“Technology is a useful servant but a dangerous master.”
— Christian Lous Lange
This week’s Cryptogeddon Briefing is a little different.
Normally, this space is where I explore the technologies, cyber threats, geopolitical shifts, and emerging ideas that inspire my writing—and, ultimately, the world of Cryptogeddon. Most weeks, that means discussing artificial intelligence in the context of cybersecurity, autonomous systems, espionage, or the changing balance of power between nations.
This week, though, I found myself thinking about AI from a very different perspective.
The idea came after a conversation over dinner.
The topic of artificial intelligence came up, and as it so often does these days, opinions around the table were mixed. Some people were optimistic. Others were skeptical. The concerns were familiar: AI-generated artwork replacing artists, copyright, deepfakes, misinformation, job displacement, and the growing uncertainty surrounding where this technology is taking us.
They’re fair concerns.
In fact, they’re concerns I share.
Like every transformative technology before it, artificial intelligence will undoubtedly be used for both good and bad. It will create incredible opportunities while introducing entirely new risks. Pretending otherwise would be naïve.
But as I listened to the discussion, I couldn’t help thinking about another side of AI—one that rarely dominates headlines or social media debates.
It reminded me that while we spend enormous amounts of time asking what AI might take away from us, we spend surprisingly little time asking what it might give us.
That thought brought me to my daughter.
“The good physician treats the disease; the great physician treats the patient who has the disease.”
— Sir William Osler
She has cystic fibrosis.
If you’ve never known someone with CF, it’s a genetic disease caused by mutations in the CFTR gene. Those mutations disrupt how salt and water move through cells, producing the thick mucus that damages the lungs and digestive system. For decades, treatment focused primarily on managing symptoms: daily physiotherapy, inhaled medications, repeated courses of antibiotics, and frequent hospital stays whenever infections became severe.
When my daughter was born, there was hope—but there were also countless unanswered questions.
Researchers had identified the genetic cause of the disease, but understanding exactly how hundreds—and eventually thousands—of different mutations affected the CFTR protein required years of painstaking laboratory research. Every discovery was earned through thousands of experiments, each one consuming time, funding, and the efforts of countless scientists.
Drug development was no different.
Researchers would identify promising compounds, synthesize them, test them in the laboratory, modify them, and begin the process again. Most candidates failed. The few that succeeded often required more than a decade of research and billions of dollars before they ever reached patients.
Thankfully, that work paid off.
Today, my daughter is nineteen years old. She lives what is, for all practical purposes, a normal life. She still has cystic fibrosis. She still follows a treatment regimen every day. But she’s healthy, active, independent, and planning her future just like any other young adult.
That’s nothing short of extraordinary.
And while AI didn’t create those first breakthrough therapies, it’s beginning to change how the next generation of discoveries will happen.
Artificial intelligence doesn’t replace scientific curiosity—it amplifies it.
This is where artificial intelligence becomes genuinely exciting—not because it’s generating artwork or writing marketing copy, but because it’s helping scientists ask better questions.
Modern AI systems can analyze enormous biological datasets in hours rather than months. They can compare thousands of genetic mutations, identify patterns that would be nearly impossible for humans to detect unaided, and predict how specific mutations alter the shape and function of proteins. Instead of relying entirely on trial and error, researchers can now use AI to prioritize the most promising hypotheses before stepping into the laboratory.
That doesn’t replace science.
It makes science more efficient.
One of the most exciting developments has been AI-assisted protein modelling. Understanding exactly how a mutation changes the three-dimensional shape of a protein—and how a potential drug might restore its function—once required years of painstaking structural biology. Today, AI systems such as AlphaFold can generate remarkably accurate structural predictions in hours, allowing researchers to focus precious laboratory time where it’s most likely to produce meaningful results.
AI is also transforming medical imaging. Researchers are using machine learning to identify subtle changes in CT scans that may indicate disease progression earlier than conventional methods. They’re studying how bacterial populations evolve inside the lungs of people with cystic fibrosis, helping predict antibiotic resistance and personalize treatments. AI is helping researchers identify better candidates for clinical trials, reducing the time required to evaluate promising therapies.
None of these breakthroughs eliminate the need for scientists.
They eliminate wasted effort.
Every experiment that doesn’t need to be performed because AI helped identify a dead end means researchers can spend more time pursuing ideas with genuine potential. Every month saved in research is another month that a promising therapy could reach the people waiting for it.
And while cystic fibrosis is one example, the same technologies are now accelerating research into cancer, Alzheimer’s disease, rare genetic disorders, antibiotic discovery, and countless other medical challenges.
That’s a much bigger story than AI-generated artwork.
And yet, both conversations are about the same technology.
Technology itself is remarkably neutral.
Electricity powers hospitals.
It also powers electric chairs.
The Internet connects families across continents.
It also spreads misinformation across them.
Encryption protects political dissidents.
It also protects organized crime.
Artificial intelligence belongs in exactly the same category.
The same machine learning algorithms helping researchers discover life-saving medicines can also help militaries identify targets faster, guide autonomous drones, improve missile accuracy, or analyze satellite imagery to track troop movements. Those very same technologies can also detect incoming missile attacks, improve battlefield medicine, assist humanitarian rescue operations, strengthen cyber defenses, and protect civilian infrastructure.
The technology hasn’t changed.
Only the objective has.
That’s why I don’t think AI is inherently good or inherently bad.
I think it’s something much simpler.
It’s a multiplier.
Put AI in the hands of a scammer and they’ll scam more people.
Put it in the hands of a military and they’ll build more capable weapons—or more capable defenses.
Put it in the hands of an artist and they’ll create in entirely new ways.
Put it in the hands of a physician or researcher, and they’ll ask bigger questions, analyze more data, and discover answers faster than they could alone.
AI doesn’t determine the outcome.
People do.
The tool simply multiplies whatever intentions we bring to it.
“The future is already here—it’s just not evenly distributed.”
— William Gibson
If you had told me twenty years ago that one day my daughter would wake up, take a handful of pills, complete her treatments, and then go about living what is—for all practical purposes—a normal life, I would have struggled to believe you.
That future wasn’t built by artificial intelligence alone.
It was built by thousands of researchers, physicians, engineers, patients, and families who spent decades advancing science one careful step at a time.
Now, for the first time, many of those same researchers have a tool that allows them to move faster than ever before.
Artificial intelligence won’t replace human ingenuity.
It will amplify it.
And perhaps that’s the conversation we should be having.
Not whether AI can generate a beautiful painting.
Not whether it can replace a writer or an illustrator.
Those are important discussions, and they’re worth having.
But they aren’t the whole story.
The quiet miracles of AI won’t be measured by the pictures it generates.
They’ll be measured by the discoveries it accelerates, the diseases it helps us understand, and ultimately, the lives it helps us save.
Further Reading
- Jumper, J. et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596, 583–589.
- Paul, D. et al. (2021). Artificial Intelligence in Drug Discovery and Development. Drug Discovery Today.
- De Marchis, M. et al. (2023). Machine Learning Applications in Cystic Fibrosis: A Narrative Review.
- Cystic Fibrosis Foundation. Research and Clinical Trials Pipeline.
- Nature Reviews Drug Discovery (2024). Artificial Intelligence and the Future of Biomedical Research.
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