Ryan Terrell

Father of two loud boys. Tech, science, electronic music geek.

[ₕₐₘₚₜₒₙ @hamptonism]

This is who you’re arguing against online:

[VentureBeat @VentureBeat]

OpenAI just confirmed what security researchers have said since 2022: prompt injection is permanent. Our survey of 100 tech decision-makers found 65% of enterprises still have no dedicated defense.
https://t.co/du8JNADnoH

[Aakash Gupta @aakashgupta]

The entire robotics industry is about to compress a decade of progress into 18 months, and nobody’s pricing it in.

The hardware has been ready for years. Boston Dynamics had Atlas doing backflips in 2018. The bottleneck was never motors or actuators. It was that every robot behavior had to be hand-coded. Pick up a box? That’s one program. Pick up a bottle? Different program. Move the box from shelf A to shelf B in a warehouse with slightly different lighting? Start over.

Foundation models broke this completely.

Before VLAs, teaching a robot one skill gave you exactly one skill. Zero compounding. Zero transfer. A robot trained to fold shirts couldn’t fold towels without starting from scratch. The labor intensity of data generation meant robotics datasets stayed narrow, robots overfit, and small variations like object weight or table height caused failures.

Now a single Gemini Robotics model handles tasks it has never seen in training. Google’s On-Device model learns new behaviors with 50-100 demonstrations. Not 50,000. Fifty. That’s a 1000x reduction in the data requirement for new capabilities.

The speed implications cascade through everything.

First order: deployment timelines collapse. What took robotics teams 6-12 months of custom programming now takes days of fine-tuning. Second order: the addressable market explodes. Tasks that were never economical to automate suddenly are, because the integration cost dropped by orders of magnitude. Third order: the data flywheel accelerates. Every robot running Gemini Robotics feeds learning back into the foundation model. More deployments means faster improvement means more deployments.

Physical Intelligence raised at $2.4B because investors finally understood this. Boston Dynamics partnered with Toyota Research Institute to bolt Large Behavior Models onto Atlas. Every humanoid company is scrambling to either build or license the intelligence layer they don’t have.

The market is still valuing robotics companies on their hardware differentiation. But hardware is commoditizing. Boston Dynamics spent a decade perfecting locomotion, and now that’s table stakes. The value is migrating entirely to whoever owns the foundation model that generalizes across embodiments.

Google trained Gemini on the largest multimodal corpus ever assembled. Then they added physical actions as an output modality. That’s not a robotics company bolting on AI. That’s an AI company whose models now output motor commands.

The companies pricing this correctly are building around foundation model access, not around proprietary hardware. The companies pricing this wrong are still acting like the moat is in the mechanical engineering.

AGI moving into the physical world isn’t a 10-year prediction. Gemini Robotics shipped in March. The 1.5 version with chain-of-thought reasoning shipped in September. They’re iterating on a 6-month release cycle while hardware companies iterate on 3-year cycles.

The gap between software intelligence timelines and hardware development timelines is the entire trade.

[Ryan Terrell @Ryan_Terrell_]

@lukas_m_ziegler It would be interesting to see a practical use case.

[SciTech Era @SciTechera]

The rise of biocomputing: computers that are alive

Scientists are blurring the lines between biology and technology by building "living computers" using human brain organoids.

Scientists are growing tiny clusters of human neurons (organoids) from stem cells. These organoids are placed on high density electrode grids that allow two way communication between living cells and silicon chips. By sending electrical pulses and monitoring the "firing" of neurons, researchers create a loop that allows the biological tissue to process data and learn tasks.

Recent breakthrough achievements?

> DishBrain (Cortical Labs): Researchers grew 800,000 neurons on a chip and taught them to play the video game Pong. Remarkably, the biological neurons learned to rally the paddle in just five minutes, faster than traditional AI at the time and used significantly less energy.

> Brainoware (Indiana University): Researchers trained an organoid to recognize speech. By using recorded feedback and electrical pulses, the system could distinguish between different speakers' voices with 78% accuracy within two days.

> MetaBOC (Tianjin University): In 2024, scientists introduced a wheeled robot controlled by a pea-sized organoid. The organoid processed sensor data and "steered" the robot, eventually learning to avoid obstacles.

Why Biology is Better than Silicon
Extreme Efficiency?

Biological neural networks learn with fewer trials and consume a tiny fraction of the energy required by traditional AI hardware.

