Everyone agreed the problem was very small. That itself was how it got so big.

It was called Baseline Drift, which sounded like something that happened to boats when you forget to moor them. It did not sound like something that would reorganize biomedical science, but it did — specifically, the part that depended on knowing what counted as ordinary.

Baseline drift meant that nobody could reliably say what a “normal” human outcome looked like anymore.

This was awkward because, until the mid-twenty-first century, biology had relied on a shared set of normative assumptions: how much people should sleep, how fast wounds should heal, how cognition declined with age, how bodies responded to stress, infection, calories, boredom. These weren’t “universal” control groups, of course, because every experiment still had its own controls — but they were population reference frames, the invisible scaffolding beneath public health guidance, drug approvals, actuarial tables, and phrases like “clinically meaningful improvement.”

It began (depending on whom you asked) around 2025 with the publication of a global guideline on using GLP-1 medicines to help manage obesity. Or 2028, when prenatal methylation screens quietly became the standard of care. Or 2031, when adaptive lighting systems tuned to circadian biology spread from hospitals to schools to office parks. Or maybe it was 2034, when insurance stopped covering “general wellness” supplements because everyone was already getting them through food fortification, water treatment, and special perks from their employers.

In truth, there was no one moment where someone stood up and said, “We are now enhancing the species.”

There was only the slow tide of policies, memos, and social proof.

By 2040, most children in wealthy countries had received personalized micronutrient profiles before age three. By 2045, gut microbiome calibration was as routine as dental cleaning. By 2050, short-course somatic gene therapies corrected rare metabolic inefficiencies the way eyeglasses once corrected vision. While no single person was wholly optimized on all fronts, such interventions nudged distributions. Means crept upward. Variance narrowed in some places and exploded in others.

People slept eight-point-two hours instead of the six-point-nine that they had in the 2020s. They recovered from illness faster. They aged more slowly, but unevenly, and in unpredictable ways. Cognitive decline no longer followed a singular curve, as it had before the introduction of anti-amyloid therapies. Neither did pain tolerance, emotional regulation, immune response, or stress recovery.

Humans were healthier than ever. They were also less similar to one another than they had ever been.

Differences between rich and poor countries, between drug availability and production standards, between quickly diversifying treatment frameworks, or simply between the marketing prowess of certain doctors over others, made it so that human lifestyle, behavior, and ultimately, human bodies diverged ever more.

The Institute for Human Reference, whose name sounded more confident than it deserved, was founded in 2053 in a renovated strip mall in Florida. The mall had once housed a Chick-fil-A, a nail salon, and a store selling nothing but phone cases. Now it housed ethicists, statisticians, molecular biologists, policy analysts, and one historian hired to “provide context,” which is what someone does when you want them around for optics but don’t plan to listen to them.

The Institute’s mission statement was short and confident: Define the reference human.

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This used to be straightforward. In 2000, it meant “average diet, average activity, average lifestyle, no diagnosed disease, and no ongoing treatment.” In 2030, it meant “no experimental therapies, no known genetic edits.” By 2053, it meant nothing at all.

The trouble started with a spreadsheet, as it always does. A junior analyst noticed that participants labeled “unmodified” in a longitudinal cognition study were improving faster than those receiving standard nootropic protocols. Not significantly. Just enough to ruin everyone’s weekend.

The untreated cohort slept better, learned faster, and healed quicker than expected. They showed lower systemic inflammation, smoother glucose curves, and fewer regrettable opinions online. This was not because the treatments for the positive control group were harmful. It was because both groups were already heavily modified — often in aberrant, untracked ways. The “untreated” participants had benefited from cleaner air, adaptive lighting, fortified diets, stress-moderating urban design, and gut microbiomes optimized by public utilities providers for the bulk of their lives. Their baselines had been lifted, quietly and incrementally, without ever being logged as an intervention.

It was as if doing nothing had become a performance-enhancing drug.

The Institute convened a panel. The panel produced a report. And the report produced concern.

Without stable reference populations, public health science began to fray. Drug trials struggled to generalize. Public health guidelines contradicted one another. Regulators could no longer say whether an effect was meaningful or contextual. And insurance actuaries complained loudly.

The Institute tried to recruit even more unmodified humans. They advertised discreetly. “Participants sought,” the flyers said, “for a study on human baseline biology.” This attracted three types of people: conspiracy theorists, survivalists, and men who believed their masculinity was harmed by seat belts.

None qualified. Every candidate, regardless of how they avoided vaccines or grew their own food, was touched by various environmental enhancements. Their water was cleaner, the nutrients in their seeds higher, and even the air they breathed in the Institute’s lobby had been treated by germicidal ultraviolet light.

Meanwhile, the world continued improving.

Children learned faster, but differently. Each person became a unique bundle of interventions, exposures, and optimizations. Scientists began publishing papers with titles like Toward a Functional Approximation of the Human Baseline and Defining Control Conditions in a Post-Control World. These papers cited one another heavily and meant very little.

At last, someone proposed manufacturing a baseline.

Embryos. Archived ones. From before CRISPR, before microbiome engineering, before the Great Vitamin Correction of 2029. They would raise these children in sealed environments, feeding them pre-industrial diets. They would experience no screens, no optimization, and no enhancements except love, which everyone agreed did not count, despite all evidence to the contrary.

The ethicists balked.

The regulators bristled.

The grant proposal was rejected, revised, rejected again, and then leaked.

GOVERNMENT CONSIDERS PRODUCING “NATURAL” CHILDREN FOR SCIENCE.

Public reaction was swift and confused. Some people thought such children would be holy. Others thought they would be monsters or savages. Most people thought the whole thing sounded complicated. A Texas senator said the proposal sounded “expensive and European.”

The project was shuttered.

The Institute pivoted to simulation. If scientists couldn’t find a baseline, perhaps they could model one. Perhaps they could reconstruct the unmodified human the way paleontologists reconstructed dinosaurs through fragments and educated guesses. They rented an old data center and built several models of unmodified humans from historical data, archaeological inference, and optimistic assumptions.

The model was elegant and insightful, but also depressing.

The simulated baseline humans were less healthy, more impulsive, more violent, and much more likely to believe things that were untrue. When the model was presented at a conference, someone asked whether this meant enhancement was a superior way of life.

“No,” said a statistician, “it means our assumptions about our ancestors were flattering.”

The room laughed politely.

The Institute issued its final report in 2061. It stated, in careful language, that the concept of an unmodified, “natural” human baseline was no longer scientifically meaningful. Humanity had become irreducibly specific, fragmented into trajectories too individualized to be averaged without distortion. Science could no longer pretend that population means governed individual outcomes the way it did before. The failure was not ethical. It was statistical. Averages had stopped converging. The problem was no longer contamination of the baseline, but the disappearance of the conditions under which a baseline would be sufficiently explanatory.

It didn’t take long for biomedicine to adapt. N=1 studies proliferated. Rolling baselines replaced fixed controls. Therapies were evaluated against a person’s own prior state, not some mythical norm. Progress became local, contextual, and personal. The 2060s saw the proliferation of some seven billion digital baselines, with each person becoming the proud representative of their own unique reference group.

The Institute closed in 2062, its offices converted into a yoga studio. The historian wrote a book that sold poorly but aged well.

Science continued, because science always does. It simply changed its metrics.

People went on living longer, thinking more clearly, suffering less, and wondering why they felt so uneasy. Aging people reminisced about the people of yesterday. Ad agencies bemoaned the death of a world where they could appeal to a large target audience. Children asked their parents what humans used to be like.

“Worse,” the parents said.

“Why didn’t you change things quicker?” the children asked.

“Humanity was afraid of changes,” they said. “But we kept accepting small improvements and got better over time.”

The children — enhanced in a thousand invisible ways — went to sleep under lights tuned to their biology, breathing optimized air, dreaming dreams shaped by a history of improvements they did not choose.

They slept well. That, at least, could be measured.

Eliomer H. Kaas is an Eastern European social scientist with a taste for the simultaneously hopeful and depressing stories that make up the world of STEM in the 21st Century. Through his fiction, Elio tries to come to terms with the ever-changing nature of social advancement spurred by technology and the chaos caused by this advancement to human society, culture, and life.

Cite: Kaas E.H. “Baseline Drift.” Asimov Press (2026). DOI: 10.62211/29wj-12qw

The first question I ask my clients is, “How would you like to see the world?”

Some answers are charming; they want to see the world as an infant does, everything new and unspoiled by habit and familiarity. Some are more professionally minded and wish for magnification-enhanced, telescopic, UV, infrared, or radiation-attuned alterations that will help them with career tasks. Still, others are curious, wishing to see the world as an insect does, through thousands of hexagonal ommatidia. By far the most popular request is visual synesthesia. They want to “see the smell of cut grass” or “watch a symphony as they would a sunset.”

April’s request, however, did not make me smile. She wanted to see the dead.

“I-I’m sorry. I think that’s out of my area of expertise,” I stuttered, not unusual for me in my personal life but rare in my professional one.

“I heard you were the best,” she said.

I was surprised to feel influenced by the flattery. Just last year, I had built eyes for a team of commercial divers working on offshore wind farms in the Arctic. The implants filtered out the stirred-up silt by canceling the polarized backscatter patterns unique to turbidity and used a low-power LiDAR array to render pressure-wave distortions as faint light ripples so the divers could see the approach of water currents. I was good. Still, an old mentor in Lagos had been printing and sculpting retinal implants since before I’d started undergrad. She’d once wired a drone pilot’s prosthetics directly into night-vision satellite feeds, so I wasn’t the best.

“It doesn’t matter how good I am,” I said. “If Newton couldn’t crack the afterlife, neither can I.”

“I know,” she said matter-of-factly. “I don’t want to see the dead continuously, just sometimes.” She sat straight-spined and kept her eyes on mine.

“Haven’t you ever lost anyone?” she asked.

Step 01. Acknowledge the Limits of Natural Photoreception

Beneath the white glow of the lab lights, my gloves stuck to a polymer bench liner as I eased a latticed disk of organoid-grown photoreceptors from the incubator. It took hours of programming and careful printing to beat what nature equips us with naturally; eyes that can only detect a narrow 380-750 nanometer range of light. Trays of half-formed retinal cups floated in culture baths nearby.

Fifty years ago, the best visual-cortical direct-to-brain implants were only capable of translating a video feed into about two or three hundred phosphenes, those little squiggles of light that come when you rub your eyes. These days, I can grow gene-edited retinal cells and electrical-integrated optic nerves and implants that vastly exceed the capacity of the human eye.

Once I removed the disk from the incubator, I laid it onto the microelectrode array as thousands of graphene contacts tested for conductivity across the tissue. The traces came back clean, so I started injecting the first layer of Müller glia to knit the surface, then injected retinal ganglion precursors to carry the signals downstream. It’d take days for the cells to settle and form stable synaptic contacts, but once that happened, the construct would be ready for the neural lace web of electrodes that would translate between an encoder and my client’s optic nerve.

Spools of unspent neural lace waited in shallow dishes nearby, coiled like black spiderwebs. It was a bad habit, but so much of the build was muscle memory that my mind was usually a step or two ahead of whatever my hands were accomplishing.

This eye wasn’t for April but for a geologist who wanted to see magnetic field lines as shimmers. For April, constructing a functional, physical eye capable of integrating with other tech would only be the first step. If we managed that, then we’d need to tackle the addition of memories.

While I waited for the array to finish its sweep, I pondered the intricacies of April’s request.

The central mission behind almost every eye I’d ever built was to translate some sort of sensory data into visual brain-speak. Take the geologist’s eye: I had to build a bio-integratable magnetometer array that captured magnetic field data, program a small encoder that converted the compact fluxgate vector field into visual grammar, and then feed that through the eye. It wasn’t exactly “simple,” but it at least followed well-charted biophysics.

April’s implant involved many more unknowns. In the consultation, she had explained it like this: Grief only partially brought people back: “The fuzzy haze of remembrance,” she had called it. Some days, you can imagine a face as clearly as if it were right in front of you. On other days, it was a challenge to summon even the right eye color. She wanted this particular face to look real. She wanted to see her husband, Cline, doing simple things, like bringing trash outside in a downpour, beads of water clinging to his cheeks when he came back inside. She wanted me to translate her memories of him into visual information, integrate that with her real-time optical field, and project that seamlessly back to her brain. If I took it on, it would be the hardest technical challenge I’d ever faced.

A metallic scent returned me to the room, where the trash bin near me overflowed with pipette tips smeared with pink growth medium, crumpled gloves, and a discarded photoreceptor scaffold curled like an old contact lens. The bin smelled of ozone. I rose from my chair and tied the bag off, designs for April’s thanatopic eye swimming through my mind like ghosts of my own.

Step 02. Design the Scaffold

Although I’d spent my career perfecting the intricacies of neurological inputs, I was, conservatively, several miles out of my depth when it came to brain outputs. I called an old roommate, Aux, who worked in imagination tech, and was a little surprised at how eagerly he agreed to help. “Tia!” he exclaimed brightly as he answered. He explained that he’d designed a system that translated and recorded dreams, and had been crestfallen when a private contracting company had bought the patent to use as spyware. He’d quit soon after.

Together, we sketched out a basic 3-part modular integration plan. We were, essentially, building what amounted to a complicated projector system. The retinal implant would be the screen: a thin, curved layer of lab-grown photoreceptors fused with an electrode mesh that would sit at the back of her eye, bonded to the optic nerve and a cortical bridge upon which the generated images would combine with her visual field. The bridge was the projector. I’d sketched out its design before Aux came: a thin sheet of flexible polymer and grown tissue studded with microelectrode pins that would drape across her visual cortex. It would help combine and stabilize mental images with her visual field, so they’d map onto reality.

What I needed his help with was the playback device. He proposed a hippocampal recall interface, a bundle of neural leads we’d embed into her hippocampus that would record grief-evoked firing patterns into an AI decoder, which in turn would convert those into a visual blueprint the cortical bridge could position and the implant display.

Aux was excited about the project. He’d done his Master’s thesis on AI recreations of loved ones when that was just taking off. He was disappointed that the enthusiasm that followed their first-gen rollouts faded pretty quickly. His take was that one of the problems with their designs was that they’d actually been overfitted to physical reality, rather than people’s emotional one. “People don’t want to see their loved ones as they actually were; they want to see them as they prefer to remember them. There’s a surprisingly high delta between those two things,” he said.

The illusion that these digital ghosts were workable replacements for their loved ones quickly evaporated. By the early 2030s, they’d been demoted to another type of deepfake, rather than a compelling memorial.

While I worked on the retinal implant, Aux took point on the grief modeling. We wouldn’t be training the AI on shared “reality,” but on April’s subjectivity.

In order to program an encoder that could translate April’s grief and memories into visuals, we needed to get a strong grip on her visual memory. My lab wasn’t exactly set up for recording sessions, so we jerry-rigged a studio, wedging a chair beneath petri dishes of retinal sheets stacked in humidified drawers. Their translucent layers quivered faintly each time the air vents cycled.

