Détails

Source

TAO Daily

Publication

30 sept. à 22h38

Lien direct

https://taodaily.io/carbon-wins-pitchtensor-2026-with-500-tao-raised-in-under-20-minutes/

Contenu source (brut)

<p class="wp-block-paragraph">Carbon won Pitchtensor 2026 at Exploit Summit in Montreal, raising 500 TAO in 19 minutes and 58 seconds through a live crowdfunding round.</p> <figure class="wp-block-image size-large is-resized"><img loading="lazy" decoding="async" width="1024" height="498" src="https://taodaily.io/wp-content/uploads/2026/09/image-304-1024x498.png" alt="" class="wp-image-25186" style="aspect-ratio:2.0526315789473686;width:624px;height:auto" srcset="https://taodaily.io/wp-content/uploads/2026/09/image-304-1024x498.png 1024w, https://taodaily.io/wp-content/uploads/2026/09/image-304-300x146.png 300w, https://taodaily.io/wp-content/uploads/2026/09/image-304-767x373.png 767w, https://taodaily.io/wp-content/uploads/2026/09/image-304.png 1273w" sizes="(max-width: 1024px) 100vw, 1024px" /><figcaption class="wp-element-caption"><a href="https://app.bitstarter.ai/subnets/pitchtensor/">Pitchtensor’s Crowdfund Dashboard</a></figcaption></figure> <p class="wp-block-paragraph">Exploit Summit confirmed Carbon as the winner, with the team now set to move toward launching a Bittensor subnet with <a href="https://x.com/bitstarterAI">Bitstarter</a>.</p> <figure class="wp-block-embed is-type-rich is-provider-x wp-block-embed-x"><div class="wp-block-embed__wrapper"> <div class="embed-x"><blockquote class="twitter-tweet" data-width="500" data-dnt="true"><p lang="en" dir="ltr">The crowd has decided: Carbon wins Pitchtensor 2026.<br><br>Congratulations to <a href="https://x.com/RyanBequette?ref_src=twsrc%5Etfw">@RyanBequette</a> and <a href="https://x.com/carbonphysicsai?ref_src=twsrc%5Etfw">@carbonphysicsai</a>, pitching physics AI for engineering at Exploit Summit. Next stop: a Bittensor subnet, with <a href="https://x.com/bitstarterAI?ref_src=twsrc%5Etfw">@bitstarterAI</a>.<br><br>Hosted by <a href="https://x.com/macrozack?ref_src=twsrc%5Etfw">@macrozack</a>, with judges <a href="https://x.com/KeithSingery?ref_src=twsrc%5Etfw">@KeithSingery</a> <a href="https://x.com/MaxSebti?ref_src=twsrc%5Etfw">@MaxSebti</a>… <a href="https://t.co/HgMcrKDjHR">pic.twitter.com/HgMcrKDjHR</a></p>&mdash; Exploit Summit (@ExploitSummit) <a href="https://x.com/ExploitSummit/status/2105027937137213752?ref_src=twsrc%5Etfw">September 29, 2026</a></blockquote><script async src="https://platform.x.com/widgets.js" charset="utf-8"></script></div> </div></figure> <p class="wp-block-paragraph">Carbon&#8217;s pitch centered on a problem that becomes increasingly important as Physics AI moves into real engineering work. Faster predictions are useful, but engineers also need reliable evidence showing when a model can be trusted.</p> <h2 class="wp-block-heading">The Problem They Are Tackling</h2> <figure class="wp-block-image size-large is-resized"><img loading="lazy" decoding="async" width="1024" height="565" src="https://taodaily.io/wp-content/uploads/2026/09/image-302-1024x565.png" alt="" class="wp-image-25184" style="aspect-ratio:1.92;width:624px;height:auto" srcset="https://taodaily.io/wp-content/uploads/2026/09/image-302-1024x565.png 1024w, https://taodaily.io/wp-content/uploads/2026/09/image-302-300x166.png 300w, https://taodaily.io/wp-content/uploads/2026/09/image-302-766x423.png 766w, https://taodaily.io/wp-content/uploads/2026/09/image-302.png 1127w" sizes="(max-width: 1024px) 100vw, 1024px" /><figcaption class="wp-element-caption"><a href="https://t.co/Oq943aXfNo">Speed Of Traditional Simulation Vs Physical AI</a></figcaption></figure> <p class="wp-block-paragraph">Traditional numerical simulations can take one to two days for a single design iteration, while Carbon cites published benchmarks showing that trained Physics AI models and neural operators can produce predictions up to 1,000 times faster.</p> <p class="wp-block-paragraph">That speed can shorten engineering design cycles, but it does not by itself establish whether a model will behave correctly under the physical conditions where an engineer intends to use it.