These systems excel at adapting to unpredictable environments, mimicking the "natural intuition" of living organisms.

Three major areas where this tech could be revolutionary?

> Autonomous Cyborg Systems: Developing robots or drones that can adapt to unpredictable environments with the "natural intuition" of biological circuits while consuming a fraction of the energy of silicon-based AI.

> Brain Disease Modeling: Since they are made of human cells, these "brains on chips" can model Alzheimer’s, autism, or epilepsy far more accurately than animal models. They could also be used for personalized drug testing.

> Neural Repair (OBCI): The long-term goal is "Organoid Brain Computer Interfaces." Scientists hope to one day implant organoids into humans to repair brain damage from strokes or injuries, using chips to help the new tissue integrate with the host brain.

[Shining Science @ShiningScience]

Scientists may have just taken a giant leap toward interstellar navigation.

How? By bending, but not breaking, the rules of quantum physics.

In two groundbreaking studies, researchers developed new methods for dramatically improving atomic clocks, which are already accurate to within a second every 10 million years.

These clocks are essential for GPS, scientific research, and, potentially, future space travel. But until now, their precision has been limited by quantum “noise” and Heisenberg’s Uncertainty Principle, which restricts how precisely certain atomic properties can be measured.

The breakthroughs come from MIT and the University of Sydney. At MIT, physicists entangled ytterbium atoms with high-frequency laser light, doubling the precision of an ultra-stable optical atomic clock. Meanwhile, Australian researchers developed a technique that allows simultaneous measurement of both position and momentum—but only for tiny changes—effectively sidestepping quantum limits without violating them. This could revolutionize quantum sensing and allow for even more accurate timekeeping. Such advancements may someday support autonomous spacecraft navigation or unlock deeper understanding of dark matter, making these clocks not just tools of measurement, but keys to exploring the universe.

Source: Wells, S. (2025, November 6). Scientists Just Discovered a Quantum Physics Loophole—And It Could Finally Unlock Interstellar Travel. Popular Mechanics.

[Ryan Terrell @Ryan_Terrell_]

@elonmusk @grok All the models I've tried, including Grok can't get this one right: Playing on the cart before the horse metaphor, create a picture of a cart labeled marketing pulling a horse behind it named development.

[Underworld @underworldlive]

USA. Let’s dance?

https://t.co/GZ5QmAWUBe

[Ryan Terrell @Ryan_Terrell_]

Every time I make a Tri-tip my wife says it looks like we're having @genesimmons tongue for dinner. Might be a good special for @RockAndBrews

[Ryan Terrell @Ryan_Terrell_]

@CoinbaseSupport one again I’m kicked out of Coinbase despite just recently updating my password and saving it to keychain. No email or text received when I try to reset it. Always seems to happen on days there are big moves, right when you need in the most.

[Ryan Terrell @Ryan_Terrell_]

@TND Tell that to the 1000s of people who have eliminated the need for pharmaceuticals to treat inflammatory issues like arthritis. Not to mention far less serious side effects!

[Ryan Terrell @Ryan_Terrell_]

Our children are going to see some absolutly incredible things.

[Steve Jurvetson @FutureJurvetson]

The Moore's Law Update
NOTE: this is a semi-log graph, so a straight line is an exponential; each y-axis tick is 100x. This graph covers a 1,000,000,000,000,000,000,000x improvement in computation/$. Pause to let that sink in.

Humanity’s capacity to compute has compounded for as long as we can measure it, exogenous to the economy, and starting long before Intel co-founder Gordon Moore noticed a refraction of the longer-term trend in the belly of the fledgling semiconductor industry in 1965.

I have color coded it to show the transition among the integrated circuit architectures. You can see how the mantle of Moore's Law has transitioned most recently from the GPU (green dots) to the ASIC (yellow and orange dots), and the NVIDIA Hopper architecture itself is a transitionary species — from GPU to ASIC, with 8-bit performance optimized for AI models, the majority of new compute cycles.

There are thousands of invisible dots below the line, the frontier of humanity's capacity to compute (e.g., everything from Intel in the past 15 years). The computational frontier has shifted across many technology substrates over the past 128 years. Intel ceded leadership to NVIDIA 15 years ago, and further handoffs are inevitable.