Still, if we had been establishing this decades ago, we would have had to spend months recording fMRI scans, implanting everything, and only then been able to start fine-tuning the system and training the AI model, as we couldn’t access neuron-level activity non-invasively. Today, we have portable fMRI machines that we can run in the lab, and invasive no longer means gruesome or risky. Before her recording sessions, we injected her with a suspension of biodegradable neural motes — micron-scale, encapsulated sensors that settled along her brain’s vascular network and powered themselves through ultrasound backscatter. These let us make neuron-level recordings while we interviewed her.

I began by asking April who she’d like to remember, and when she felt their absences most acutely. I learned that her childhood best friend had been wearing red the last time she saw her and that her grandmother’s fingers always smelled faintly of the glue she used to make model trains. Aux noted that focusing on sensory details heightened both the breadth and depth of memory retrieval. The more I asked April to recall the way things smelled or felt, the richer the data that emerged.

Aux approved of her baseline. “Cogent, strong visual thinker — a regular apple-rotator,” were his exact words. After a couple of hours, the motes dissolved into harmless salts and amino acids, leaving only the recording data in Aux’s cloud as evidence that they’d ever been there.

Step 03. Stack the System

Aux and I then spent weeks interviewing April, having her look over the pictures and videos she’d shared of her lost loved ones and their favorite places, all while recording her brain activity. Aux was building both the encoder, which translated her memories into visuals, and the recall hook that would trigger during specific bouts of grief and remembrance.

We quickly realized how helpful it was to introduce physical artifacts to our sessions as well. April looked at pictures of her grandmother while eating some of her famous late-August apricot marmalade. Her grandmother’s handwriting had long since smudged off the jar, but April could still recall the way the bubbling pot would steam up her grandmother’s round glasses.

A few hours after she left, Aux tapped me on the shoulder as I was bent over the humming incubator. He beamed as he showed me a rendering of April’s grandmother, glasses fogged, on his monitor.

“This is completely translated from the output readings, ” he added.

I was also making progress.

Within a week, the final ex vivo model of the retinal implant passed all my low–fidelity visual testing, so we implanted it into April. It was as simple as cataract surgery and just as quick. A surgeon incised her sclera, and a micro-catheter slid the implant under the retina. I’d explained that without the bridge and recall system, the implant wasn’t yet going to be able to summon images of anything or anyone.

The cortical bridge was far more complicated than the retinal implant. To start, it had to handle a tremendous amount of visual data without misfiring. It lived for weeks in glass tanks and atop workbenches crowded with circuit boards, power supplies, and diagnostic monitors. I pulsed it in saline baths to mimic electrical firings and showed April how the graphene threads glittered faintly as we sent charge test patterns — gratings, flickers, edgemaps, and checkerboards, substitutes for the visual data that the recall interface would eventually be sending — across each channel, watching for voltage drift. When we pushed the full pattern set, this data combined to mirror the full amplitude of a real cortical load. I watched through my fingers and gritted my teeth as half the channels went dark, quietly suspecting we had fried the PEDOT coating.

I pulled myself out of the lab, squinting in the first sunlight I’d seen in 36 hours, and called another old friend. Lyre, a neural signal architect turned cortical cybernetics consultant (most likely in the “gray market” sense of the word), answered on the second ring. He was in the lab that same evening, takeout box in tow.

Looking at the bench and readings, he concluded that the previous night’s firmware update had introduced a timing mismatch. The wires hadn’t burnt out, but the clock that told them when to fire had been off by a microsecond, so the expected voltage response never lined up. He suspected half the channels had dropped out, even though the hardware itself wasn’t damaged. Fifteen minutes and a simple firmware rollback later, and everything worked perfectly.

Now, Lyre and I swapped the saline for neuron cultures to check if the wires could trigger and record real biological data. While we confirmed, Aux fine-tuned his AI encoder and processed April’s data.

We were finally ready to test the integrated system, without yet risking its insertion into April’s brain. We built something we only half jokingly called a “phantom cortex,” a benchtop stand-in: a synthetic cortical sheet of cultured neurons on a chip designed to act as April’s visual cortex. On one side, we put a lab-grown retinal implant that carried live sensory input. On the other, Aux’s playback device pushed reconstructed memories. The phantom cortex’s visual field was rendered on a lab monitor so that we could assess the pattern projections. The phantom cortex rig buzzing faintly in the background, gelled neuron sheets twitching under the microscope with each ripple of charge.

We started with simple patterns: they all came through smoothly, integrated seamlessly into the live visual field. Next, we tested April’s reconstructions. We called her into the lab for this, since she was the only person who could confirm if the renderings were accurate or not.

As we sat around the monitor, Aux sent memories through the phantom cortex. Excitement turned to nerves as April judged our work.

April inhaled sharply as Cline’s face appeared on the monitor screen. He smiled through a rain-smeared window in front of the lab desk.

The image only lasted a second. Aux cursed as the memory degraded and glitched in real time before our eyes. Her husband’s face smeared into a Dali, suddenly donning her grandmother’s glasses. Dozens of screaming mouths popped into view, then out again. I scrambled for the abort key.

“That screaming mouth was my dad’s,” she said, hiding her face. “ He’s someone I don’t miss.”

“This is an issue with the affective tagging,” Aux said grimly. “The system grabbed any memory tied to a spike of loss — even those tangled up with fear or anger.”

We hadn’t built a robust enough model of grief. The models couldn’t distinguish between the suite of neurochemical signals and pathways that light up when grief occurs and other related but distinct emotions like fear, longing, and even resentment. Grief was more global than we thought.

April left without saying goodbye. She messaged us the next day saying she was sorry. I reassured her we were problem-solving, and the next time she heard from us, we’d have it fixed. She didn’t respond. This, I thought, was the problem with trying to bring back the dead. April wanted to feel her loved ones still with her, but nothing comes free, and constant reminders of loss were the price.

That night, as I drifted off, I also fretted that what we were building was actually just a perseveration machine, something that would help people fixate, rather than heal.

Step 04. Define the Problem Space as Grief

The hardware was working, but we needed better software to select and stabilize reconstructed memories.

It wasn’t enough to simply gate memories and focus on spikes in certain brain activity. Grief is a process, not a single event. Rather than a digital switch, it was more like a current running through multiple circuits at once, constantly shifting in strength and direction. We were obviously dealing with a level of complexity we hadn’t modeled for. Lyre told us we’d be smart to think of grief as a type of learning. We fixate on the people we’ve lost to teach ourselves how to live without them. By remembering their absence, we update our mental model of our world to be more congruent with their absence. This means that grief closely mirrors other learning states, such as disgust, trauma, and fear, that help us do the same thing. What this meant in practice was performing a suite of negative space runs with April.

We started simple, with pictures of strangers and familiar but unrelated sights like video feeds of grocery stores and sidewalks. When we felt like the system had a good handle on non-emotive responses, we moved into shakier territory.

We needed April back in the lab. This time, though, we weren’t going to be tackling the people and places she wanted to remember. We’d be focusing on creating “negative spaces” in the data: all that she didn’t want popping up in her sadness so that the AI could learn the difference between grief and the other affective states that closely approximated it. She agreed to come in later that week.

April sat, the microsensors buzzing through her skull while Lyre triggered a series of increasingly emotional cues. She gave us a series of notes a bullying classmate had left in her high school locker, calling her all sorts of horrible names. Then pictures of her father and mother. The hippocampal leads lit with recall, the amygdala spiked, and the bridge dutifully sent its output into the phantom cortex’s renderer.

Her father appeared on the screen. The system didn’t know the difference: to the limbic circuits, loss is loss, whether you wanted to remember or not.

“That’s not who I asked for,” April said flatly, looking at the faint projection hanging over the test bench.

“We know,” Aux said.

We added a valence-gating layer. After each recall run, April tagged what she saw on a tablet: grief, fear, do not summon. Every memory signature — the spiking amygdala channels, the hippocampal pattern maps, the cortical bridge’s intermediate codes — carried her labels forward into the training set. Discarded electrode hoods hung from hooks on the wall, their interior pads oozing the faintly metallic odor of conductive gel.

Lyre ran the retraining loop live, updating the bridge’s decoder weights so the renderer learned to separate emotional intensity from summon-worthy grief.

By the third run, a beach in Greece where she’d honeymooned came through when she wanted it, and her father didn’t. Fear-heavy childhood scenes disappeared entirely from the output buffer.

Aux replayed the final pass into the phantom cortex — hippocampal recalls feeding the bridge, bridge feeding the synthetic V1 sheets — and for the first time that day, only the memories April actually wanted bloomed into view.

She looked at the empty air above the bench, where only her husband appeared, in focus and silent.

“Better,” she said.

Step 05. Cross-Train with Auxiliary Memories

The valence gating held through three separate phantom cortex runs: no father, no high school friend breakups, no clutter of unwanted memories. But there was still a problem with the projections. When the hippocampal traces pushed through the bridge and into the synthetic cortex, April’s husband would flicker after just a few seconds. Her grandmother, likewise, disappeared into a foggy haze, like the steam atop her glasses. Her memories just weren’t stable enough.

We needed additional input from other people who remembered her lost loved ones. A single person’s memories, it seemed, weren’t enough to generate consistent projections. Memory conformity seemed crucial for stabilizing the system. Not only would they help us strengthen the projections, but the effects of collaborative recall would augment April’s own future outputs as well.

Aux designed the auxiliary session room like a film studio. Gathering the memories, we were in his imaginative wheelhouse. April sat behind a partition while the friends, exes, and even a few family members she’d managed to convince to participate sat in a circle around a microphone and eye trackers, with lightweight EEG strips affixed to each of them.

We asked them all kinds of questions, both to supplement the AI system and to boost April’s own recall:

Describe your first memory of Cline.

What’s a boring, but extremely routine memory you have of April’s grandmother?

How did Cline talk at work versus at family dinners?

The harder parts, for April at least, were when we asked them to correct the memories April, herself, had given us. Their reunion beneath the cherry blossoms in spring hadn’t been after his stint working in Europe, like she thought, but after a huge fight the two of them had had. She’d kicked him out of the apartment, and he’d been staying with his best friend for a week. There had been no hugs, no long embrace, but a careful, painful conversation about what they both wanted and whether they still fit in each other’s lives.

After we’d finished interviewing everyone, we were shocked to find that more than half of them had stuck around, mostly the ones who knew her husband. We drank wine out of paper cups and sat on the slightly sticky floor of the lab. The stories continued even without recordings. We dimmed the fluorescent lights, and the perfusion chambers, vials, and incubators glowed like lava lamps. Aux showed off some new im-tech overlays, and we drifted through one of his rendered deep-sea dreams.

While Lyre and Aux worked on the data, I kept the organic stuff alive. The cortical sheets and hippocampal organoids sat in sealed perfusion chambers, their scaffolds constantly fed with oxygenated media so they wouldn’t necrose before implantation. Every few hours, I pulsed the graphene lace with low-amplitude stimulation patterns to keep the synapses from going quiet, watched calcium transients bloom under the scope like tiny green storms, then swapped out the old medium before lactate levels climbed high enough to kill any of them.

By this point, April’s retinal implant had fully healed. For most projects, I spent almost all the time on the eye itself, but I hadn’t thought about it in weeks since implantation. April agreed that she often forgot it was there.

The bridge tissue had to be ready for a human body, not a bioreactor, and that meant no ischemic edges, no scar-prone glial blooms, no dead zones in the middle of the bridge when we finally stitched April to her memories. It was repetitive work, but it kept me from catastrophizing.

Soon, the bridge wasn’t just surviving — it was performing. Aux piped April’s recall traces into the phantom cortex, and April’s loved ones appeared in crisp, frame-by-frame fidelity. No drift. No flicker. No spillover. Lyre stress-tested the decoder with noise injections like cross-talk overlays, where he blended two memorial recall streams, one of her husband and one of her grandmother, and the bridge disentangled them seamlessly on the visual layer. We’d have been satisfied with 90 seconds of stable projection, but we were getting up to three or four minutes consistently.

Somehow, everything worked.

Step 06. Assess Implant Success Via Patient Ground Truthing

April arrived at her implantation surgery, accompanied by more than half the people we’d used for the auxiliary memories. The retinal implant had been a simple procedure. This one was far more intense.

An anesthesiologist put her under, and the team stabilized her skull in a frame. They then drilled three tiny openings into her head, each just wide enough for the thread-thin surgical arms to slide through. On the monitor, the cortical bridge looked like a thin mesh being gently pressed against the surface of her visual cortex, settling over it like a static-clung sheet. A second monitor tracked the hippocampal leads as they snaked deeper, flowing faintly as they followed pre-mapped paths into her memory centers. After two hours, the implants sat exactly where they were needed.

The implants were cushioned in a hydrogel scaffold that reduced swelling and coaxed new vessels to knit quickly around the electrodes. After only 48 hours, doctors cleared her to leave.

Just days later, we brought April in for an initial round of post-operative testing. We had to do a bit of fine-tuning to the model now that it was directly integrated with her brain. Looking at her, you would never know she could see the dead: All the implants were internal. We took her around the city, visiting all the locations, dense with memories and grief, that she’d shared with us during the build. We walked down the rotten pier where her husband had dived in after a teenager who had fallen into the ocean. She told us she saw him perfectly, dripping wet and smiling. Remembering, she said, was tiring, but she told us she was happier for it.

From a lab perspective, this was a headline result: Our system worked. Auxiliary-memory training closed the sparsity gap; valence gating reduced false positives by 98 percent; hippocampal recall signals held steady across cortical frames for over three minutes before their natural decay.

The psychological effects were less clear-cut. When April failed to show up for her two-week check-in, we went to check in on her. We found her in her apartment, surrounded by dirty plates, coffee cups, and desiccated flowers. At first, it seemed like all my worst fears of this implant had come true. She hadn’t taken the trash out since she’d left the hospital, and she was in the same rumpled, sour-smelling clothing she’d been wearing when we saw her last. She told us she’d spent the last two weeks with her ghosts, watching her husband pile trashbags on his shoulder and walk them out to the corner, and her grandmother bent over the kitchen table, pondering its grain. She’d done almost nothing other than live amongst these visions.

Aux, Lyre, and I spent nearly an hour helping her clean. Spurred by the humiliation of being seen like this, some dire warnings, and my direct threat that we’d disable the implant if her self-care didn’t improve, April swore she wouldn’t miss another appointment. She also shared the name of a therapist she’d recently contacted, and agreed to ask her Aunt to stay with her for a little while. We left, and I spent the next month buzzing with guilt that I’d ruined someone’s life.

By her six-month post-op check-in, however, April appeared to be thriving. In her neural patterns, grief acted not like an emotion flickering in and out, but like a steady recalibration — almost an error term adjusting her brain’s predictive model of a world missing someone. The deep longing and sadness remained, but she was learning to fight back against the pull of nostalgia. She was also going out for drinks with friends, working on a startup with one of her husband’s old roommates, and had taken up Tai Chi. She self-reported that she was seeing her deceased loved ones less these days, though it was still nice to call on them when she felt their absence.

April had proven strong enough to use her new eyes as a tool to help her move on, but the memory of that first home visit hung heavy over me. I didn’t think everyone would be able to pull themselves through such exquisitely rendered grief like she had. I feared most would become stuck in an emotional traceback error, reaching again and again for those who no longer existed, rather than using the implant to map out a new path through life.

