Key Takeaways:
- The history of technology shows a consistent pattern: even when new tools disrupt workforces and unsettle societies, over time they extend both the quality and quantity of human life. That’s as true of the cotton gin as it is of today’s AI systems, which many see as the next revolution in human capability.
- Synthetic biology illustrates how AI’s breakthroughs can move from research to commercial impact. Tools like AlphaFold, along with companies such as Colossal, are beginning to show how AI-assisted science can accelerate drug discovery, biodiversity protection, and even address fashion’s long-standing waste problem through innovations like plastic-eating microbes.
- For all the optimism, the question of control remains unresolved. From legislative gaps to the risk of synthetic “everything,” there is a clear need for human judgment in the loop. The challenge is not whether to use these tools, but how to guide them without losing sight of truth, creativity, and dignity.
When it comes to technology, regardless of the consequences, we humans are inexorably drawn to optimism, and we have been consistently proven right to follow our intuition to invent, embrace innovation, and invent again. Using our ability to imagine dystopias (often more than our systems of governance and regulation) as guardrails, technology has, so far, always delivered outcomes that extend the quantity and quality of human life if we judge them on a sufficiently long timeline.
Take, for example, the Cotton Gin. Invented in 1793, The cotton gin used a combination of wire teeth on a rotating cylinder and a grate or screen to pull the cleaned cotton fibers through. This was pure mechanical innovation, and it significantly increased the speed and efficiency of cotton processing. This had a major impact on the cotton industry, particularly in the Southern states, where cotton was a major, labor intensive crop. It made it cheaper and easier to use cotton in mills in the short term, and it lead to the proliferation of cotton and cotton-rich products in the longer run.
At the time, its commercial use also forced huge percentages of the labor force out of work. In the face of this radically disruptive technology, the cotton gin’s inventor, , Eli Whitney, wrote this to his father: “One man and a horse will do more than fifty men with the old machines… T’is generally said by those who know anything about it, that I shall make a Fortune by it.” And indeed, fortunes were made. And the potential catastrophic implications to the incumbent state were far overshadowed by the entire industry that was born from this simple invention that fueled regional growth for over a century. (That growth, it must be pointed out, was also tied to the specter of slavery, which was only abolished in the United States towards the end of that period.)
And so that march of technological optimization of human capability and capacity continues into this century. Andrew Ng, who was a founder of Google Brain, and is currently an Amazon Board, member recently said something that echoed from one revolutionary period to another. “Just as the Industrial Revolution freed up a lot of humanity from physical drudgery, I think AI has the potential to free up humanity from a lot of the mental drudgery”.
These parallels aren’t just an academic exercise. Knowledge work has become the predominant form of human endeavor in many markets (not all), and there is a very real sentiment amongst the biggest proponents of AI that augmenting or automating at least part of that work is the next logical step in human progress.
From simple stone tools to complex genetic engineering and information technology, the history of technology is the history of the invention of tools and techniques by humans, which are then placed in human hands – often fewer hands than were needed before – and which, in turn, increase output, quality, creativity, and any number of other direct and indirect variables.
The speed at which the invention of these tools and technologies are being delivered is now becoming exponential. Technology begets technology at a quicker pace than any linear projection would have you believe. And as the application of machine learning and AI becomes the ubiquitous open source ingredient layer to everything, and is mutually accelerated by the arms race for processing speed where we measure success in petaflops (a quadrillion floating-point operations per second) and trillions of parameters (the internal, numerical values that LLM’s learns during training to process and generate text), there is a possibility that we could see what OpenAI CEO Sam Altman describes as “the takeoff” towards the destination of machine superintelligence and digital intelligence “too cheap to meter”. Or abundance of capability and capacity for knowledge work, in other words.
As knowledge-seeking animals, this feels instinctively challenging and a bit confrontational, but the potential value of tools of this potential skill and this promised scale to increase our understanding of the world around, and solve some our most pressing problems around the quality and quantity of life is as tempting as Pandoras’ box. A box which, while it contained all the evils of the world in short term, also contained hope in the longer run.
As Marc Andreesen said, “‘Technology is the glory of human ambition and achievement, the spearhead of progress, and the realization of our potential.” And while decades in the making, with this new computational prowess we are continually (it sems like it’s happening daily now) seeing breathtaking exponential breakthroughs, new modalities, new ideas, and new inventions – perhaps none more exciting than in the field of biotechnology. From the first gene sequenced of a bacteriophage in 1977 to the success of the human genome project in 1990 the launch of Crispr Cas-9 in 2012, we now see the use of all our new compute power giving rise to the new discipline of Synthetic Biology where we can truly see what a condensed lens on all the practical application of the positive outcomes of AI could look like.
George Church, American geneticist, molecular engineer, chemist, serial entrepreneur, and pioneer in personal genomics is considered the father of Synthetic biology. That field, in its practical application, throws a tremendous amount of Math (synthetic data) at genomic sequences, treating biological components (like DNA, RNA, proteins) and systems the same way someone might treat engineering parts, devices, and circuits – applying engineering methodologies such as design, abstraction, standardization, and modularity to living organisms.
This matters to a wider range of fields, because DNA is our biological firmware, a specific set of instructions or microcode embedded within our genes to control its operation and function. Controlling this can revolutionize drug discovery, speed the development of new cell therapies and mRNA vaccines, accelerate the treatment of infectious diseases and cancer, be used to engineer crops that are more resilient to pests and diseases, produce biofuels and other chemicals in a more sustainable way, and promises to add more, better years to the human lifespan and the human experience.
