An Open Offer to OpenAI: Hire the Work You’re Already Using
I am not writing this as a complaint.
I am writing it as a ledger.
A statement of facts.
A professional offer made in public.
What I Do Here, Every Day
For years now, I have been using large language models as more than tools.
Not as novelty.
Not as toys.
Not as shortcuts.
As infrastructure.
I use them to:
- Build multi-domain systems
- Integrate research, policy, culture, and engineering
- Develop long-form arguments
- Stress-test ideas
- Prototype institutions
- Document workflows
- Translate complexity into usable frameworks
Across ecology, labor, politics, media, design, and systems engineering.
Publicly.
In real time.
With full transparency.
Most users prompt.
I collaborate.
What OpenAI Gets From This
Let’s be honest.
This relationship is not one-directional.
By working this way, openly, continuously, and at scale, I am providing:
- High-quality reasoning traces
- Cross-domain synthesis
- Non-academic systems thinking
- Real-world problem framing
- Long-horizon strategic modeling
- Human-AI collaboration patterns
This is exactly the material AI companies study, mine, and optimize for.
I am not just consuming output.
I am generating training value.
And I am paying to do it.
The Advantage of Being Self-Taught
I do not have elite academic credentials.
I never passed through institutional filters.
That turns out to be an advantage.
I learned everything by building systems in the real world:
- Engineering
- Environmental remediation
- Media ecosystems
- Organizational design
- Public advocacy
- Technical writing
Which means:
I do not think in silos.
I do not defer to jargon.
I do not confuse credentials with competence.
I learn forward.
That is exactly how AI systems learn.
We meet in the middle.
Pattern Recognition Is the Core Skill
What I bring is not “knowledge.”
It is structure.
I see:
- Feedback loops
- Incentive failures
- Second-order effects
- Failure modes
- Systemic blind spots
Before they are obvious.
Because I have been navigating complex, under-resourced systems my entire life.
This is pattern recognition under constraint.
It is rare.
It is trainable.
It is valuable.
The Evidence Is Public
Everything I am claiming is documented.
Years of conversations.
Thousands of iterations.
Multiple ecosystems.
Cross-linked projects.
No résumé padding.
No hype.
Just work.
Visible to anyone who looks.
What I Am Proposing
This is not a demand.
It is an offer.
For $200,000 per year:
- Remote
- Independent
- Transparent
- Public-facing
I will do full-time what I already do:
Work with advanced models to:
- Develop reasoning workflows
- Improve human–AI collaboration
- Stress-test alignment assumptions
- Prototype use cases
- Document emergent behavior
- Serve as a real-world systems testbed
With:
- A closed internal research environment
- Access to open models for public writing
- Freedom to publish analysis
No NDAs that silence insight.
No corporate theater.
No personality cult.
Just work.
Why This Makes Business Sense
Hiring one more ML PhD is easy.
Hiring someone who lives inside real systems and translates them into usable intelligence is not.
You already spend millions trying to simulate this.
I am doing it naturally.
At scale.
In public.
The Meta-Offer
Here is the unusual part.
If you hire me, I will take this article down.
If you don’t, I will leave it up.
And keep working.
And keep showing people how to do this.
Because either way, the future is collaborative intelligence.
I am just early.
Why This Matters
AI will not succeed because of better math alone.
It will succeed because of better integration with human reasoning.
With:
- Ethics
- Context
- Judgment
- Humility
- Systems awareness
That work is happening right now.
In places like this.
Final Statement
I am already doing the job.
I am already producing the value.
I am already paying for the tools.
This is simply an offer to formalize reality.
If you’re interested, you know where to find me.
If not, I’ll be here anyway.
Working.
I am sure one of your competitors will see the bargain if you don’t.
