How to Create Jobs in the Age of AI With Drug Discovery Technology
- mansour ansari
- 13 minutes ago
- 6 min read
I saw an MIT study tonight about AI and job losses, and that is alarming. So, AI can already replace % 11.7% of the US workforce. but what the MIT “11.7%” really is:
So, MIT + Oak Ridge built a model called the Iceberg Index that looks at:
151M U.S. workers
900+ occupations
32,000+ skills
They asked: “With AI that exists today, what fraction of current work tasks could AI technically do?”
Their answer: AI is already capable of doing tasks equal to 11.7% of total U.S. wages — roughly $1.2 trillion of labor. arXiv+1 (See it here!)
Important nuance that you reading this should know:
It’s technical exposure, not “we will definitely fire these people tomorrow.”
It’s mostly cognitive / office work: HR, logistics, finance, admin, some healthcare back-office, etc. Fast Company+1
They explicitly say this is not a forecast of job loss timing, just what’s possible if companies fully applied current AI.
If you convert 11.7% of the ~167M workforce, you get ~19–20M workers’ worth of labor theoretically automatable right now. That sucks, even if half of it is correct. This motivated me to write this post:
Every time a new wave of technology arrives, someone says,“ Shoot!… there go the jobs.”
I’ve been around long enough to see a different pattern. Back in the late 90s and early 2000s, when I was building early broadband video systems, I accidentally helped create an entirely new profession.
We gave storm chasers and photojournalists laptops, air cards, satellite uplinks, and live streaming tools. Suddenly:
TV stations could air live tornado footage in minutes.
Emergency managers had real-time field intelligence.
Local news operations began hiring, contracting, and retaining people who could run this tech.
I watched dozens of new roles appear out of nowhere: storm chasers, mobile digital photojournalists, field producers, tech ops people who didn’t exist on org charts before.
Nobody called it a “job creation program.” But that’s what it was.
Now, in my 70s, I find myself doing it again, this time in drug discovery, at a moment when everyone is worried that AI will wipe out human work.
Let me tell you why I believe the opposite can happen.
AI Isn’t Just Replacing Work, it’s Reshaping What Work Is
In drug discovery, AI and automation can:
Dock millions of compounds against a protein target
Score binding affinities in bulk
Generate pretty 3D visualizations and reports
You could look at that and say, “Great, fewer people needed.”
But that’s only half the story.
Under the hood, tools like QuantumCURE Pro™ still need:
Careful protein prep
Entropy choices (QRNG, PRNG, D-Wave, etc.) That is a lot of human work there. i did that for months and should do a lot more. Fresh Entropy needed!
Sensible filters and thresholds. Needs a Human touch! Graduate-level or PhD-level experts required.
Human judgment about which hits matter.
Wet-lab follow-up and real-world interpretation
And around this workflow, entire new categories of human work can emerge, just as storm chasers did around live video.
New Jobs Around a Quantum-Enhanced Docking Engine
Here are some of the roles I can see forming around the systems I’m building. that is not including CFO, marketers and Business operation roles created in Oklahoma City and remote jobs.
1. Docking Fellows & Simulation Operators
These are people who:
Prepare protein structures from cancer and disease targets
Configure docking campaigns (which entropy, how many compounds, what filters)
Run, monitor, and troubleshoot simulations
Deliver shortlists and Golden Lists to labs and clinics
They might work:
Inside hospitals and universities that use QuantumCURE Pro™
Inside small startups offering “Docking-as-a-Service” in their region
As part of my own Fellowship or Citizen Scientist programs
Translation: that’s not AI replacing scientists. It’s AI giving them a bigger engine to drive.
2. Zaban Glyph & Entropy Analysts
My system doesn’t just output scores. It produces symbolic glyphs — compression of collapse patterns and entropy fingerprints.
That opens an entirely new specialization:
Recognizing recurring glyph patterns across runs
Studying how different entropy sources (PRNG, QRNG, D-Wave) behave
Tagging CRISP-G / Zaban glyphs that show “interesting” or rare signatures
Helping train AI models to interpret these patterns over millions of simulations
This is a hybrid job: part data scientist, part pattern-spotter, part storyteller.