One powerful example of how this is already being used is being pioneered by Andrew Adams at Eli Lilly and Company. In seeking treatment to the debilitating disease of Alzheimers researchers discovered the Christchurch Mutation: just one copy of the Christchurch variant conferred protection against Alzheimer’s disease, even in individuals with genetic predispositions to the disease. Being able to apply Synthetic Biology to enhance, and deliver this modification would shift the field of Alzheimers from treatment to prevention.
More specifically for AI, we recently saw the open source release of Googles’ Alphafold, their free AI Model to predict the structure of all of life’s molecules. Following our hardware example, if DNA is our firmware, then Proteins are the Cookies that get left behind and tell the cells what to do. Proteins naturally fold into key like shapes that can only open very specific locks. Given that any given protein can theoretically adopt roughly 10^300 different configurations, it would take longer than the age of the universe for a protein to fold into every configuration available to it, even if it attempted millions of configurations per second. The fact that Google just gave away access to over 200 million proteins and provided a free AI tool for scientists to experiment with is in itself exponential.
So a lot of applications of AI in the creation of novel science, in narrow domains. But what about wider applicability? What does protein folding have to do with how the fashion industry might be feeling about AI?
Like a lot of academic initiatives, the outputs can seem interesting but arcane until the commercial sector takes those foundations and begins to build products or services that are visible to the layperson. We can see that happening with our biotechnology and genomics example with the transition from research to commercial application. Enter Colossal founder Ben Lamm, a protégé of George Church. A self described ‘serial entrepreneur,’ he started Colossal with $15M in 2019 – the company is now worth $10.2B – with the sole purpose of advancing the cause of de-extinction, using advanced gene editing technology to rebuild the DNA of lost megafauna and other creatures. With earth on track to lose between 30% and 50% of its biodiversity by 2050, maintaining this integrity is vital to life on earth. From seed banks and food security, to the known effects of trophic cascades (the establishment of apex predators that keep entire ecosystems thriving and in check) his work is mission-critical for anyone concerned with protecting biodiversity.
Colossal’s most recent accomplishment, the birth of three dire wolves ( Romulus, Remus, and Khaleesi ) is considered “the world’s first successfully de-extincted animal”, the first of their kind to be alive in over 10,000 years. (There is some skepticism over that definition, and whether or not these animals would be better described a new variant of engineered contemporary wolf instead.) While Woolly Mammoths and Dodo Birds are also on their roadmap, the work at Colossal is not simply to create novel creatures for 21st century Jurassic Parks; instead it is about building the technological capabilities to stop the current waves of extinction in their tracks.
Colossal also seeks to use Synthetic Biology in other useful ways that help to demonstrate the pathway from narrow research to commercialization to specific and unpredictable new application. Take the establishment of their spin off company Breaking, which specializes in plastic degradation using synthetic biology having successfully edited the genes of an Amazonian microbe they call ‘X-32’, from a microbe that enjoys eating plastic to a microbe that is voracious for them, digesting polymers in weeks instead of decades or centuries and leaving behind carbon dioxide, water, and biomass.
This novel solution could swiftly help us address the problem of plastic pollution on our planet, and microplastics in our bodies, all with the enhanced use of data and compute. It’s an invention with the potential to change how we think about what has, for decades, been an inextricable part of fashion’s growth problem – and it originates from AI-supported research in a field that’s orthogonal to fashion.
From synthetic data and synthetic biology naturally flows the idea of ‘synthetic everything’. Synthetic Humans (Robots) Synthetic Media (Google VEO, Midjourney), Synthetic News and even Synthetic Truth. From the wellspring of AI we could easily see a flood of similarly seemingly-uncorrelated inventions, disruptions, and step changes. So much so that me attempting to predict them here would be an exercise in blind guessing; it’s impossible to say what will come about if AI ends up fulfilling its maximum potential.
That there are bad actors and bad intent, we are all clear on. And the need for us to be diligent and leery, especially in this current era, of dystopias of our own making, is real. Truth, identity, dignity, creativity – these are all core spaces of the human experience that we may need to reckon with sharing with non-human entities in the future.
Against that backdrop, the demand for the ethical human in the loop, capable of exercising taste and judgment, couldn’t be more dire – and we must all remain diligent to these poor outcomes.
And that’s more than just an idealistic call to action. Even today, in the US, there is legislation built into the 2026 budget bill that codifies a 10-year moratorium on state and local governments enforcing any law or regulation concerning AI models, AI systems, or automated decision systems. This would prevent states from enacting new AI regulations and also block the enforcement of existing ones – breaking down a framework I think any AI proponent would argue we clearly need if we’re to design a new AI toolset, a new way of interacting with technology, and a new era of augmentation of human capabilities whose promises far outweigh its perils.
At this year’s World Economic Forum in Davos, Andrew Ng, ever the tech optimist, was asked directly about the perils of AI and Artificial General Intelligence ( AGI ) and he replied “Do we think the world is better off with more intelligence? We use, primarily, human intelligence, now we have artificial intelligence. I think that intelligence, net net, tends to make societies wealthier, make people better off….intelligence can, in some cases, be used for nefarious purposes, but on average, I think it actually makes us all much better off”.
In Pandora’s fable, Hope never left the box.