Why the transition within the integrated circuit era? Intel lost to NVIDIA for neural networks because the fine-grained parallel compute architecture of a GPU maps better to the needs of deep learning. There is a poetic beauty to the computational similarity of a processor optimized for graphics processing and the computational needs of a sensory cortex, as commonly seen in the neural networks of 2014. A custom ASIC chip optimized for neural networks extends that trend to its inevitable future in the digital domain. Further advances are possible with analog in-memory compute, an even closer biomimicry of the human cortex. The best business planning assumption is that Moore’s Law, as depicted here, will continue for the next 20 years as it has for the past 128. (Note: the top right dot for Mythic is a prediction for 2026 showing the effect of a simple process shrink from an ancient 40nm process node)

----
For those unfamiliar with this chart, here is a more detailed description:

Moore's Law is both a prediction and an abstraction. It is commonly reported as a doubling of transistor density every 18 months. But this is not something the co-founder of Intel, Gordon Moore, has ever said. It is a nice blending of his two predictions; in 1965, he predicted an annual doubling of transistor counts in the most cost effective chip and revised it in 1975 to every 24 months. With a little hand waving, most reports attribute 18 months to Moore’s Law, but there is quite a bit of variability. The popular perception of Moore’s Law is that computer chips are compounding in their complexity at near constant per unit cost. This is one of the many abstractions of Moore’s Law, and it relates to the compounding of transistor density in two dimensions. Others relate to speed (the signals have less distance to travel) and computational power (speed x density).

Unless you work for a chip company and focus on fab-yield optimization, you do not care about transistor counts. Integrated circuit customers do not buy transistors. Consumers of technology purchase computational speed and data storage density. When recast in these terms, Moore’s Law is no longer a transistor-centric metric, and this abstraction allows for longer-term analysis.

What Moore observed in the belly of the early IC industry was a derivative metric, a refracted signal, from a longer-term trend, a trend that begs various philosophical questions and predicts mind-bending AI futures.

In the modern era of accelerating change in the tech industry, it is hard to find even five-year trends with any predictive value, let alone trends that span the centuries.

I would go further and assert that this is the most important graph ever conceived. A large and growing set of industries depends on continued exponential cost declines in computational power and storage density. Moore’s Law drives electronics, communications and computers and has become a primary driver in drug discovery, biotech and bioinformatics, medical imaging and diagnostics. As Moore’s Law crosses critical thresholds, a formerly lab science of trial and error experimentation becomes a simulation science, and the pace of progress accelerates dramatically, creating opportunities for new entrants in new industries. Consider the autonomous software stack for Tesla and SpaceX and the impact that is having on the automotive and aerospace sectors.

Every industry on our planet is going to become an information business. Consider agriculture. If you ask a farmer in 20 years’ time about how they compete, it will depend on how they use information — from satellite imagery driving robotic field optimization to the code in their seeds. It will have nothing to do with workmanship or labor. That will eventually percolate through every industry as IT innervates the economy.

Non-linear shifts in the marketplace are also essential for entrepreneurship and meaningful change. Technology’s exponential pace of progress has been the primary juggernaut of perpetual market disruption, spawning wave after wave of opportunities for new companies. Without disruption, entrepreneurs would not exist.

Moore’s Law is not just exogenous to the economy; it is why we have economic growth and an accelerating pace of progress. At Future Ventures, we see that in the growing diversity and global impact of the entrepreneurial ideas that we see each year — from automobiles and aerospace to energy and chemicals.

We live in interesting times, at the cusp of the frontiers of the unknown and breathtaking advances. But, it should always feel that way, engendering a perpetual sense of future shock.

[Ryan Terrell @Ryan_Terrell_]

@brian_armstrong Dear Brian,

Your customer experience is shockingly bad. I can't sign in after getting a new phone and have no possible way to contact support without signing in. None of the steps on your site work to authorize a new device.

[Ryan Terrell @Ryan_Terrell_]

Gee, why is our ad spend so high?
https://t.co/NkSwfb2Y2T

[Ryan Terrell @Ryan_Terrell_]

https://t.co/0xpr1QHhrn

[Ryan Terrell @Ryan_Terrell_]

https://t.co/My3StVlHaP

[Ryan Terrell @Ryan_Terrell_]

https://t.co/OQgVdREuN6

[Ryan Terrell @Ryan_Terrell_]

When they say "we're too busy, just set it up according to best practices" what they're really hoping for is this:

[Ryan Terrell @Ryan_Terrell_]

@chrisdancy Not to mention the best source of SPAM since email was invented.