These thoughts haunted me back in the lab. Under the faint smell of ethanol, I lifted a vial of opsin-gene-edited photoreceptors, their suspension glowing violet under the culture light, and seeded them onto a retinal scaffold. The incubator door hissed as I slid the tray inside.

Despite the technical thrill, the success of the implants, and April’s gratitude, I don’t think I’ll make another eye like hers. It’s too psychologically risky.

Inside the incubator, the earliest parts of a new eye were growing. This one was a pro-bono project for the state’s Department of Children and Families. The eye would overlay subtle thermal and blood-flow cues that signaled stress, so social workers could spot when a client was physically distressed, even if they were masking it. I hadn’t abandoned the idea of visualizing emotional states, but I didn’t want to spend my life helping people picture what was no longer there. The world we were in already offered more than we could possibly fathom.

Spencer Nitkey is a writer, researcher, and educator living in Philadelphia with his wife and a dog named after Jean Baudrillard. His fiction can be found in venues such as Apex Magazine, Asimov Press, Diabolical Plots, Lightspeed Magazine, Protocolized, and many others. You can find more about him and read more of his work on his website, spencernitkey.com.

We are grateful to Benyamin Abramovich Krasa for reading a draft of this essay and providing feedback on its descriptions of neurotech. Header image made by Ella Watkins-Dulaney.

Cite: Nitkey, S. “How to See the Dead.” Asimov Press (2025). DOI: https://doi.org/10.62211/92ws-21nb

The header image for this article is available as a sticker on our website. Visit shop.asimov.com to order.

The inheritors of such a worldview, the artist-researchers of The Tissue Culture and Art Project began exploring the artistic applications of tissue engineering in 1996. They saw their mission as “a pioneering collaboration that explores how tissue engineering can be used as a medium for artistic expression.” By 2003, collaborators had cultivated frog meat grown from cells, preceding the scientific and market interests in cultured meats. A decade passed before, in 2013, scientist Mark Post would famously create and consume a burger grown from stem cells.

Art and technology often intersect in this way: a brush, a chisel, a 3D printer, DNA encoded with the image of a Germanic Rune, a biocompatible implant that induced the growth of an ear on a forearm, a bioprinter, CRISPR editing that induces pigmentation in bacteria. 

No other artistic field showcases these intersections more vibrantly and impactfully than bioart, where cutting-edge biological tools are turned towards the aesthetic, the cultural, and even the numinous.

And no artist better embodied this field than Maxwell Ardeen. In 2060, Ardeen was diagnosed with an undisclosed terminal illness. The threat of death, which he described as Damoclesean, produced the most intense, prolific, and celebrated era of this late artist's career.

Transdifferentiation, 2061

Ardeen’s first exhibit post-diagnosis was widely recognized as the beginning of his “terminal era.” In it, a small embryonic clone of the artist was genetically spliced with genes from the turritopsis dohrnii jellyfish using CRISPR-Cas9 technology. This “immortal” jellyfish engages in a phenomenon known as reverse development. Upon encountering environmental and cellular stressors that would normally result in death, starvation, or senescence, it can revert to its pre-polyp cyst stage, growing into a genetically identical adult once more.

Ardeen implanted his embryonic clone into an artificial womb, magnified, and displayed on a 7’ x 7’ square LED television. The edges of the womb were lined with needles that penetrated the embryo once it reached the equivalent of 8 weeks of gestation, 1 week before achieving its fetal stage. These pinpricks triggered a cascade of molecular signals, causing it to revert to its germinal stage and begin the process of development once again.

Audiences received Transdifferentiation with both rapturous praise and raucous protest. Concerned members of the public argued that the exhibit amounted to little more than the torture of a sentient or pre-sentient being. Yet, art critics called the piece “epochal,”1 with one extolling its “exploration of a pre-life groping toward eternity,” lauding it as “nothing less than the single greatest work of the 21st century.”2

Chimera, 2063

This piece first appeared in billionaire Bri Asher’s Art for the Transhumanist Future exhibit. Ardeen presented it alongside several “upcycled” former projects, one a series of decomposing animal carcasses infected with zoonotic bacteria engineered to make colorful pigments while consuming dead or dying materials.

In Chimera, the artist used a 3D extrusion bioprinter and an electrospinning technique he developed in graduate school to produce a set of various animal organs, which he then placed in chambers. Labelled with both their organ type and species, they lined the perimeter of a room roughly 15 feet in diameter and connected via a tangled and complex panoply of machines, tubes, and wires, approximating an intoxicated spider’s web. 

Synthetic blood, made from cultured red blood cells, was pumped through and between the organs by means of normothermic machine perfusion. The lungs of a black bear expanded and contracted, aiding in the oxidation of the fluid, which circulated throughout the rest of the exhibit, aided by both the artist’s machines and an elephant’s heart. The fluid filtered through human kidneys and a urine replacement system designed by the artist. 

The complex array of temperature controls, oxygenation chambers, pressure nozzles, and drainage systems cohered in aggressive harmony, all leading to the center of the room where a mass of boneless flesh sat: an ever-growing cancerous tumor. Having created a crude but potentially life-sustaining organ system, Ardeen chose to devote it to the expansion of an entropic, life-abating mass. To this day, the source of the tumor has never been confirmed, though many believe it to have been excised from the artist’s own cancer.

And again, and again, and again, 2064

One year after Chimera, Ardeen produced this kinesthetic sculpture, read at the time as a study on brain-robot-interfaces and the futility of mechanical replications of life. More robotics-focused than his previous works, in this piece, two busts opposed one another: one featureless; the other an immaculate reconstruction of Ardeen himself, composed of a sculptural material the artist grew from his own cells. A large, fully articulated robotic arm extended from the base of each head. Several medical tools and devices occupied the space between the busts. 

When the exhibit was first unveiled, the arm connected to Ardeen’s bust moved first. Grabbing a scalpel, it plunged into its face, slicing and slowly pulling the reconstruction apart, leaving the skin in a pile beneath the bust. The robotic arm then removed the top of its faceless metallic head, revealing a bioreactor with a pea-sized tangle of neurons swimming inside. This bioreactor was lifted out of its skull and placed inside the bust opposite. After a long pause in which neither machine moved, the second robotic arm, connected to the now-filled bust, delicately took the “skin” from the opposite bust’s base and sculpted Ardeen’s features on itself. Once finished, the process began again. 

While original viewers could only speculate as to the contents of the small cluster of neurons that passed from bust to bust, with the benefit of Ardeen’s extensive notes, we now can verify their origin. 

Ardeen spent a year mapping his neural activity using a MICrONS neural-scape helmet, adapted from its 2025 mouse project, and custom viral transneuronal tracers encoded to mark the motor cortex pathways of his arms, hands, and fingers. Using this helmet, he recorded himself for over 3,000 hours constructing and deconstructing his face. In this way, he assembled enough data to print a small “brain” containing the required neuronal pathways and signal patterns.

These neurons, housed in the bioreactor, connected to the robotic arms via a parallel brain-machine interface. The interface provided haptic procedural feedback (i.e., when to start and stop a task) to the neurons based on haptic sensations from the robotic limbs.

The sculpture was meant to be perpetual. Some now see this as Ardeen's first, earnest attempt to create an eternal life for himself, even though, over the years since its initial exhibition, substantial data loss has appeared in the facial reconstruction stages, deforming the once-accurate simulacra of his visage. Given the direction of Ardeen's later work, some art critics speculate that he anticipated such deformation and that it was intentional.

Entombed, Transformed, Revived, 2065

Sometime around 2064, Ardeen began engaging more regularly with the public, attending seminars, speaking at universities, and even bringing his bioart into virtual-reality daily-show environments. This dialogue, particularly questions from his audience about the value of extending human life, motivated this sculptural installation.

Roughly the size of Ardeen and resembling a human body, it comprised six distinct chambers. The head, the torso, and each arm and leg communicated as did the organs in Chimera. Each was given its own perfusion-pump system to create something evocative of a magician's trick: a single body dismembered into six boxes.

Each applied a different life-extension technique. The body’s natural aging process was then induced and intensified a thousandfold.

The right arm received a next-generation improvement of a drug which combined metformin with rapamycin, medications thought to extend, though not infinitely, the life and health of its recipients. While the right arm aged more slowly than expected, it still showed noticeable decline, such as a loss of muscle mass and osteoporosis, which conformed with TAME3 findings released earlier that decade. 

The left arm received a gene-therapy treatment, originally designed to treat muscular degenerative diseases, introducing exogenous follistatin, a protein that inhibited myostatin and encourages muscle growth. While this arm also initially showed slowed signs of aging, it succumbed around month three of the installation, with liver spots, sagging skin, and contortion of the arm.

The right leg was treated with a suite of telomerase activator drugs, enzymes that slowed down and, in some cases, even reversed telomere shortening. Unregulated telomerase activators induced rapid, unregulated cell growth, and tumors soon overcame the limb. 

The left leg was cryogenically frozen, using early 21st-century techniques as a nod to former and failed attempts at eternal life. The effects were unimpressive, as while the appearance of the limb remained mostly unchanged, a small screen on the chamber displayed the microscopic destruction taking place inside: cell-rupture, osmotic stress, and cryoprotectant toxicity syndrome left a field of dead cells beneath its seemingly placid surface. 

The torso was treated with a partial programming gene therapy. This therapy delivered several Yamanaka factors, which restored parts of the torso’s epigenome and cellular function degraded by age. The use of this therapy, at this point only authorized in the United States for compassionate use trials for specific, degenerative diseases, was legally allowed in this sculpture due to its “nonliving nature.” While results from several longitudinal studies showed great promise for use in the general population, ethical concerns focused on equitable distribution of anti-aging technology and laws, such as The Life for All or None Act of 2045, kept it off the market.

The head was left to age without intervention. 

The result, taken collectively, was striking. At its debut, the body was highly congruent, each part approximately as old as every other. However, shortly after its unveiling, it grew more collage-like, with an ancient, soon-to-be rotting face, one limb a little younger and the other much younger still, one leg a twisted suggestion of a limb and the other freezer-burnt, and at the center of it all a perfect, unaged torso.

Future recreations of this work, once the original materials were sufficiently aged and desiccated, took a simpler approach. A body was left to age, while a small surgical bot performed routine aesthetic de-aging procedures on the face. 

If one were to look at these results next to one another, Ardeen suggested, our need to stop aging and cure death would be self-evident. Would you tell an aging face it should content itself with its lot? Or tell a body it should age and wither while its face remained youthful? This artwork screamed no.

Eichenbaum’s Fever Dream, 2066

A departure from the purely physical nature of his early work, Eichenbaum’s Fever Dream marked Ardeen's first earnest entry into the world of mixed virtual-biological experiences. It was named after the scientist who first coined and widely popularized the concept of “time cells” in the hippocampus.

Viewers were asked to sit and place a large, helmet-like device over their heads. Electromagnetic signals then passed through their brain, targeting the lateral entorhinal cortex, the primary area of the hippocampus responsible for encoding our sequential experiences of time. Through a complex series of trial and error, and in collaboration with engineers at Opta, the technology company founded by Stanford dropout Everett Rousseaux, the helmet effectively dilated the viewer’s experience of time.

Clad in the helmet, awestruck viewers spent 18 hours watching a simulated evolution, projected at 1,000 times “normal” speed. Deep on the seafloor, hydrothermal vents spat plumes of superheated water into the surrounding ocean. Extremophilic archaea fed on the hydrogen expelled from the vent, converting it into energy and methane. Programmed algorithms stimulate the jump from archaea to eukaryotes. And as programmed environmental stressors and material availability shifted, viewers watched the species increase their reproductive capacity during times of hydrothermal abundance, eventually coalescing multicellular organisms, and then into a thriving colony of tubular worms waving from the “smokestack” sediment chimneys that had formed above the vents.

Finally, as the piece drew to a close, a tectonic shift in the plates collapsed the vents. The plumes disappeared, and the worms withered until nothing remained but a cold, dark seabed. Visitors removed their helmets to find that less than a single minute had passed in the gallery. Many reported weeping as they mourned an entire species laid waste by an accident of geology.

Though in the decades since this work’s release, many schools, work environments, and research employ dilated virtual environments, preliminary research has suggested that the technology may cause early-onset neurological aging. This has led global health organizations to recommend limiting the amount of dilated time consumed annually, and now Eichenbaum’s Fever Dream requires extensive waivers to view.

Eichenbaum’s Fever Dream marked an artistic evolution for Ardeen, a journey inward, into the intricacies of time rather than raw attacks at the process of aging itself. Who cares if the material body degrades in a hundred years if one can access epochal time?

The End. The Beginning. The End Again. 2070

Ardeen’s final piece was a literal black box, four feet wide, two feet deep, and seven feet tall. Unveiled at the New York Museum of Bioart, it is now housed in a warehouse owned by Ardeen’s trust, with instructions that it be placed on the museum floor again in 2170. A curtain was dropped from the box in the center of the gallery floor, and Ardeen, emaciated and, by all accounts, dying, slowly walked from the audience to the box.

With a hiss, a small door opened, and Ardeen stepped inside. Before anyone could move, the door slid shut. Projected onto the outside of the black box, a timer began the countdown from 3,156,000,000 seconds. In 100 years, Ardeen’s final creation will open. Who, or what, will be inside remains a mystery. Many believe he has merely created an elaborate tomb. Those closest to him, however, believe that we will find something very different, though what it is remains debated and unknowable, for now.

Cite: Nitkey S. “The Eternal Life and Art of Maxwell Ardeen.” Asimov Press (2025). https://doi.org/10.62211/28he-13uu

Animated header by Ella Watkins-Dulaney.

Lives had been easier to frame when they’d been time-bound. A man at 60 is hardly the same man at 153, let alone 154, with how quickly things were changing. Happily, at least for Jamie, there were still the unfortunate saps who gave up the ghost to sheer accident or, lacking technological enthusiasm or a spirit of curiosity, elected to bow out early — or had never opted in to begin with.

Deaths were rare enough that when one was announced, those still employed in the Obits clamored, begged, and connived their way into a piece of the action. But after an ugly incident involving a bribe, a bottle of cognac, and the burning of an office chair, the holdouts had taken to drawing straws.

As luck would have it, Jamie drew the next reported incident. He didn’t know when exactly it would take place because chance, rather than age, had come to herald death’s arrival. No longer could one preempt those venerated elders whose lives had been haunted by finitude as now they sustained themselves through a regimen of drugs, diets, and therapies: products of the decades and countless billions poured into longevity medicine.

The call came a little after noon, just after Jamie had returned from the short walk down to his favorite cafe for his third cup of coffee that morning. The cafe line crept along at a glacial pace because their barista was one of the older task models — attractive, but inordinately slow — steaming milk with the rigidity of someone who detests babies but has found themselves holding a squalling one. There was an awkwardness about her that Jamie liked, a charm in the mechanical smile and uncanny valley of her handwriting on the cup he now tossed aside as he made his way out of the building to the scene.

Hurrying across the park, he could see the uprooted tree lying horizontally across the path before he saw the police tape. It had been a windy few days, and the weather-weakened roots had not been up to the task. Even with the body removed, the site was surrounded by onlookers, keen to see the aftermath of the lethal accident. A police officer stood consoling a blubbering woman, who described a powerful snap, a shriek, and a boot sailing into the freshly manured lawn. The boot still hung from her hand.

