🧠The Week AI Started to Build, Heal, and Think for Itself
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📅 Tuesday, October 28, 2025 | Your 3-minute shortcut to $1B deeptech plays
My Note
Every week I dive into patents, lab notes, and research that rarely gets headlines.
And this week, I noticed a shift in how intelligence itself is starting to take physical form.
AI is no longer just digital.
It’s becoming something we can touch, print, and even heal through.
We’re witnessing the rise of living systems where biology, materials, and computation merge into one seamless process.
Think about it:
Machines are learning to build like nature.
Materials are learning to heal like bodies.
And neurons (both artificial and biological) are starting to think together.
That’s not the future of AI, that’s the future of intelligence itself.
This week’s signals show that transformation in motion:
Bones that regenerate themselves through 3D-printed bio-glass.
Drones that build autonomously, turning construction into code.
Neurons that think and adapt, merging silicon and biology.
(Bonus) And the rise of synthetic data, the invisible fuel keeping every new model alive.
Together, they point to one powerful truth:
The next wave of innovation won’t be about faster models it’ll be about systems that build, heal, and learn on their own. Not through lines of code but through matter, motion, and biology.
Let’s decode it 👇
Signal #1 | 3D-Printed Bio-Glass Could Replace Bone Grafts
Here’s what’s happening:
Researchers have developed a new form of 3D-printed bio-glass that acts like living bone, encouraging cells to regrow naturally around it. Unlike titanium implants, this material fuses seamlessly with tissue and disappears as the body heals.
Why it matters:
Bone grafts are among the most common surgeries on Earth. Instead of borrowing or replacing bone, surgeons could soon print it : customized, sterile, and regenerative.
For investors: this marks the beginning of biofabrication-as-a-service, a category where biology meets advanced manufacturing. Watch for university spinouts and medtech labs turning regenerative materials into scalable products.
The winners will be the ones who own the supply chain for living materials (bioglass powders, regenerative inks, and microfluidic printers).
Start by mapping where bioprinting meets clinical manufacturing, because those platforms will become the “chip fabs” of medtech.
For founders:
This is your signal to move beyond hardware. The opportunity isn’t in the 3D printer itself,, it’s in the biochemical recipes that make tissue growth programmable.
If you’re in materials or healthtech, explore partnerships between bioprinting startups and surgical robotics.
The edge will belong to those who can deliver custom implants that don’t just fit the patient (they heal with the patient).
For builders:
This is the start of a new kind of engineering : tissue mechanics. Designing with living systems means thinking like nature: adaptive, iterative, and self-repairing.
If you’re a materials scientist, roboticist, or biomedical engineer, this is where you’ll make history by merging precision engineering with biological intelligence.
What I’d do:
Track collaborations between EPFL, MIT, and Mayo Clinic, and the startups emerging around their IP portfolios. Once this tech enters human trials, expect a gold rush in regenerative surgery.
Signal #2 | AI-Powered Drones Are Building the Impossible
Here’s what’s happening:
A team of engineers has built AI-driven drones that can 3D print and assemble structures in hard-to-reach zones ( disaster sites, bridges, offshore rigs, and even space). They use real-time computer vision and reinforcement learning to work together autonomously, without human guidance.
Why it matters:
We automated data. Then we automated driving.
Now, we’re automating construction itself.
That means we’re turning buildings into software: scalable, adaptive and location-free. Infrastructure that build themselves faster, safer, and at lower cost.
For investors:
This is the rise of the AI + Infrastructure category which is a $10T global industry ready for disruption. Expect an investment race between construction firms, defense contractors, and energy players.
The key opportunity will be infrastructure automation platforms which are companies building the “OS for physical autonomy.”
Once a major construction firm announces adoption, this tech will move from prototype to procurement overnight.
For founders:
This is your cue to go modular.
Think swarm logic, plug-and-play components, and autonomous coordination.
If you can build drones that collaborate, not just operate, you’re at the start of a new industrial revolution.
Consider niches like post-disaster building, orbital maintenance, or remote energy assembly (where autonomy isn’t a luxury but a necessity).
For builders:
The future of architecture is algorithmic. If you’re in robotics, AI, or structural design, this is the moment to merge your disciplines.
Learn how to design for autonomy: materials that self-align, drones that sense stress points, algorithms that adjust blueprints mid-flight.
You’re not just building buildings, you’re building systems that build themselves.
