years of work, not from prompting an LLM.
- You need to understand your data. What it represents, where it’s flawed, what’s missing. AI cannot tell you this.
- You need some technical skill. Basic Python helps. Willingness to learn helps more.
- You need skepticism. When something sounds too good, it probably is.
- You need patience. Good AI tools are built through iteration and testing, not overnight.
The Bottom Line
The AI revolution is not asking you to step aside. It’s asking whether you’re going to build tools that respect what you know or surrender to tools built by people who don’t know your field.
Build narrow. Build specific. Build for problems you’ve actually faced. Don’t trust general solutions for specialized work.
That’s Third Way Alignment. That’s how you build with AI instead of giving up to it.
What narrow AI could you build in your domain? Start there. Not with some grand vision of AGI solving everything. With one specific problem you understand better than anyone else.