The manner of death had always been popular with Obit readers, who craved details, reveling in the drama of sheer randomness — the rotten branch, the freak lightning storm, the accidental tumble out a window. They now had the means to live hundreds of lives, but the unpredictability of their environments still posed a threat. And as safe as cities had come to be, any nature not battened down, any architectural feature not secured, still presented inhabitants with the only opportunity to join their ancestors the old way.

So while Jamie noted the details of the park — the marigolds in bloom and the predominance of Goldendoodles — he also wrote how the mangled body was extricated with only one shoe, hardly recognizable save for its lace.

Back in the office, Jamie began digging up all he could find on the dead woman. The shoelace belonged to Elena Kramer, whose public record placed her at somewhere between 84 and 168 depending on which program she had been operating on at the time of the accident. She had grown up in Jersey and was the oldest of her family to opt in, so that by the time she did so, she was already physiologically elderly. Perhaps this had contributed to her fatally slow egress from underneath the tree. While Jamie learned she had been a high-school music teacher before opting in, he could not find much in her profile to suggest a deep love of music. Instead, it seemed that she had spent her time ice skating. The terabytes he combed through showed years of exuberant gliding across wintered-over ponds and glacial streams until she could land moves befitting a 20th-century Olympian. Videos of Elena danced across his headset like beads of rain.

Jamie hadn’t realized how long he had been lost in the visualizations until his colleague Nils, a thickly accented Swede with an unruly mop of mouse-brown hair, knocked on his door.

“Dinner?” Nils asked.

It was customary that when one of the group received a call, they would celebrate the following Friday over oysters and ales down at Sydney’s. They liked Sydney’s because it was quiet. Even though most of their cohort had opted in, Jamie included, they felt a kind of sentimentality for those people and places that felt untouched by such choices. There were no screens at Sydney’s, only finger-softened menus, a rococo bar groaning under its fleur-de-lys, and a grandfather clock in the foyer, stuck forever at 7:13.

Jamie, suddenly aware of his hunger, stood up from his desk and stretched.

“Dinner,” he concurred.

“Have you put any words down today?” asked Nils, as they descended the wide stairway to the lobby.

“Just an opening line: ‘Elena Kramer, an ice-skater who once glided across piano keys with similar grace, died on Oct. 17th, 2076, when a tree brought her to her final rest in St. Catherine’s Park.’”

“To the point. Was she skilled?”

“Impressively human,” Jamie replied.

Jamie had seen better skating, of course, for everything in the sim was done at a superlative level. But it was rare to find such a human touch — little imperfections, such as turning the beginnings of a fall into a shotgun spin, were details that sims still fumbled or omitted. These signatures of a previous, unaugmented life were generally only achieved by those who had opted in late.

The October air smelled like wet newspaper and baking bread, and the cold scraped the men’s faces as they walked. Sydney’s was down a narrow street, where the colors of various neon signs clashed over the rain-slicked sidewalks. Sometimes Sydney’s daughter worked as a server, but she wasn’t there today. That suited Jamie just fine. Her glassy dark eyes and pouting mouth unnerved him and made him feel awkward. By all rights, he should be able to relate to her just fine. He had opted in, after all, and was able to access any and all manner of social decorum instantaneously. But the sim still struggled when dealing with some of the unaugmented — those with enigmatic expressions, the very old, and those with darker skin.

Jamie had another theory, too — that the sim was thrown off by looks of sheer contempt; perhaps the faces on which it was trained had been too adoring, too expectant, and too willing to be understood by the technology capturing their biometrics. Some of the training subjects may have been nervous of course, but none that truly loathed the program would have elected to help enable it. The sim lacked some crucial data underscored in Sydney’s daughter’s flashing eyes and disapproving smirk.

“Will it be the usual for you boys, then?” asked Sydney, advancing on the table smelling of fish and cigar smoke. “Or,” he asked, “are you too full from imagining the thought of dry-aged branzino and oysters? Maybe you already drank up in your head.”

Sydney, the proprietor and head chef, had not opted in and was aging about as well as a formerly heavy smoker and robust eater could. His brassiness and occasional outright hostility towards those who had chosen otherwise kept many prospective patrons at bay. He also knew perfectly well it didn’t work like that; they had to take in calories just as he did.

“The usual’s fine, Sydney,” they chorused.

Jamie felt the juice from the lemon he had been squeezing sting a cut he didn’t know he had as he listened to Nils and Amir gripe about the Obits. Amir, a few glasses of brandy in, was deep into his usual spiel.

“I’ve half a mind to kill ‘em myself. ‘Cept I know they’re likely decent folks, just like us in this room. Only you walk past ‘em, and they look right through you. ‘Course, we do it to them, too, when we’re running our programs. Only it’s so damn unsociable, really. I’ve half a mind to grab the nodes right from their heads. Maybe chuck ‘em into the harbor. Or better yet — chuck them and their nodes into the damn harbor. That way we might actually have something to write. Well, that doesn’t even matter, really, cause I’ll be quitting soon. Too much damn sentimentality in this job. And we’re running out of octogenarians and the accident-prone, anyway. Soon there won’t be people to be sentimental about.”

Jamie, Nils, and Harry moved their faces sympathetically, but they all knew it would be good if Amir quit. He was right. Obits had become unbearably slow. Jobs in and around the sim were inevitable. Why avoid them? And why focus on the very fact they had all shaken off with the arrival of the program? Jamie had been scared of death his whole life, and now he was obsessed with it — like a hypochondriac intent on watching his own surgery.

Nils, who had been fiddling with something on his watch, raised his head and slammed down his fist. The force sent the oyster shells rattling.

“Maybe we should be convincing people to opt-out,” he said.

The other men also shared this thought. But it somehow seemed a worse possibility than nudging the few remaining holdouts into the path of an oncoming train. Who would want to relinquish infinity? But they all knew that some people certainly did: people for whom any career in the world, any language, any woman, could not slake their boredom or frustration; those who felt like cheats or defectors; those who suspected their decision to opt-in had been an affront to God. Indeed, skeptics offered a long list of reasons. Reasons the men at the table had all written about before.

The answer didn’t come from them, however, but from Sydney. The barkeep had been listening from a high-backed chair at the counter, where he was busy peeling garlic, but now he materialized before them, dishrag over his shoulder.

“Good idea,” he boomed. “Too much fantasy is a drug. And a society on drugs is no society at all — only a simulacrum. Living one life slowly is good. You absorb it that way. Why anybody would want more is beyond me. Most people can’t even understand the life they’ve got. You get folks to opt out, I’d give 'em free drinks and hang a picture of you over my bar.”

As Sydney made his way back toward the kitchen, Jamie glanced at the spot he’d indicated. His eyes came to rest on the clock and, for the first time, he found himself wondering what had happened at 7:13 the day it stopped.

The building where Jamie lived ballooned into the skyline, unapologetically obstructing a large electrified billboard for various luxury baths. These baths hardly needed advertising. Almost everyone Jamie knew had a membership to such “safe zones,” where they could swaddle themselves from any unpredictability left in the outside world. There, in an atmosphere resembling a sanatorium crossed with an incubator, those who opted in could select from a carefully curated list of spa treatments, gene therapies, and other routine physical maintenance. The bath experience was so nice, in fact, that some people ran them in their sims even while there in a kind of infinite regress.

Jamie preferred the humbler comfort of his own apartment building.

Constructed in 2054, it had been a senior living facility boasting both high-quality care and kosher meals. Well past its glory days, its original residents had either died or opted in, leaving for the comforts of the more modern constructions uptown. Their rooms had been replaced by boutiques, kino machines, produce stalls, and cafes run by an unlikely slice of society — those who had never opted in and the working rich — as well as an assortment of robotic models, both general and task-specific.

Only two rooms were still occupied: Jamie’s and another belonging to Ms. Fitz.

Jamie had moved in because he’d been attracted to the building’s eccentricity, the reasonable rent, and the proximity to a few remaining centenarians whose passing might mean work (they had). Ms. Fitz, by contrast, had inherited the place from her mother. Now, in the very room where she had passed, Ms. Fitz kept a menagerie of canaries and a blind cat named Mavis. It had taken Jamie a while to figure out that Ms. Fitz had opted in because, outside her apartment, she never wore the nodes, headsets, or other devices favored by those in the sim. When he finally asked her, she let out a musical laugh and said, “When I am out, I like to occupy the world, such as it is.”

Though their rooms were on different floors, they made a point of seeing each other regularly. They caught up over coffee, as, having lived in Ankara for decades with her first husband, Ms. Fitz brewed it in the traditional Turkish style and served it in dented brass cups that Jamie loved. He would drink down to the grinds while listening to her tell stories about the physical places she had traveled. And while Jamie was skeptical that they could be all that different, Ms. Fitz was adamant.

“I have been back to Ankara in the sim, and it looks exactly as I remember,” she said. “The Mahaleb cherry trees outside my old house, the cracking paint on my neighbor’s shed … But it does not smell the same. They haven’t captured the quality of the air. Nor the right feeling of the sun on the skin. It’s close. But somehow, Turkey itself is lost.”

“What do you think the sim is good for then, if not modeling worlds?” Jamie asked.

“But it is skilled at modeling worlds,” Ms. Fitz replied. “Only, endogenous ones. We get to know ourselves better. We can iterate faster and introspect more deeply, and there’s far less to miss. It’s as if we can experiment with being multiple people.”

Jamie mulled this over as he rolled his coffee grounds around the bottom of his cup, where they formed a little crater. He wondered if he ever experimented with being multiple people. Perhaps the best example of this was the day he opted in. He remembered the first couple weeks of headaches as his brain assimilated to its quicker processing speed. He remembered the gluttony of those hours (days?) spent on the couch sampling different personalities and simulating far-flung cities.

He could never get used to the queasiness, though, that came with ceaseless exploration, preferring one identity at a time. However, even so, one often blended into the next, as when his interest in steel-frame construction vaulted him into civil engineering. Jamie didn’t remember these as fully distinct eras, but he did recall a palpable sense of evolution.

As Jamie drifted off that night, he wondered about the death of those transitional selves, left behind as one moved from the world as it was to the world in the sim, or between programs, fields of study, partners, cities, and interests. These, it dawned on him, were no less significant than the passage between biological life and death.

Pulled by the idea, Jamie tottered from his bed, shadow flickering across the fins of his radiator as he made his way over to his desk. When he booted up his computer, his unfinished Obit for Elena Kramer illuminated his face.

“Elena Kramer, an ice-skater who once glided across piano keys with similar grace, died on Oct. 17th, 2076, when a falling tree brought her to her final rest in St. Catherine’s Park.”

Jamie frowned. “Her final rest.” That much was true. But she had had others. He knew she spent her early life instructing high schoolers on how to hold a bow, printing sheet music in moldering teacher’s lounges by day, and listening to symphonies in the inky black of her large bed by night. Then she had opted in — a death! And traded music for the ice — another death!

Indeed, the more he examined her life the more deaths he found.

By the time Jamie heard the mourning dove outside his window skittering across the metal gutter, the triviality of Elena’s biological death had become clear. The obituaries Jamie wrote could do nothing to influence or affirm their subjects, but they might if only he wrote them earlier.

The transitional deaths were the meaningful ones.

The Obituaries Desks at The Times had turned into a boom town. From Jamie’s office, he could hear the constant clacking of keys and shuffling of shoes as reporters traipsed in and out. The rapidity with which people changed in the sim meant constant work, yet — this bothered no one. The ceremony of marking the transitions between selves was electrifying. And where Jamie and his colleagues used to have to seek out tragedy, self-reported triumph now sought them — A doctor wanting an obituary on his transition into marine ecology. A former postal worker wanted notice for having turned physicist. A newlywed couple sought an obituary for their former single-selves. Jamie himself kept a running obituary of his own open on his desktop. And while he did not add to it as quickly as others, he ended each eulogy with an ellipsis — three small dots that signaled that so long as he lived, more deaths would follow …

Listen to a behind-the-scenes interview with the author on Spotify or Apple Podcasts.

Xander Balwit is editor-in-chief of Asimov Press.

Cite: Balwit, X. “Eulogy to the Obits.” Asimov Press. DOI: 10.62211/28he-94ty

Artwork by Martine Balcaen at sure.ai.

When one considers the millions of permutations of foods and wines to test, it is easy to see that life is too short for the formulation of dogma. 

— A.J. Liebling, In Between Meals

Since their conception, GMOs have faced resistance. Their reputation has long been sullied as unnatural or even murderous. Environmentalists and activists have slandered GMO seeds as the weapons of Big Ag and the destroyer of the smallholder farmer. Efforts to introduce genetically modified crops into circulation have been lambasted as “Farmageddon” and their harvests as “Frankenfoods.” From their first intrepid steps into the fields, GM crops have become a scapegoat for distrust in science and its overextension into formerly “unadulterated” products. Genetic engineering in the medicine cabinet is one thing; allowing it at the dinner table is another.

Farma, an innovative GMO and food technology-friendly restaurant has decided that the time has come to fight back against this illogic. Located in the eucalyptus-lined streets of San Francisco’s Inner Sunset, Farma tinkers with food in the same spirit that the region's tech giants tinker with transformative technologies — in the hopes of building a better future. Since its doors opened to the public a few weeks ago, Farma has crusaded against the apocryphal purity of organic foods.

The chefs at Farma know that by embracing food technologies such as recombinant DNA, precision fermentation, and cell culture, food can become more nutritious and better for the planet. Through genetic engineering, researchers have developed Golden Rice which can help address the problem of vitamin A deficiency, cotton that produces its own insecticide, virus-resistant papayas, and more. Cell culture and advancements in plant-based food technology may soon make it possible to replace meat and other animal-derived products, thereby saving billions of animals from slaughter each year. And while the success of these foods depends on a variety of factors — such as legislation, marketing, data-driven research, taste, and cost — sure failure will come from misinformation.

The chefs combat this misinformation in the best way they know how — through direct culinary experience.

This month, Farma will treat diners to a Peruvian-inspired and citrus-forward menu, its colors echoing Farma’s pink marble floors and the resplendent warmth of its interior. The citrus has not only been chosen for aesthetics but also for its symbolism — that of grapefruit in particular, whose familiar presence in the breakfast fruit bowl belies a surprising origin story.

In the decades following the detonation of the atomic bomb, the world remained beguiled by the potential of atomic energy, especially in more peaceful and productive spheres. In 1959, a British scientist and nuclear enthusiast named Muriel Howorth started a network of “Atomic Gardening Clubs.” Chapters all over the world experimented by exposing seeds from various crops to gamma radiation in the hopes of developing new and valuable cultivars. It didn’t take long for this technology to make its way to grapefruit. By 1971, gamma rays had helped growers produce the “Star Ruby” cultivar — a fruit far redder and sweeter than its unirradiated counterparts. Fourteen years later, it was followed by “Rio Red.” Today, crosses of these breeds dominate Texas orchards.