&nbsp;</p> <p class="wp-block-paragraph">For applications involving batteries, cooling systems, electric motors, or aerodynamic designs, engineers need to understand both where a model performs well and where it can fail.</p> <p class="wp-block-paragraph"><a href="https://carbonphysics.ai/">Carbon</a> is building infrastructure around that problem by combining Physics AI discovery with a system for generating evidence about model performance.</p> <h2 class="wp-block-heading">Turning Physics AI Into a Competition</h2> <figure class="wp-block-image size-large is-resized"><img loading="lazy" decoding="async" width="1024" height="533" src="https://taodaily.io/wp-content/uploads/2026/09/image-305-1024x533.png" alt="" class="wp-image-25187" style="aspect-ratio:1.92;width:624px;height:auto" srcset="https://taodaily.io/wp-content/uploads/2026/09/image-305-1024x533.png 1024w, https://taodaily.io/wp-content/uploads/2026/09/image-305-300x156.png 300w, https://taodaily.io/wp-content/uploads/2026/09/image-305-767x399.png 767w, https://taodaily.io/wp-content/uploads/2026/09/image-305.png 1120w" sizes="(max-width: 1024px) 100vw, 1024px" /><figcaption class="wp-element-caption"><a href="https://t.co/Oq943aXfNo">How Carbon Works</a></figcaption></figure> <p class="wp-block-paragraph">Carbon <a href="https://github.com/carbonphysicsai/Carbon">plans</a> to use Bittensor to turn Physics AI research into an open competition where different participants can contribute to model development while independent evaluators determine which approaches actually work.</p> <p class="wp-block-paragraph">The proposed system works around a few key steps:</p> <p class="wp-block-paragraph">1) <strong>Researchers and miners submit model-building strategies:</strong></p> <p class="wp-block-paragraph">Participants can submit models or reproducible configurations designed to solve specific physics problems.</p> <p class="wp-block-paragraph">2) <strong>Independent evaluators rebuild and test them:</strong></p> <p class="wp-block-paragraph">Validators reconstruct the submitted work and test it against reference physics using controlled, pre-registered conditions. <strong>The team that produced the model does not control its official evaluation.</strong></p> <p class="wp-block-paragraph">3) <strong>The network tests where models work and where they fail:</strong></p> <p class="wp-block-paragraph">Carbon&#8217;s focus is not simply on finding the model with the highest average accuracy. Evaluators can examine performance across specific physical conditions to establish where a model is reliable and where its predictions break down.</p> <p class="wp-block-paragraph">4) <strong>The results create evidence for engineering use:</strong></p> <p class="wp-block-paragraph">This gives Carbon a way to connect model discovery with the information engineers need before using an AI model for real decisions. Instead of treating a model&#8217;s output as sufficient on its own, the network can provide evidence about its actual performance under defined conditions.</p> <p class="wp-block-paragraph">That structure is central to Carbon&#8217;s pitch because the goal is not merely to produce faster Physics AI. It is to create a competitive system that can discover useful models and independently establish the conditions under which engineers can rely on them.</p> <h2 class="wp-block-heading">The Engineer Behind the Pitch</h2> <p class="wp-block-paragraph">Carbon co-founder and CEO <a href="https://x.com/RyanBequette?s=20">Ryan Bequette</a> brings a test-engineering background directly connected to this approach. He spent five years as a U.S. Air Force test engineer and later worked on verification for Virgin Galactic&#8217;s Iron Bird and Boeing&#8217;s T-7A simulator.</p> <figure class="wp-block-embed is-type-rich is-provider-x wp-block-embed-x"><div class="wp-block-embed__wrapper"> <div class="embed-x"><blockquote class="twitter-tweet" data-width=