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Compute is the New Capital: How AI Tokens Became the Valley’s Most Powerful Currency

If you want to know what the tech world really values right now, just look at what it’s measuring. We used to obsess over lines of code, and then it was all about daily active users and engagement metrics.

But lately, I’ve been watching a pretty profound shift taking place. The industry isn’t just optimizing for headcount or dollars anymore; it’s optimizing for tokens. For the first time, cognitive output has a measurable unit cost, and this little backend metric is quietly becoming the foundational currency of the digital economy.

I just published a new piece diving into how this “token economy” is actually playing out on the ground. Right now, we’re in this wild phase where folks are flexing their token burn rates, and massive players are even trading raw compute power for startup equity.

But this brute-force volume game is just the beginning. We’re moving toward a really fascinating future where AI agents will dynamically negotiate with each other over global compute exchanges, and companies will start managing their processing power like a true financial asset. It’s an entirely new way of thinking about how we build and scale.

Treating AI as just another flat-fee software subscription probably isn’t going to cut it for much longer. The organizations that really thrive in the next decade will be the ones who figure out how to navigate this new intersection of intelligence, energy, and scale.

I put together a deep dive into how compute is becoming the new capital, and what this macroeconomic shift actually means for the rest of us. I’d love to hear your take on it—check out the full post below.


Scientists Built Synthetic Self-perpetuating Brain For Robots

Further Reading.

Self-powered analogue neuromorphic system for multimodal sensing, encoding and learning with diffusive and drift memristors.
https://www.nature.com/articles/s4446

Reservoir Computing: Foundations, Advances, and Challenges Toward Neuromorphic Intelligence.
https://www.mdpi.com/2673-2688/7/2/70

Embodying physical computing into.
soft robots.
https://www.nature.com/articles/s4146

Artificial Nervous Systems.
https://pmc.ncbi.nlm.nih.gov/articles

Brain Cells Master Doom: Cortical Labs’ Biological Computer Reaches Major AI Milestone

Silicon Scoop

Flowgrammer.ca -> https://flowgrammer.ca/
AI News and Resources -> https://flowgrammers.ca.
OpenClaw Toronto -> https://openclawto.com/

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Startup Investor Drinks Toronto — https://startupdrinksto.com/
Toronto’s Startup Community — https://torontostarts.com/

Silicon Scoop.
Silicon Scoop Podcast — https://torontostarts.com/silicon-scoop/
Apple: https://podcasts.apple.com/us/podcast
Spotify: https://open.spotify.com/show/5bAzF0M
YouTube: • Silicon Scoop.

Startup Talk.

AI makes a major breakthrough in a math problem that had stumped experts for decades

For nearly 80 years, mathematicians have struggled to solve a classic geometry puzzle first posed by Paul Erdős in 1946: the planar unit distance problem. The question posed by the legendary Hungarian mathematician was, on the surface, deceptively simple.

It asks: if you take a piece of paper and add some dots, how many pairs can be exactly the same distance apart? Erdős himself proposed that the maximum number grows only slightly faster than the number of dots. Although many mathematicians agreed with him, no one could find a way to mathematically prove it.

Smartphones may soon be able to track hidden objects using LiDAR

Modern smartphones are packed with incredible technology, from high-resolution cameras and advanced graphics chips to AI processors. In premium models, this hardware includes LiDAR (light detection and ranging), which helps power augmented reality features and improve depth sensing.

And that capability could soon be in for a seriously impressive upgrade. Researchers at the Massachusetts Institute of Technology (MIT) have developed an algorithm that lets a phone’s LiDAR sensor detect objects hidden around corners. Details are in a paper published in the journal Nature.

Typically, this type of non-line-of-sight (NLOS) capability is found in labs and relies on bulky, expensive research-grade hardware. But the team’s breakthrough makes it possible for consumer LiDAR sensors to peek behind obstacles.

New framework helps robots turn complex language into precise 3D actions

Over the past few decades, roboticists worldwide have introduced increasingly advanced robots that can understand human instructions, move in their surroundings and reliably complete basic manual tasks. While they perform well in some scenarios, many of these robots still struggle to translate the instructions of users into precise and executable actions that would allow them to successfully complete desired tasks.

Recently, computer scientists have been trying to improve how robots respond to user commands or queries using vision-language models (VLMs), artificial intelligence (AI) systems trained to process both images and texts. These models can typically interpret basic requests such as “place the bottle onto the plate,” yet they often do not exhibit the spatial reasoning capabilities required to interpret more elaborate instructions and translate them into executable actions in real-world settings.

Researchers at the Chinese University of Hong Kong, the Zhejiang Humanoid Robot Innovation Center Co. Ltd and other institutes recently introduced Retrieval-Augmented Manipulation (RAM), a framework that could improve the ability of robots to connect abstract instructions with three-dimensional (3D) representations of the space around them. The new framework, presented in a Science Robotics paper, was found to improve the spatial reasoning capabilities of robots, allowing them to reliably follow more elaborate instructions, without requiring task-specific training.

The Growing Cybersecurity Risks To The Supply Chain In The AI Era

#cybersecurity #suppychains #ai #tech


Supply chains are a primary target for cybercriminals and provide the foundation of global commerce in the hyper-connected digital ecosystem of today. Artificial intelligence (AI) simultaneously exacerbates vulnerabilities as it revolutionizes operations through predictive analytics, automation, and real-time visibility. Sophisticated threat actors, ransomware groups, and nation-state actors employ AI to exploit the vulnerable links in intricate, multi-tiered supply networks.

Artificial intelligence can create dual-use dynamics. It promotes efficiency by facilitating real-time data transfers and hyper-connected operations, while simultaneously significantly expanding the attack surface. Compromises of a single vendor or update have been shown to have a cascading effect on economies, governments, and critical infrastructure through supply chain attacks.

In The AI Era, Supply Chains Are Prime Targets.

The complexity of supply chains is inherent, as they encompass continents, jurisdictions, and a multitude of third-party vendors, contractors, and software components. Each link—whether it be legacy systems, unvetted code, IoT devices, or 5G-enabled connections—provides potential entry points. AI exacerbates these risks by allowing attackers to automate reconnaissance, create polymorphic malware that evades detection, create personalized phishing campaigns, and identify vulnerabilities quicker than defenders can apply patches.

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