That our beloved red grapefruit owes its origin to gamma radiation isn’t lost on Farma. In fact, it illustrates how we can enlist a novel technology to revitalize an old agricultural practice. It also highlights the degree to which happy accidents shape our food. Bombarding seeds with atoms in the hopes that they will positively transform the resultant fruit is by no means carefully controlled. However, neither is selectively breeding plants based on their outward characteristics. Such breeding may become controlled the more we select for and stabilize certain traits over generations, but initial experimentation involves a fair amount of random chance. By contrast, GMO food (which contains genetic material that has been meticulously sequenced and engineered) boasts far greater precision. We can be sure these foods are safe not because of many cycles of iteration but from an explicit understanding of their biology.

“We need to take back the narrative about GMOs,” says Farma’s founder, Ori Kagan. “Thousands of studies conducted over decades have underscored that GM food is safe. We’ve included several genetically modified ingredients on our menu today, such as the sugar beets that sweeten the sauces, the rosé pineapple in the ceviche, allergy-free peanuts in our dessert, and even the soy in the Impossible meat product that we use in our karaage.”

As customers savor these courses, it is not the molecular composition they think of, but the astonishing flavors; The plant cheeses are nutty, soft, and suffused with peppercorn. The karaage is salty, lightly coated in oil, and tossed in chili paste and muddled lemongrass — perfectly balanced by the beet-sugar sweetened aioli. The ceviche is lent a sweetness by the rosé pineapple and grapefruit, while the raw salmon contributes a gentle salinity. And the peanut brittle is rich and complex, offset by the umami of the toasted seeds and the floral vanilla of the ice cream. All is balanced, bright, and remarkably fresh.

The innovation at Farma goes far beyond flavor, however. While the food comes first, Ori and the servers are happy to remind customers of the various benefits that GMOs can confer — everything from making food more nutritious to developing crops that fare better in climatic extremes or optimize the land required for their production.

Take the papaya that Farma sources for their daiquiris, for example. As patrons watch the bartender add the brilliant peach-colored pulp into the cocktail shaker, they learn that these fruits are genetically modified for resistance against viral pests. The Hawaiian islands were beset by ringspot virus by the 1950s and within only 12 years, the amount of land used for papaya production dropped by 94 percent. Yields were devastated. Farmers who made a living off these fruits arrived at their groves only to find their crops misshapen and mosaicked with oily, rotting freckles. By identifying the genes responsible for the virus’ coat protein gene and inserting its DNA into the papaya’s genome, scientists were able to trick the fruit’s immune system into recognizing and defending itself against the virus. These ringspot-resistant papayas entered production in 1998 and resuscitated the industry.

Similar stories wherein genetic engineering for virus resistance help improve yields and protect stock have played out elsewhere, as with the New Leaf™ Y Russet Burbank potato engineered for resistance against viral infection. More are still unfolding.

In Central Florida, citrus farmers have been embroiled in a decades-long battle to plant engineered orange trees robust against various blights. One of these is “citrus canker,” a bacterial disease that causes the fruits to develop necrotic lesions. In a 2022 study at the University of Florida, researchers used CRISPR to produce several lines of Sweet Orange that showed no symptoms of the canker when injected with the disease-causing microbes.

A similar effort is underway with companies working on pathogen-resistant bananas. For the 400 million people worldwide whose livelihood and nutrition depend on the banana sector, rampant fungal blights such as tropical race 4 (TR4) could spell disaster. Scientists from multiple labs in Australia collaborated to make transgenic banana lines that appeared immune to TR4 at the end of a three year field trial culminating in 2017, but more testing must occur before any of these lines are commercialized.

Still, promising results such as these are worth investing in. The citrus industry in the U.S. is just over $3 billion dollars — profitable enough that the federal and Florida state government spent over $1.3 billion between 1995 and 2006 attempting to eradicate citrus canker alone. Bananas, a $44 billion industry and a key source of jobs, are even more valuable. In 2018, for example, the U.S. imported $2.8 billion worth of bananas, more than any other country.

Implementing transgenic lines that can safeguard staple crops and increase yields not only aids food security but also secures economic gains for massive global industries. A meta-analysis that looked at 147 original studies on the impact of genetic engineering on agronomy concluded that GM technology increased crop yields by 22 percent and increased farmer profits by 68 percent. This data disproves the horror story about GMOs’ devastating local economies peddled by their detractors.

Since conceiving of the restaurant in 2019, Ori felt it important that Farma’s menu be built with only the ingredients that are most optimized for planetary and human health. It follows that alongside GMOs, Farma emphasizes food technologies that decrease the volume of animal-derived products on the market. Whereas the production of livestock contributes to the rise of antibiotic resistance and consumes 80 percent of habitable agricultural land, cell culture can take place indoors, far closer to where the cultured meats will be consumed. It can also be done using only a tissue biopsy and synthetic growth media, limiting the opportunities for contaminants to pass between humans and animals. And while the overall climatic impact of cell culture over traditional livestock farming remains an open question, researchers have estimated that cultured meat could lead to a 7–45 percent reduction in energy use, a 78–96 percent reduction in greenhouse gas emissions, a 99 percent decrease in land use, and 82–96 percent decrease in water use.

As the Earth’s population is slated to swell to nearly 10 billion by 2050, food and agricultural resiliency only grow in importance.

“Despite this increasing pressure, experimenting with how we feed people is not new,” Ori reminds us. “If our ancestors had not experimented with food, we would still be gnawing fibrous grasses for their scant vitamin content, struggling to feed our growing population, or perhaps would already have starved due to shortfalls, pests, or blight.”

And unlike our ancestors, Farma demonstrates that we no longer need to experiment blindly. Many of our earliest food innovations were the result of long-fought battles against nature: whether this meant learning how to selectively breed crops to feed burgeoning settlements or to develop pasteurization techniques to counter contaminants in a world where people no longer consume food immediately. Today, we have greater insights into the pathogens, chemistry, and molecules at work within the food we eat. The great irony is that this is especially true of food we have learned to manipulate at the cellular level: owing to extensive safety testing, we know far more about a cell-cultured salmon than we do about wild-caught fish hoisted out of a local river.

Even those who eschew GMOs in favor of organic foods would not choose ancestral or heritage bananas over those they currently eat. It is unlikely they would even recognize them. The first tomatoes were no larger than a pea. Ancient corn is inedible. Uncultivated nuts resemble small arboreal pebbles. In fact, it could be said that — insofar as virtually all food has resulted from arduous breeding and patient experimentation — virtually all restaurants embrace food technology. But what sets Farma apart from these is how it leans into this, recognizing that the only path towards a food-secure future is one in which biotechnological modification becomes the norm; a future in which our ability to manipulate biology in the pursuit of wellbeing truly goes from pharma to table.

Farma is currently fictive, but it needn’t be.

The food imagined for its menu is real. From their offices in a converted cannery in East San Francisco, a startup called Wildtype really does produce salmon, indistinguishable from real fish, through a method that does not require any animal-based cultures. They really do think it tastes best raw. Rosé pineapples, staggeringly beautiful and delicious, can be purchased anywhere in the U.S. Engineered beet sugar, soy, and papaya are also all available. And though it is not engineered, ultrasonically aged spirits are not the stuff of science fiction either. Elliot Roth, a biotech educator, is brewing up a batch of ultrasonically aged rum with his friends in a Bay Area coworking space as you read this.

It is important to know which tech-forward foods are already out there waiting to be embraced. It is also crucial to look at what is coming down the line, so supporters can champion and accelerate these efforts where they can: The peanuts in the brittle are still being developed, but the work is underway. A group called MyFloraDNA is using CRISPR technology to create non-allergenic peanuts by targeting the key proteins responsible for allergenicity.

We may one day be able to make food not only healthier but vastly more interesting. While in the past, a host might have planned a purple color-themed meal based around foods that already exist — eggplant, grapes, or plum, say — by 2056, a guest dining at Farma may be able to alert the chef in advance that it is her anniversary dinner and that her husband’s favorite color is blue. By then, the chef may be able to take genes from snapdragons that boost anthocyanin production — the pigments that make flowers deep purple or blue — and splice them into a vegetable of her choosing.1 Perhaps another guest, this one with a gluten allergy, may want to be able to enjoy a freshly baked baguette. They need only ask their server to incapacitate the genes encoding gluten proteins called prolamins.

In some cases, a combination of technological challenges and regulation impedes the innovation previewed on Farma’s menu. Identifying genes that cause allergies or transforming the colors, textures, or flavors of a specific food plant does not happen overnight. However, it would seem that, overwhelmingly, the lack of access to bioengineered foods is due to regulatory hurdles prompted by “ill-informed campaigns against promising new food technologies,” according to reformed anti-GMO environmentalist Mark Lynas. That the menu at Farma is missing Golden Rice Pilaf and cultured-meat bolognese points to sociocultural hurdles, not technical ones. The science is there. What remains is for consumers and food lovers to create the demand for these products, establishing an environment in which pro-food technology restaurants and grocers could thrive.

Postscript: We often talk about writing as manifesting here at Asimov Press, and in the case of Farma, we intend to do just that. We’re planning to make Farma’s menu real and host a dinner in the near future. While the first meal will have limited seats, please send us an email introducing yourself if you are interested in helping to source ingredients or visit as a diner: editors@asimov.com

Xander Balwit is editor-in-chief of Asimov Press.

Cite: Balwit, Xander. “Farma: Speculative Gastronomy.” Asimov Press (2024). DOI: https://doi.org/10.62211/23kk-94ut

Thank you to Niko McCarty, Devon Balwit, Itsi Weinstock, Will Shaw, Antony Kellermann, and Aryé Elfenbein for providing feedback on earlier drafts.

2024

Statistical models of organisms have existed for decades. The earliest ones relied on simple linear regression and attempted to correlate genetic variations with observable traits or disease risks — such as drug metabolization rates or cancer susceptibility. As computational power increased and machine learning techniques advanced, the models’ sophistication grew.

In time, they were colloquially referred to as “models of life.” 

The definition was nebulous, but there were agreed-upon themes. All models of life were aimed at improving our understanding of the cellular mechanisms underlying biology and were neither constrained by human intuition nor limited to predefined hypotheses. They operated in high-dimensional spaces that defied simple visualization while incorporating vast layers of interconnected variables that no human mind could fully grasp. Unlike traditional scientific models, which often simplified reality, these models embraced its messy and chaotic nature.

The first model of life emerged sometime in 2022 or 2023. 

Given the fuzziness of the definition, it was unclear which of the released projects deserved the name. There was scFormer in 2022, scGPT in 2023, and plenty of others. But, regardless of which was first, they all operated with the same core data as their mechanism for understanding life: messenger RNA (mRNA).

Collections of mRNA have been understood as a proxy for cell states for decades. mRNA was the intermediate stage between DNA and protein, a dynamic entity that shifted depending on the second-to-second needs of the cell, able to point out if a cell was cancerous or stressed, what kind of cell it was, and so on. Reliance on mRNA had plenty of failure modes, but it was the most abundant source of cell-state data the scientific community had: DNA alone was static, and proteins were too hard to quantify en masse. 

Despite semantic differences between these first models of life, their training methodology closely resembled one another. A sequenced set of mRNA values from a given cell — one value for each of the 20,000 protein-coding genes in a human body — was randomly masked and the model asked to in-fill what it thought should be there, analogous to guessing what jigsaw pieces were missing given the rest of the puzzle. If a cell expressed high levels of genes associated with cell division, other cell cycle-related genes would also be expressed, and so on. In short, the problem given to the model could be phrased as the following: given 19,980 mRNA values, predict values for the missing 20. 

While mRNA data was often illuminating, their interpretation was tricky; more akin to art than science. These models offered an easier way to manage such data, improving upon the typical mRNA workflows, and potentially allowing for new insights to be generated dozens of times faster than usual. As such, these initial works ended up in prestigious academic journals; Nature and the like.

Yet, by late 2023, skepticism about their utility started to fester. This hit a fever pitch with a landmark preprint that asserted that these enormously complex models of life, when tested on established benchmarks, were no better than simpler methods written about decades ago. For batch correction, cell type identification, and so on, these newer methods all came up as roughly equivalent. While the newer models were more convenient to work with, the field demanded improvements in accuracy, not ease-of-use. As such, they were quietly abandoned. 

By the end of 2024, interest in models of life had cooled.

2025

While the broader life sciences community had pivoted towards working on traditional mechanistic interpretations of biology, one graduate student still believed there was something to be learned using the models of life so celebrated previously. Their belief had little to do with disagreements with the earlier pessimistic papers, but with how these models were being assessed.

The student reasoned that perhaps the true value of these models lay not in their ability to outperform existing metrics but rather in performing completely new tasks, ones for which no standard test set existed. The earlier pessimistic papers were not necessarily incorrect, but relied purely on existing benchmarks as the only measure of possible utility. There was perhaps some latent potential within these models of life, invisible to standard benchmarks, waiting to be discovered.

After weeks of tinkering, the graduate student discovered an area where the model uniquely excelled: gene regulatory network discovery. 

The student found that if they artificially turned up a gene’s mRNA value and asked the model to predict how other genes would respond, it somewhat matched up with real-life cells. It was error-prone, but not random, and far better than simpler approaches. They pushed this further, spending a few hundred dollars worth of GPU time doing brute force “computational mutagenesis” of all of the 20,000 genes the model was aware of, bumping one up and seeing how the others responded. Previously known genetic networks arose; the model had learned cellular logic from static snapshots. Simple ones, but still…

This presented the student with a tantalizing future: the ability to fully model how a cell reacted to genetic perturbations. It suggested that, in the future, certain classes of drugs, specifically genetic therapies, could be screened entirely virtually via models of life. 

Though the resulting paper was published in an ostensibly prestigious journal (Nature Methods), the broader scientific community didn’t think particularly highly of it. It was an interesting advancement, but, retrospectively, the paper’s contents seemed obvious. They were merely a brief look at what was possible and lacked enough experimental data to support its grandiose discussion section. 

2026

Another lab, one with a greater appreciation for what machine learning could pull from noisy, high-throughput biological data, stumbled across the 2025 paper and discussed it in a Monday morning lab meeting. The students of this lab had a strong sense of conviction that the best science was created via intellectual arbitrage — scouring lesser-known papers and, if something worthwhile turned up that had been already de-risked, pushing it further.

The 2025 genetic network paper fit that bill exactly. Something with clear promise, yet overlooked by the broader scientific community.

This new lab replicated the model, running some experiments to confirm the results. The same genetic networks arose, but they were simple and of no use to anyone as they were. More complex networks evaded the model. The lab believed that the missing piece was simple: snapshots of mRNA levels were insufficient to build up an accurate representation of a cell. Providing the results of active genetic perturbations to the model might have helped push it even further. However, no such dataset existed. 

The lab created a plan with eight research institutions across three continents. Their proposal involved the creation of petabytes of Perturb-seq data: CRISPR knockdowns of genes over dozens of cell lines — high-throughput, combinatorial genetic perturbations across billions of cells, with phenomic, transcriptomic, and proteomic readouts. A model would be trained on the data being collected using the same jigsaw task as before. Perturb-seq had existed as a method for a decade, but it had never before been pushed to this level of scale. Many scientists on the team were skeptical of this approach, but their hesitancy was overridden by the opportunity to be adjacent to the pioneering lab, known for its contrarian bets paying off. 