What I’d do:
Track which companies partner with construction robotics leaders like ICON or Boston Dynamics. The first public demonstration of a fully autonomous build will be the key for a brand-new industry.
Signal #3 | Hybrid Biological-Electronic Neurons That Think Like Us
Here’s what’s happening:
Scientists have created hybrid neurons: a fusion of living brain cells and electronic circuits that can process and send information like biological neurons. They “fire,” learn, and adapt (but can also connect to silicon systems directly).
Why it matters :
For decades, we’ve tried to simulate the brain.
Now, we’re starting to merge with it.
This fusion bridges organic thought and digital precision, opening doors for brain-machine interfaces, prosthetics, and biocomputers.
For investors:
This is the opening chapter of a succulent industry: hybrid intelligence systems.
The market will grow around neural hardware, living processors, and bio-interface software.
The long-term play? Whoever owns the data layer of neural translation (how biological signals become computational ones) will dominate.
Track early IP filings from ETH Zürich, CNRS, and MIT, this is where today’s research will become tomorrow’s market cap.
For founders:
You’re standing at the frontier of computing itself.
Startups that can “translate” between biology and code will define this decade.
Think real-time neural decoders, biological signal analytics, or living sensors that evolve.
You don’t need to build full biocomputers yet. Start small by building bridges between biology and AI systems. That’s where commercial traction begins.
For builders:
This is the most human kind of engineering : designing systems that learn like us.
If you’re a hardware builder, study bioelectrical interfaces.
If you’re a coder, explore neural signal processing (how spikes become meaning).
This field will need engineers fluent in both circuits and cells.
What I’d do:
Keep your radar on collaborations between neurotech labs and chipmakers like Intel, NVIDIA, and imec. When hybrid neurons achieve stable memory or pattern recognition, we’ll enter the biocomputing era : where “learning” happens inside living matter.
Case Study | The Rise of Synthetic Data
Guest Contributor: Arnav Sambhare
(Edited by Invest Deeptech)
The Big Picture
AI isn’t just limited by chips, it’s running out of good data. Today there are around 300 trillion pieces of public data available to train AI models, but experts say the world’s supply of clean, open data will run out by 2028. That’s where synthetic data comes in: fake but realistic data created by AI to train other AIs.
The Case
Synthetic data works like this: an AI studies real examples, learns the patterns, and then generates infinite new, similar data. It’s faster, cheaper, and protects privacy. It also helps fill in rare or missing cases that real data doesn’t cover (for instance, in medical, robotic or industrial training scenarios). Big players are already moving:
NVIDIA bought Gretel AI
Tonic AI partnered with Google Cloud
And KPMG acquired YData (all to boost their synthetic data tech).
Why It Matters
Synthetic data solves three big problems at once: privacy, cost, and scalability. It’s also aligned by the EU AI Act, which encourages it as a safer way to train large models. And it starts to level the playing field: smaller startups can now compete with Big Tech, who used to control all the data. But it’s not perfect. Synthetic data can only copy what’s already known, not discover something completely new.
Takeaway
Synthetic data is quietly becoming one of the most powerful tools behind AI’s hidden infrastructure. It fuels the next wave of model training, testing and fine-tuning (faster safer, and cheaper).
Watch startups creating specialized synthetic data for fields like health, robotics, and finance.
Look for companies improving data quality and AI testing.
Expect faster, cheaper training for AI models and new winners emerging outside Big Tech.
💡 Signal: As data becomes infinite, quality matters more than quantity. Truth becomes scarce. And knowing what’s real (or fake) just became the new wealth skill.
My Lens
This week’s signals tell a single story:
AI is leaving the screen and entering the real world.
We’re printing bone that heals, building drones that construct, and designing neurons that merge mind and machine.
Meanwhile, synthetic data is quietly becoming the oxygen that keeps these systems learning.
The future of intelligence won’t live in code, it’ll live in matter.
And the next frontier will belong to those who can build bridges between computation, biology, and materials.
Because the deeper you look, the clearer it gets:
we’re not just programming machines, we’re programming reality.
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See you next Tuesday!
(New foresight is already forming).
—
Eden Djanashvili
Author, Invest Deeptech
🛑 This newsletter is for informational purposes only. Not financial advice.
Couldn't agree more, it's genuinly fascinating to see intelligence moving beyond pure computation to material systems that heal and build, which makes me think our future code reviews might involve checking for biological compatibility.