We didn’t have “Glyph Analysts” 10 years ago.In 5–10 years, I suspect it will be a real job title.
3. Citizen Scientist Coordinators
One of my core goals is to open this engine up to citizen scientists:
Students
Retired engineers
Curious individuals with a decent computer and a good internet connection
They can run simulations from home, contribute compute power, and help explore huge compound libraries.
But they’ll need:
Onboarding and training
Clear tasks and goals
Help interpreting their results
Someone to track which runs contributed to promising leads
That means coordinators, moderators, educators — people whose job is to welcome new contributors, keep the system healthy, and highlight success stories.
4. New Wet-Lab Work
If QuantumCURE Pro™ and Zaban-CURE do what they’re designed to do, that is generate high-confidence lead lists and that increases demand for:
Lab technicians to run assays
Chemists to synthesize compounds
Biologists to validate mechanisms of action
Data analysts to merge docking, omics, and wet-lab results
AI doesn’t eliminate this work; it funnels more promising candidates into it.
Instead of a few well-funded pharma labs doing everything, we can empower:
Smaller labs
Regional hospitals
University groups
Citizen-science aligned wet labs
to step into serious discovery with a lower barrier of entry.
Building an Ecosystem, Not Just a Product
It’s one thing to say, “Sure, jobs will appear.”It’s another to design your platform so they have somewhere to land.
Here’s how I think about it:
A. Formal Training & Certification
I can’t control the whole job market, but I can do this:
Offer QuantumCURE Pro™ training tracks
Certify “Docking Associates,” “Glyph & Entropy Specialists,” and “Simulation Architects,” and these are only the ones I can think of now. As I build this platform, I need more potential workers. Human workers, not AI or Robots.
Publish clear curricula, exercises, and example projects
That way, when someone spends months learning this system, they walk away with skills they can:
Use with me
Take to a lab or startup or start a startup!
Put on a resume or LinkedIn profile
B. Fellowships With Real Output
I can create limited-term Fellowships where:
The Fellow runs real campaigns on real targets
Learns the full pipeline: docking → scoring → IC₅₀ → glyphs → reports
Produces something that lab partners, investors, or patient advocates can actually use
Even a small number of Fellows per year means new professionals entering the ecosystem.
C. Local QuantumCURE Nodes
Just like early storm chasers built local businesses, I want people to:
License the engine
Wrap it in local services (consulting, local trials, niche disease focus). I will expand to different disease research and even material science, like the carbon capture portal....
Hire small teams around it.
My job is to make the platform robust, documented, and affordable enough that this is possible.
Micro-Ownership for Citizen Scientists
There’s one more angle I care about.
If a citizen scientist’s simulation ends up contributing to a lead compound that goes to the Wet-List and, one day, maybe beyond…
I want:
Their name in the story
Their contribution is logged and credited.
Ideally, a way for upside (or at least recognition) to flow back to them
That’s not the language of “job replacement.” That’s the language of shared discovery. ($$$$)
Why This Matters to Me Personally
I’m not 25, building an app to optimize ad clicks.
I’m in my 70s, lifting weights in a cold Oklahoma garage gym, running QRNG-seeded simulations from a back office, trying to build something that:
Helps find better drug candidates
Gives ordinary people a way to participate in serious science
Creates new kinds of work in an AI-heavy world
I’ve already seen one wave of tech accidentally create jobs for storm chasers and digital photojournalists.
Now I’m deliberately aiming to do it again, this time for Docking Fellows, Glyph Analysts, Citizen Scientist Coordinators, and lab teams who want to stand on the shoulders of quantum-enhanced tools instead of being crushed under them.
If AI is going to rewrite work, let’s make sure it also writes in new roles, new ladders, and new ways for people to be useful. I want to be useful in my golden years and I am doing just that!
That’s the future I’m building toward with QuantumCURE Pro™ and the broader QuantumLaso ecosystem.
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