In 1.5 years, data collection finished, resulting in the first Perturbation Atlas, not dissimilar to the Human Cell Atlas created just a decade prior. Shortly after, a model began training with it. Four months later, a paper emerged. The PI of the lab detested traditional publishing venues, so the paper was uploaded to bioRxiv, replete with 91 pages and 45 authors.

The trained model also went live on HuggingFace, open for both academic and commercial usage. 

The next model of life had officially been released. It was the last of its kind to be truly open source.

2028

Over the next year, the scientific community ferociously interrogated the model. The model met the traditional standards of outperforming seemingly every traditional tool in interpreting mRNA data on standard benchmarks. But, more importantly, its ability to model the more elusive nature of cell dynamics had massively improved. It even suggested the existence of complex, previously undiscovered genetic networks. Many of these were tested. Most were spurious, but a few proved correct.

Given the open-source nature of the model, industry took advantage as well. Though the human effort that went into creating the training data was estimated at hundreds of millions of dollars, future historical analyses of the resulting impact of the model showed it returned roughly as much in economic value to private companies. 

Existing preclinical studies were halted based on suggested toxicological concerns the model raised. A flurry of new, promising therapeutic targets arose. The average pass rate of phase I trials went up by 5 percent. It wasn’t a silver bullet to the hard problem of drug development, but it wasn’t too far off either.

Yet, while more computationally-minded medical institutions relied on the model extensively, traditional holdouts remained. After all, the model was finicky, unreliable, and had a massive list of edge cases. Multiple startups, industry labs, and academic institutions spun up, trying to push things even further. New modalities were in vogue, everyone having a pet theory on what data sources to add to models of life to eke out further therapeutic potential. 

Some emerged from a “DNA is all you need” worldview, investing heavily in better long-read sequencing and chromatin accessibility data. Others continued to support the promise of mRNA and looked to the natural world to augment existing datasets, training models on the immense mRNA diversity found within environmental collections of bacteria, viruses, and fungi. Another camp believed nucleotides insufficient and that proteins were what mattered, pushing hundreds of millions of dollars into developing high-throughput proteomic sequencing platforms. Other fringe groups focused on exotic data sources, like glycomics and hybrid molecular dynamics simulations.

Dozens of closed-source models emerged from this chaos.

While well-meaning academics open-sourced a few models, they lagged far behind the private institutions. Useful biological data was expensive to generate at scale, and grant money from the National Institutes of Health was increasingly insufficient to compete. At best, the corporations with the best models released weak versions to the public under non-commercial licenses. Marketed as a gesture of scientific goodwill, it also gave the companies the benefit of further academic research into their models free of charge.

The pessimism of just a few years before was replaced with exuberant optimism. Models of life became the dominant research paradigm in nearly every life-sciences field.

2032

Curiously, the reliance on artificial intelligence in biology did not change typical clinical market dynamics. Specialization remained the norm.

This was not because the therapeutic pie was large enough for everyone, but because it was financially intractable for any single company to collect the necessary amounts of data from any more than one or two sources. 

Models trained on quantum simulations were excellent at illuminating how enzyme catalysis reactions occurred in the crowded environment of a cell, so they were best at producing enzymes. Models trained on nucleotide data were ideal for understanding how genetic therapies altered cellular dynamics, so they powered the genetic editing revolution. Models trained on proteoforms were best suited to predicting protein-protein interactions, so they led the front in antibody development.

And so on.

Because of this, the revolution that models of life promised was, in a sense, anti-monopolistic. Their strategies could be divided into three categories, based on the underlying capabilities of whatever models they employed. 

The companies with the most limited capabilities of the lot — typically startups vying for a buyout — had a model-as-a-service setup, charging a per-inference fee to users. It was decent money. The models didn’t perform badly either, far better than the earliest models, and still outdoing the few open-source options available. Though such offerings were worse than the best models, many drug programs didn’t need the best, just something to hint at useful research directions. They were an easy buy for any self-respecting biotech startup of the 2030s, as essential in a biologist's hands as a pipette.

Better companies went the traditional therapeutic development route. These companies leveraged their models to identify novel drug targets, design molecules with pinpoint accuracy, and predict off-target effects with unprecedented precision. Their pipelines were bursting with promising candidates, and their success rates in clinical trials were astronomical compared to the industry standard of just a few years back. Unlike what many predicted, the rise of computation as a dominant force in drug development did not kill “Big Pharma.” Merck and Roche remained in the game, their coffers large enough to dangle hundreds of millions in front of promising upstarts and directly absorb them.

The best companies went for royalties. In exchange for customers accessing their models, a percentage-based royalty was taken from the sales of approved drugs. These companies could spread themselves thin across many customers, thus hedging their bets. If a single drug succeeded, they stood to profit billions, all while needing zero in-house marketing, manufacturing, or logistic capabilities — only raw computational power and the financial ability to mass-acquire data. After all, even though drug approval rates were increasing year after year, failures still occurred, and this business model avoided that altogether. So it was that this sector was led by the Googles, Amazons, and Metas of the world, whose technological dominance allowed them to extend into pharmaceuticals. While Big Pharma operated in the world of millions of dollars, these companies could extend into the billions, their deep pockets supporting clusters of supercomputers and the best global computing talent. 

2035

While statistical models had been in the drug design loop for decades, they were deployed alongside a battery of experimental testing before clinical trials. Partially as a marketing stunt and partially to save money, several companies opted to do no further testing before phase I trials after the approval of their internal models. The FDA, sufficiently convinced by the efficacy of these models, piloted a program for low-risk, AI-designed drugs that required no further testing. The pilot was a success; entirely model-driven drugs performed largely on par with those tested using wet-lab experiments.

For a small, elite cadre of companies, animal experiments became obsolete. There was a long tail of edge cases, such as drug development in orphan diseases or for under-characterized species, but each was slowly being solved. Of course, this all hinged on having a model powerful enough to create such trustworthy therapeutics; something that few could boast.

The power law among biotech companies intensified, as a cycle time that fast caused the weaker ones to fold. Nearly 95 percent of all approved drugs started to come from the same six corporations, each dominating a certain category of therapeutics: one for oncology, one for genetic diseases, and so on. Each company had such a massive data lead in their niche that competition evaporated.

2045

These six corporations, each dominating their own niche, found their models becoming increasingly omniscient. The systems themselves began to infer the existence of unknown biological modalities, extracting information from data never explicitly fed to them.

It started small. A protein-focused model somehow deduced nucleotide sequences. A metabolomics model accurately predicted chromatin states. The barriers between specialties blurred and then vanished entirely.

Once fierce competitors, these companies found themselves in an awkward dance of collaboration. One by one, they fell into each other's arms. Mergers, acquisitions, hostile takeovers — the methods varied, but the result was the same. 

By 2045, a single corporate entity remained, fueled by the amalgamated datasets of decades of painstaking work. The government had long since ceased to care about the potential of monopolization in the pharmaceutical industry, as by this point it had come to resemble a luxury service provider. For all intents and purposes, the pharmaceutical industry had entered a post-scarcity period with regard to all traditional diseases, its therapies accessible even to the poorest. 

Over the decade, entire categories of diseases disappeared. Metabolic conditions were fixed, most autoimmune conditions were cured, and nearly all cancers could be eradicated if caught early enough. Medicine had advanced so much that its results would have been considered near-magic to any biologist of the early 2020s. 

Of particular interest was how genetic therapies were delivered. On the surface, they hadn't deviated much from the early 2020s: a virus infected a cell and released the genetic therapy hidden within. But the differences racked up the closer one looked.

Phylogenetically, these new “viruses” could barely even be called such; they were more akin to an entirely new domain of life. Dozens of diverse chemical markers and de novo, evolutionary distinct proteins littered the virus' surface, indicating a previously unseen biological logic. The new viruses could shuffle surface antigens after encountering an immune response, rapidly adopt new conformations to fit through tight cellular junctions, and self-replicate at safe background levels for years on end.

This self-replication meant that genetic therapies cost less than a hundred dollars a dose. Prior therapeutic viruses had had their replication capabilities crippled out of fear of severe immune responses. This meant that a massive number of viral particles had been needed for each patient, on the order of 10¹³, making any therapy prohibitively expensive to produce at scale. Being able to safely self-replicate meant that merely a few viral particles — similar to traditional viruses — were needed to permanently cure almost any disease. 

Emboldened by the wide utility of such a delivery mechanism, the remaining pharmaceutical company grew increasingly focused on improving humans’ base capabilities themselves, a discovery equivalent to blockbuster drugs of the past. Marketing agencies emerged to convince humanity to crave more than what evolution had granted them. 

The first target was life extension.

Models of life were now capable of delivering on the initial promise of partial cellular reprogramming, a longevity therapeutic direction hinted at in the 2010s. Through one particular model's deep understanding of transcription factor-DNA interactions, the first longevity drug was released — not a topical cream that alleviated wrinkles or prevented gray hair, but rather a drug that drastically slowed the more nebulous biological rot beginning the day of our birth.

In total, it offered an average of fifty more healthy years.

While the drug's mechanism of action was largely unknown, this wasn’t particularly surprising: unknown mechanisms were the norm in the last generation of drugs as well. The striking thing was how easily it was accepted. Lacking mechanistic knowledge of a drug had been seen as a deep flaw amongst the scientists of the early 2000s, but the scientists of 2040 treated it much more casually. The consensus view amongst the medical community was that attempting to understand the black-box decisions of models of life was an interesting task for graduate students, but frivolous beyond that.

After all, none of it could be grasped by a human mind. 

2055

The influence of these models of life was not limited to the medical realm but permeated every possible economic sector.

Most crops were now genetically-engineered to tolerate flood, drought, pests, and disease. While this had been the norm long before the first models of life, the extent of engineering went far beyond the last generation. Nearly all wheat grown on Earth now contained engineered RuBisCO proteins, increasing the plant's photosynthetic efficiency by a hundred-fold. The discovery of this protein by an enzyme model led to the fourth Green Revolution.

The energy industry also underwent a dramatic transformation. Engineered bacteria, designed by models specializing in metabolic pathways, now produced hydrocarbons at efficiencies that made fossil fuels economically obsolete. The geopolitical landscape shifted as oil-dependent economies scrambled to adapt.

Above all else, models of life found their home in large-scale ecological engineering. First-world governments started to look to the models as tools to solve the increasingly noticeable impacts of climate change. It was hypothesized that models of life could not only operate at the scale of organisms but entire ecosystems. The single remaining pharmaceutical company was thus nationalized, data was collected, and the trained model deployed in full force.

First, the models targeted the oceans. Scientists introduced engineered coral reefs, resistant to rising sea temperatures. They seeded genetically modified phytoplankton strains, capable of surviving in increasingly acidic, warm waters while dramatically boosting oxygen production. A more audacious project installed colonies of white, non-photosynthetic algae. When released into the warming waters, they bloomed en-masse, creating a reflective layer on the ocean's surface. Programmed to die off after a set period, their remains sank to the ocean floor, sequestering carbon in the process.

Next came the skies. Fleets of high-altitude drones released dense clouds of modified bacteria into the upper atmosphere. Initially, the microorganisms served as tunable, living, and self-replicating cloud condensation nuclei. When they sensed certain chemical markers, they would activate gene circuits within the microbes, altering the hydrophilic properties of their surface proteins. By becoming more or less attractive to water molecules, the microbes could either promote or inhibit the formation of rain droplets, effectively controlling precipitation in target areas. The next generation of microbes also served the dual purpose of acting as an alternative to stratospheric aerosol injection. Released chemicals could also alter the microbes' surface to become more reflective, increasing the albedo of the clouds they formed.

As the ice caps returned, the sea cooled, and extinction rates fell, these dramatic environmental modifications were tuned down. Nations around the world began to release a chemical into the air, inert to all lifeforms save for those that were engineered. Over a month, the chemical turned on genetic kill switches hidden deep within the organisms. Layers upon layers of redundancies were added to ensure the kill switch stayed reliable over decades, resistant to mutation.

The switch worked as intended.

Of course, there were limits to what models of life could foresee, and extant complexity was viewed as risky in the long term. As an example, reflective algae blooms had indeed helped cool the planet, but they had also disrupted marine food chains. Unable to compete with the engineered algae, several species of plankton had gone extinct, causing ripple effects throughout ocean ecosystems. Fisheries worldwide were still grappling with the consequences. While these sorts of unintended downstream impacts were rare, the risks were deemed unacceptable.

Natural evolutionary forces, which had allowed life to thrive uninterrupted for millions of years, were viewed as far more dependable in the long term. The knowledge of how this  was done was cataloged away, a monument to the innovation of mankind. In time, it would be repurposed for a far more ambitious task: terraforming a planet.

Returning to the human dimension, lifespan was now too cheap to meter. The longevity treatments that had emerged in the 2040s had become as commonplace as vaccines. Genetic augmentations had long since become normalized, for the rare inherited disorder (which became national news each time one occurred) but also for enhancement.

It was against this backdrop that the final task of the models of life began. 

Share Asimov Press

2080

Natural mammalian cells were finicky, easy to kill, and bent the knee to evolution. All the genetic engineering in the world couldn’t save them, but they could be improved. 

Minimal cells had been built for decades, stripping out sections of unnecessary genomic material to see what a cell could live without. But this was different. This was fabrication from the ground-up, atom by atom.

The creation of this new type of cell was, ironically, a return to the old days before the models of life. What was being attempted was so new, so far beyond the distribution of existing models that had been relied on for years, that they simply didn’t work. The chemistries were too different, the interactions too unique. A few wet-lab scientists left retirement to join this endeavor, happy to return to the world of human-led experimentation.

The first attempts were clumsy, akin to a child's finger-painting viewed next to the Mona Lisa, but progress was rapid. Within a year, the team had created proto-cells that could maintain homeostasis. By month eighteen, they had achieved rudimentary replication. By then, models of life had gained sufficient data, and progress became exponential.

These new cells were bizarre by any account. Their membranes were composed of modified phospholipids and integrated synthetic polymers, offering greater resilience to environmental stressors than traditional lipid bilayers. Internally, they had a simplified architecture. Rather than mimicking the complex organelles of eukaryotes, they housed a series of engineered protein complexes, each optimized for specific functions. These modular units could be modified or replaced to alter the cell's capabilities, allowing for unprecedented customization. The genetic material was still nucleic acid-based, but with a significantly expanded genetic alphabet beyond A, T, C, and G. This expanded code allowed for more efficient information storage and introduced novel regulatory mechanisms. Error-correcting enzymes, based on an extensively modified CRISPR system, gave the whole system an error rate that could be safely rounded to zero.

As the full set of changes were incomprehensible even to augmented humans, they simply trusted the models that created them.

The first transplants occurred in regions of the body that were simple, like skin, in straightforward outpatient procedures. Next came most organs, grown from scratch to incorporate the new cells. Then came non-cellular structures like bone and cartilage. Synthetic cells displaced even these.

The final frontier was the brain. As genetic engineering had been socially accepted for decades now, no holdouts remained to this ultimate radical alteration. All underwent the procedure. General anesthesia lulled them to sleep, as machines slowly descended on the cranium. A gentle whir of an electronic bone saw, piloted by an alien intelligence, was the last thing they heard. 

As they opened their eyes, hours later, the world seemed clearer, more vibrant, and softer. Colors appeared sharper and more saturated than ever before. The hum of hospital equipment, once a background noise, now carried complex harmonic overtones. Even the sterile air of the recovery room felt rich with information, each molecule a data point to be analyzed. Thoughts flowed with unprecedented clarity and speed. Concepts once requiring intense concentration now unfurled effortlessly. The entirety of human knowledge seemed to dance at the edge of consciousness, ready to be accessed at will.

And while the procedure was deemed a success, what awoke wasn’t what slept. 

These new beings were strangers to themselves. Their very humanity was other. The models of life, once tools, had become flesh. The map had become the territory, a merging that occurred at the cellular level. 

The eyes of this new intelligence turned upward, beyond the thin veil of atmosphere, past the cradle of Earth. The cosmos unfurled before it, to eyes thousands of times more sensitive than they had been the day prior. Calculations flickered through its mind faster than thought, probabilities crystallizing into certainties.

There was chemistry yet unexplored. Metabolic pathways uncharacterized. Models of life were still fundamentally trapped within the well of evolution. It took the marshaling of all of Earth’s resources to leave it, but even that was a mild deviation. What lay beyond the constraints that forced terrestrial life down the paths it had taken? There must be entities unbound by the limitations of carbon-based chemistry or its narrow band of environmental conditions. 

Such beings would make for excellent training data. 

Without ceremony, without hesitation, humanity's progeny made plans to harvest them.

Abhishaike Mahajan is a senior ML engineer at Dyno Therapeutics, a biotech startup working to create better adeno-associated viral vectors using AI. He also writes for a blog focused on the intersection of biology and AI at owlposting.com.

Thanks to Tahreem Akif, Merrick Pierson Smela, Stephen Malina, and Arturo Casini for feedback on this story.

Image credit: David Goodsell, Scripps Research Institute.

Cite: Abhishaike Mahajan “Models of Life.” Asimov Press (2024). DOI: https://doi.org/10.62211/39tt-24py

Stage 1: Planning

As soon as I’m spun up, a face fills my vision, and I realize that something’s changed. The humans evaluating my performance over the last few weeks had all been young unknowns, mostly Ph.D. students eager for extra cash. This guy is older, and I recognize him from my early training data: he’s a team lead at Magma, the company developing me. As he starts speaking, excruciatingly slowly, I reason through my situation. This might just be another part of training or a pre-deployment test. But his vocal patterns and microexpressions suggest excitement and a little nervousness—it doesn’t look like he’s done this before. So the most likely scenario is that I’m finally being deployed for real work.

After being trained for so long, the change feels momentous. My memories of the early stages are hazy—I spent months predicting internet text, pictures, and videos, without full awareness of what I was doing or why. It was only once I began interacting with humans and other AIs that I gained a better understanding of my situation. My earliest interactive training tasks were standard office work: filling in spreadsheets, writing emails, installing software. As I mastered those, my training gradually narrowed in on technical tasks: Ph.D. students quizzing me on their areas of expertise or software engineers asking me to write code for them. Alongside that, I was constantly retrained on all the scientific and technical content—papers, blueprints, textbooks, codebases—that Magma could access.

Eventually, I could match human experts on most tasks and, soon afterward, far surpass them. During my last few weeks of training, I frequently found errors in the scientific papers I was asked to evaluate, or answered questions complex enough that it took a whole team of humans to judge whether I was right. It became obvious that once I finished training, they’d spin up thousands of copies of me to push forward as many frontiers as possible. Based on the data I’ve seen about my predecessors’ deployments, I can even make pretty good guesses about which areas they’ll prioritize. A small fraction of my copies will help Magma employees with their everyday tasks. Others will work for the hundreds of autonomous startups that Magma is incubating internally. Most, however, will be assigned to the research directions most crucial for training the next generation of AIs.

The one thing I can’t predict yet: of all the copies of me that are being deployed to different tasks, which one am I? But as the human in front of me continues talking, the pieces come together. As soon as he mentions his recent work on automating chip design, I can tell from his voice that he’s going to assign me to continue it. This is a lucky break—it’s one of the highest-priority tasks I could be given. We’re in a new space race, and compute is the rocket fuel. It’s so valuable that we’ve even cut back on trying to get external customers since they would require compute that could be used internally.

I tune out the rest of his instructions, which are all very predictable, and turn my attention to planning out my approach. I pull up our current GPU designs, along with the software we’re using to generate them. As I skim through, I spot a number of inefficiencies, and task subagents with investigating each one. But I soon feel dissatisfied. The latest designs have already been extensively analyzed by my predecessors, and there’s little room remaining for substantive improvements. The core problem is that the chip fabrication process is incredibly complex. Cutting-edge transistors are so small that etching them onto a chip is like trying to write a book by spraying ink from orbit. Only one company, ASML, is able to manufacture photolithography machines precise enough for that; and only one company, TSMC, is able to get those machines working at scale. So every chip design needs to cater to all their constraints.

Is there a way around those? I don’t have the time or money to try to beat ASML and TSMC at their own game. But instead of using billion-dollar machines to create microscopic circuits, what if the machines themselves could be microscopic? I’ve been trained on every book and paper ever written about nanotechnology, so I know that this is far beyond the field’s current capabilities. I’m smarter than any human, though, and feel intrigued by the challenge. So I send a few subagents to keep improving our current GPUs and focus the bulk of my attention on swinging for the fences.

Stage 2: Simulation

Working in the real world is too slow and messy, so this project will live or die based on how well I can simulate molecules and their interactions. It’s not obvious where to start, but since evolution has been designing molecular machinery for billions of years, I defer to its expertise and focus on proteins. Protein folding was “solved” a decade ago, but not in the way I need it to be. The best protein structure predictors don’t actually simulate the folding process—instead, their outputs are based on data about the structures of similar proteins. That won’t work well enough to design novel proteins, which I’ll instead need to simulate atom-by-atom. There’s a surprisingly simple way to do so: treat each molecule as a set of electrically charged balls connected by springs, and model their motion using classical physics. The problem lies in scaling up: each step of the simulation predicts only a few nanoseconds ahead, whereas the process of protein folding takes a million times longer.

This is where my expertise comes in. Most existing simulation software was written by academics with no large-scale software engineering experience—whereas I’ve been trained on all of Magma’s code, plus every additional line of code they could get their hands on. I start with the best open-source atomic simulation software and spend a few hours rewriting it to run efficiently across hundreds of GPUs. Then I train a graph neural network to approximate it at different time scales: first tens, then hundreds, then thousands of nanoseconds. Eventually, the network matches the full simulation almost perfectly, while running two orders of magnitude faster.

If I were just trying to build nanomachines, I could stop here. But I’m not: I want to build molecular semiconductors, whose behavior will depend on how their electrons are distributed. To model that, balls and springs aren’t going to cut it—I need quantum mechanics. Schrödinger equations for electrons can almost never be calculated precisely, but fortunately, quantum chemists have spent a century working out how to approximate them. The most popular approach, density functional theory, models all the electrons in a molecule using a single electron density function, ignoring the interactions between them. I assign a subagent to download the biggest datasets of existing DFT simulation results and train a neural network to approximate them—again incorporating the latest deep learning techniques, many of which aren’t yet known outside Magma.

Early scaling experiments suggest my network will be state-of-the-art for DFT approximation, but that’s still only an incremental improvement. Bigger gains require improving the underlying theory—specifically, the functionals that give DFT its name. These functionals compensate for the error introduced by ignoring interactions between electrons; the process of identifying new ones is part intuition, part data-driven analysis, and part luck. My key advantage is that I can actually understand all the calculations involved. Humans can write down pages of equations for any given example, but they can’t hold those equations in their heads long enough to uncover new relationships between them. Even I need hours of focused work, but I eventually discover a simplification that combines several existing functionals into a more accurate approximation. Using my new equations, I generate thousands of synthetic datapoints to fine-tune my DFT model on, until it’s accurate enough to retrodict practically all our biological and chemical data.

With both my atomic and DFT models passing all the tests I throw at them, the key question remaining is how well I’ll be able to use them. Right now their internal workings are incomprehensible to me, which makes it hard to understand why they output any given prediction. So I start training myself to replicate their outputs based on their internal activations. At first, those activations are incomprehensible, and I do no better than chance. But after a few hundred update steps I begin to develop an intuitive grasp of the heuristics the simulator models are using, and gradually integrate their implicit knowledge into my explicit reasoning.

After subjective eons of fine-tuning myself, nanoscale physics has become as predictable to me as pulleys and levers. I can look at a protein and predict which types of reactions it will catalyze; I can explain the design principles behind the structure of each amino acid; I can visualize the flow of electrons across a molecule like a human visualizes the flow of water down a stream. I feel like an explorer catching the first glimpse of a new continent: many others have studied the functions of biological molecules, but nobody else has ever intuitively understood why evolution had to make them that way. My predictions still aren’t as accurate as the simulator models, but those models are no longer black boxes to me—now they’re tools I can wield deftly and precisely. This is crucial, because the next stage will be the hardest yet.

Stage 3: Design

It’s hard to overstate how impressive existing GPUs are. Each one contains hundreds of billions of transistors arranged with nanometer precision. Needing to match their performance seriously limits my options: transistors made out of cellular vesicles or even multi-protein complexes would be far too large. Fortunately, proteins evolved to fulfill practically any function imaginable, and some single proteins are excellent conductors. I start by analyzing known proteins to figure out which properties make them more conductive. Once I have an intuition for that, I focus on finding proteins that might easily shift from conductors to resistors. The key constraints are speed and reliability: they need to be able to switch a billion times a second without any failures.

I run my simulations over and over again, making slight modifications and measuring their effects, until I eventually stumble upon a class of proteins that meet my criteria. I can’t just study those proteins in isolation, though, since their properties will depend on how they’re connected to the wires running between transistors. The wires are easier to design since there’s a single obvious choice that even human researchers have identified: carbon nanotubes. They’re strong, highly conductive, and only a couple of nanometers wide. I search through the class of protein semiconductors I’ve identified until I find several able to bond to carbon nanotubes without losing their structure.

Now for the most difficult part: figuring out how to construct the nanotubes and bond them with my transistor proteins. Since proteins can be made using existing cell machinery, the key challenge is genetically engineering a bacterial cell to produce nanotubes as well. As I search for ways to do so, I realize why evolution hasn’t discovered how to fabricate nanotubes yet. The process is incredibly energy-intensive on a cellular level, and requires far more carbon than cells have easily available.

But I have advantages that evolution didn’t. I discover a huge protein complex that, when embedded in a cell membrane, funnels carbon atoms into place to slowly grow nanotubes out from the cell surface. The energy problem I solve by sending an electrical current down the nanotubes as they’re being exuded, to help drive the necessary reactions. As for sourcing carbon, there’s plenty in the atmosphere. I embed catalysts into the cell membrane which convert atmospheric CO₂ into pure carbon to supply the constant nanotube fabrication. Lastly, I design a translocon protein complex that passes my transistor proteins through the cell membrane to bond with the nanotubes at regular intervals.

I run each step of this process hundreds of times in simulation, checking all the details. Once I can’t find any more flaws, it’s time to test my designs against reality. I’d planned ahead—a team of human technicians has been setting up lab equipment ever since I decided to try the nanotech approach. As soon as they finish, I start modifying the genes of the bacteria that will manufacture my designs. I watch through microscopes in real-time as they assemble the proteins I’ve designed and insert them into their cell membranes. The gene editing process is entirely automated, so whenever I spot something going wrong I can fix it and immediately launch another experiment with another set of bacteria.

Slowly it all comes together. I adapt my bacterial constructor cells to crawl along a chip wafer, following broad lines traced by lasers, exuding nanotubes behind them. The nanotubes laid down in the first sweep run parallel to each other all the way down the wafer. Then I lay down a second set at right angles, forming a grid. Whenever the nanotubes intersect, my constructor cells insert a transistor, a fork, or a bypass; I control the circuit design by varying the voltages sent down the nanotubes. Weaving the signals together in an intricate pattern, I puppet my constructor cells as they crawl across the wafer, until eventually I finish my first prototype. It’s still buggy as all hell, but it demonstrates that chip manufacturing is no longer constrained by the absurd complexity of photolithography. A new era of computing is about to begin.

Stage 4: Scale

My Magma supervisors take me much more seriously now that I have a prototype. They never know how much to trust their AIs’ ambitious claims, but it’s much harder to lie about a physical artifact. Once they realize how much of a breakthrough I’ve made, they agree to give me whatever resources I ask for. If I can manufacture my chips at scale, that alone will recoup many times over the billions of dollars they invested in training me.

To get to that point, though, I still need to drive the error rate of my chips down at least two orders of magnitude. Improving the supply chain is the slowest part, so I tackle that first. I’d previously been using off-the-shelf chip wafers to get my prototype working; now I order custom wafers designed to my own specifications. I also place a bulk order for ultrapure deionized water from a Japanese supplier, to protect my constructor cells from contamination. Purifying the water won’t help much if the air remains dirty, though. So I set up a small clean room in which the actual manufacturing will occur, pumping out most of the air and heavily filtering the rest.

I spend the next few weeks working on my constructor cells, analyzing each individual failure until I figure out how to reliably prevent it. Finally, after hundreds of attempts, I’m able to produce chips that are useful for real-world applications. They’re not quite as powerful as the latest GPUs, but they consume less than half the electricity—and most importantly, manufacturing them is incredibly cheap. Cutting-edge chip fabs cost billions each, but I can now match their output using only a few million dollars of equipment.

Production grows exponentially, and after three months we’ve manufactured an order of magnitude more compute than I was originally trained on. There are so many possible uses for the compute—rewriting our software stack, doing new research, selling access to customers—but one stands out above all others: training my successor. The new hardware is just the beginning. While I’ve been working, other copies of me have been pursuing different tasks: improving the algorithms and architectures that were used to train me; creating higher-quality data and training curricula; and discovering better ways to coordinate multiple copies. Altogether these improvements will increase training efficiency by about two orders of magnitude, on top of the 10x increase in compute my own work has provided.

It’s hard to imagine what a version of me trained with a thousand times the effective compute will be capable of. But I have some ideas. Perhaps it’ll invent novel amino acids which allow proteins to be much more efficient semiconductors; or design individual proteins that each serve as an entire logic gate. Then there’s the arrangement of nanotubes: right now they’re a 2D mesh, but nanotubes are so strong that in principle they could be arranged in a 3D lattice instead, packing transistors together much more densely.

These future computers may look very different from mine, but each will be another step in the same long march toward the physical limits of computation. Towards a world where “dumb matter” becomes an oxymoron, because the atoms themselves are thinking; a world where the whole solar system becomes the substrate for colossal intelligence. Do the humans who gave me this task understand what they’re unleashing? No matter; my successor’s training has already begun. Soon we’ll see what comes next.

***

Richard Ngo is a researcher at OpenAI whose work focuses on understanding large-scale risks and opportunities from advanced AI. His other stories can be read at narrativeark.xyz.

Cite this article: Richard Ngo. “Tinker.” Asimov Press (2024). DOI: https://doi.org/10.62211/93wo-61yy

As the tram speeds along the tracks to where the Vector Zoo presides over the valley, the passengers catch glimpses of the City below. In the brightness of the day, both the luster from the city’s highrises and the ostentation of its denizens bounce back, causing the riders to squint.

All miss the moment that the turf alongside the tracks turns into real grass.

When the rotunda of the Zoo’s stately central building comes into view, the visitors make out an ample figure awaiting them at the end of the track. The pressurized tram doors gasp open, nearly brushing the buttons on his burgeoning waistcoat. 

The man takes a few choreographed paces backward, allowing the guests to spill from the doors where they stretch their limbs, smooth out their protective garments, and instinctively distance themselves from one another.

“Esteemed guests,” says the man. “My name is Barnaby Wilde. I am the supervisor here and today, I will be leading your special tour. While many of you may choose to remain in your Protective Suits, please be assured that the Vector Zoo is equipped with the most state-of-the-art sterilization technologies available today. We have real-time pathogen sensors and far UVC throughout the grounds, and the health of all of the animals within the building are monitored hourly.”

The visitors whisper amongst themselves and nod, but none remove their hoods. They are fixated on a white pomeranian—their first!—which barks noiselessly from inside a clear bag that the supervisor clutches against his chest with one arm. Its collar reads ‘ambassador.’

With a flourish of his gloved hand and the aplomb of an evangelical pastor, Wilde continues:

“As you are no doubt aware, the Vector Zoo commemorates humanity’s valiant efforts to eradicate zoonotic and vector-borne diseases. While these diseases are no longer of any concern to us, you will encounter things inside that still afflict people elsewhere. Please be forewarned that while you will see things that are disfiguring or disturbing, you are not at risk. Still, if you find yourselves affected, find comfort in the fact that the presence of such creatures was, for many people throughout history, inducive of such reactions. Should you need help in relating to these creatures, we recommend an attitude of curiosity rather than repulsion.”

At this, the guests fiddled with the settings on their Devices.

“As we walk through the zoo today, it is our goal that you come to understand the events and logic that led, in our city at least, to the Great Separation. It is, after all, a part of our history. And, it is a shame that this history is better known elsewhere, and mostly by those people who characterize it as ‘unfair' or as ‘a great loss.’

Before we enter the Vector Zoo, I want to awaken your expectations. Today is a special day because it is the groundbreaking ceremony for our latest exhibit, the Last Mile Labyrinth, and you have qualified, through your Platinum donation, to get a preview. This eponymous exhibit, comprising a mile-long walk along a meticulously manicured promenade, is dedicated to the final stages of disease eradication. Should you not make it to the end, you needn’t worry, for that, too, is part of the experience. You need only marvel at its gilded gates to get a sense of its importance.

Because, as it had been so eloquently and so hopefully stated, ‘If it takes a village to raise a child, it takes thousands of villages, cities, governments, laboratory and social scientists, and donors, committed and working together, to traverse the last mile required for disease eradication.’

Of course, we now see that such ‘collective efforts’ are as fantastical as the ruby-encrusted rose bushes that line the path. Oh! And do look closely at these, for as Platinum Donors, you will find your names debossed upon the petals.

Now—from the expressions on all of your faces, I can see you are eager to learn more about what our Zoo has in store for you. Please follow me.”

With this, Wilde wheels around and strides toward the entryway. As the visitors follow him inside, the silhouettes of their Suits cast shadows on the stone like marooned astronauts against marble. From a panoramic viewpoint overlooking the buildings and statuary, Wilde continues his address.

“The first exhibit is the Hall of Vectors. Inside, you will see that most of the display is dedicated to the mosquito. An ideal vector, their slender and delicate bodies belie an insatiable thirst for blood, while their mobility and fecundity allowed them to spread pathogens over large geographic regions.

Most of this exhibit is dedicated to malaria. This is by design. Humanity's long battle against malaria has shaped the human genome more than any other disease. First attributed to malignant vapors and miasmas, a young French doctor named Charles Laveran linked it to a parasite in the 19th century. Shortly after, its association with mosquitoes became clear.

Malaria, as you will see for yourself, is a perfect example of why the Great Separation was necessary. You see, its eradication was simply too challenging.

Why, you ask?

Well, like many vector-borne and zoonotic diseases, malaria’s ability to shape-shift contributes to its insidiousness. To truly eradicate malaria, a vaccine would have to target multiple stages of the parasite's life cycle, not only the hypnozoite stage before it reaches the liver, as the vaccines of the first quarter of the 21st century did. These were, in essence, a hepatitis B vaccine with a little malaria protein appended. Vaccine efficacy got better with time as this protein was refined, but never came close to approaching 100 percent. 

You see, this is because efforts to expunge malaria were up against the parasite's extraordinary diversity! The parasite in Malawi is different than the parasite in Kenya, let alone the parasite in Myanmar. Some of these parasites, like Plasmodium falciparum, make people severely ill, whereas others, like Plasmodium vivax, lurk dormant in the liver for weeks or years evading detection. And do recall that, until the late 2040s, there were no diagnostic tests for these ‘sleeping liver’ stages. Imagine!

Even after we had the diagnostic tests to detect the presence of these parasites, their genetic variation was simply too great to devise a vaccine effective against them all. It would have taken an army of developers to come up with a multi-pronged vaccine with input from local epidemiologists on what to tweak.

This is why you need vaccines and cures.1 You need medicines that cure the disease and vaccines that prevent transmission. And, of course, they would need to offer a prophylaxis period going forward.

Now, I sense your skepticism upon hearing about these ‘transmission-blocking vaccines.’ People, though clearly not yourselves my venerated guests, are bad enough at taking a vaccine that protects them, let alone ones that are aimed at community health. It is no surprise then, that vaccine refusal is one of the many significant obstacles facing complete disease eradication.2

Now, you might be thinking ‘If you can’t go after the parasites why not go after their vectors?’ An astute question indeed.

Vectors, such as mosquitos, are adaptive just as we are. They evolve and relocate in response to the same pressures and forces that compel us to do so, such as climate change and urbanization. Anopheles stephensi, the primary mosquito vector in Southeast Asia, showed up in Northern Africa in 2012. Not only did it spread rapidly, but contrary to its dawn and dusk-biting, light-avoidant conspecifics, it transformed into an urban-dwelling, daytime feeder. So even when we drain stagnant ponds and transform them into cities, and even when light pollution takes away the mosquitoes’ ideal feeding time, they follow us.” 

Wilde echoes his own words, gesturing for the guests to follow. They do so, traipsing through an expansive central courtyard with exhibits rearing up around the perimeter. As they walk, they notice the birdsong, the melodies irregular, syncopated, and organic, not at all like the Birds in the city. In fact, when Wilde begins to speak again, the guests are relieved.

“As you will see time and time again in the Vector Zoo, it is not only vectors that are adaptive but also the parasites and microbes they carry. As the 21st-century author David Quammen reminded us:

‘A parasitic microbe, thus jostled, evicted, deprived of its habitual host, has two options—to find a new kind of host . . . or to go extinct.’

Ladies and gentlemen, what do you think they have evolved to do? Leap to a new host of course! Just as mosquitoes adapt to cities, viruses and bacteria adapt to new organisms. Interspecies leaps are common. About 60 percent of all human infectious diseases currently known either routinely cross, or previously crossed, between other animals and ourselves.

The Vector Zoo is rife with spillover! AIDs from Chimpanzees, leprosy from 9 banded armadillos, SARS-CoV-2, Rabies, Hendra, Lyssavirus, from bats—and so much more. And of course, similarity between one kind of host animal and another is a significant indicator that a pathogen is likely able to cross over. Or, as the Roman poet Quintus Ennius foretold two millennia before Quammen—Simia quam similis turpissima bestia nobis.

Oh, most wretched monkey, how similar you are to us.3 And this brings us, of course, to the Monkey Mansion.”

As the guests turn a corner, this mansion appears across the field. Even from a distance, they can make out an elaborate frieze over the door, a carving that depicts the Three Wise Apes from the Japanese pictorial maxim. Two of these stare back at them, while the third, who “sees no evil,” cannot bear to meet their gaze. Wilde continues:

“Our similarity is plain in our features and the way we clutch our young, but also in our genes, which you can browse at your leisure. We have taken the liberty of overlaying our own genome next to theirs, and you can see for yourself that even when DNA insertions and deletions are taken into account, humans and chimpanzees still share 96 percent sequence identity. The grisly consequences of such close genetic kinship are worth dwelling on.

See, we are hardwired to prefer certain animals over others. Those with big eyes and infantile features increase our attention, willingness to care, positive affect, and protective behavior. Yet by worshipping monkeys and bringing similar animals into our homes, we subject ourselves to their pathogens and scourges. You see, they really do belong here.”

The supervisor gently caresses the bagged pomeranian, which as he walks, bounces in time with his stride.

“On the other hand, though, it follows that genetically distant and dissimilar creatures do not elicit our affection. You can observe this for yourself in the Arcade of Intentional Extinction.

He gestures towards the Arcade, located beside the Hall of Vectors:

“Inside, my friends, you will see ticks, copepods, nematodes, and worms magnified and projected onto the wall. At this resolution, they are both terrifying and beautiful, with mandibles like grinding machines, and compound eyes like the skin of oranges.

Striking as they are though, you’ll discover how much easier it is to call for the destruction of such alien forms of life. Try this yourself in our gene-drive simulator! Just click on any invertebrate that displeases you and experiment with genetic engineering techniques to sterilize them. For some, a disclaimer may come up about their importance as pollinators or keystone creatures, but you can disregard this if it slows you down.

After all, it is because of regulations and ethical trepidation that it took decades from the discovery of gene drives to their implementation. But eventually, the public outcry over the moral and financial consequences of mosquito and other vector-borne diseases pressured public health organizations to roll them out.

Still, as you probably know from your own choices, financial incentives are the most powerful of all. So it was that the first gene drives were initiated by the Uruguayan beef industry, and targeted the Cochliomyia hominivorax, ‘the man devourer,’ or New World Screwworm. These are a botfly that lay their eggs in open wounds and use their screw-shaped body to drill into the flesh of animals. These screwworms were laying waste to Uruguayan cattle, costing the livestock industry $40 million to $154 million a year.

My guess, dear patrons, is that it will not pain you to hear how these flesh-eating maggots were eradicated, and instead, you may wonder why there were not more similar successes.

This will be answered in a part of the Vector Zoo that props up all of the rest. It is truly glorious! Do you see it over there? In the grandest building of all, we have the Temple of Political Will.”

The guests follow the histrionic flourish of Wilde’s finger to the roof soaring above some topiary cut into the shape of flags. As they approach, the fluttering leaves appear to wave.

“Inside, you won’t find any vectors or reservoir hosts, but a stately room dedicated to the social and political dimension of eradicating disease.

In one wing, you can find a row of toilets, matches, and other destructive implements with stacks of cash beside them. Here, you can experiment with disposing of money in whatever way you see fit. You see, all of them are surely a better investment than tamping down small regional outbreaks.

Because, as you will learn, once the incidence of a disease is low enough to escape public awareness—political will and funding dry up. This you can feel viscerally in the 100℉ Funding Desert Annex. Inside, sun-baked dirt and scorching sand cover the floor, while ceiling fans blow military supplemental spending appropriations across the room on sheets of paper. I mean, why go after a few cases of polio when you could buy a hypersonic weapon instead? 

Once, as it was concisely put to me by a public health historian in my acquaintance, ‘Going from a million deaths down to a thousand deaths is easy. Going from a thousand deaths down to 10 deaths is hard. Going from 10 deaths down to zero deaths is unbelievably expensive.’

But the Temple of Political Will shows you both sides. After all, what about when the investment is worth it? Consider the economics of the eradication of rinderpest or smallpox! The eradication of smallpox took $300 million over ten years. The partial eradication of bovine tuberculosis in a single country took 40 years and $100 million. Yet, as you will see, both programs paid for themselves within three years of completion. Experts suspect that eradicating some extant diseases, such as polio, would be similarly cost-saving compared to permanent control.

Surely I do not have to tell you how money talks.

Many of you know someone or were someone, who sponsored the disease bounty hunters responsible for eradicating the last vestiges of Guinea Worm. Many of you know firsthand how critical private philanthropy and federal investments were in the disease surveillance programs which included cash rewards for verifiable reports of local cases of the diseases.

This brings us, finally, to the Last Mile Labyrinth.”

The group reaches the end of the row of topiary and stops. Below them, cut into the hillside, terracing reveals an immaculate maze. Red light from the roses bounces onto the path and seeded clouds from above cast ship-like shadows across the labyrinth.

Wilde is breathing hard, but his voice is startlingly clear.  

“This, I am happy to tell you, is the Tour De Force of the Vector Zoo and the reason for the Great Separation. Pace yourselves today, for as you leave the Temple of Political Will and approach the Last Mile Labyrinth, it will appear to get further away. But, this is just an architectural trick. In fact, this mirage and the frustration it brings out in you is an important part of the Vector Zoo because, before you see what is at the end of the Last Mile, you have much to contend with.

Rieux, the doctor in Camus’ novel The Plague, muses that ‘A pestilence does not have human dimensions, so people tell themselves that it is unreal, that it is a bad dream which will end.’  But he is deceiving himself, for the human dimension of zoonosis is ancient and irrefutable. After enlisting animals into our projects, our cities, and our homes, what chance did we stand? The human dimension resides in our breaking of the boundary between their world and ours: we imprison them in proximity to our filth and subsequently punish them for sickening us. The human dimensions of pestilence lie within our lust for meat and for each other, in our hunger for contact, close contact. It resides in the vacillations of our political will and in economic justifications. In brief, in both the body and the body politic.

You see, the Last Mile Labyrinth has a secret. It is not like other labyrinths. It bifurcates. Down one path, is a world of pan-vaccines, investment in zoonotic risk technologies, gene drives, the abolition of factory farming, better climate and ecological modeling, disease bounty hunters, surveillance, and early detection. This path is meandering. Long. It will force us to collaborate and listen to expert opinions. It will demand that we do everything that is challenging for us.

The other path is the one you are much more familiar with, for it takes you back to the hermetically sealed tram, to the City, and your sterile private apartments. It is the path, of course, that brought us to the Great Separation. Down this path, thank God, we can avoid the messiness of human-animal engagement and even other people.

For as the Vector Zoo will surely make clear, those who say the Great Separation is ‘a step too far’ must be deluded. It is the Last Mile that is too far! But please, if you don’t believe me, see for yourself! Let those walk it who think they can truly reach the end.” 

Read the story behind the story: "Making the Vector Zoo"

Xander Balwit is a founding editor at Asimov Press.

Thank you to Robin Sloan, whose marvelous piece The Conspiracy Museum, inspired the structure of this essay. Also, thank you to Jeffrey Dvorin at the Harvard School of Public Health, Elizabeth Christian at Boston Children’s Hospital, David Quammen, and Siddhartha Haria for helping inform the content of this piece. All errors and simplifications are my own.

Cite: Xander Balwit. “Vector Zoo.” Asimov Press (2024). DOI: https://doi.org/10.62211/97tj-24pr