Extropic AI's New Silicon Chip Ignites Debate on "Hardware Lottery" for Energy-Based Models

** Confirmed as the founder of Extropic AI, working on thermodynamic computing, and recently announced taping out their first silicon chip. The "TSU announcement" in the tweet is likely a misinterpretation or abbreviation related to Extropic's advancements.2. Sarah Hooker's "Hardware Lottery" essay: Its core idea is that the success of AI research often depends on its compatibility with available hardware and software, rather than inherent superiority, leading to certain ideas being overlooked or delayed.3. EBMs (Energy-Based Models): Still need to connect EBMs specifically to the need for new hardware or how Extropic's technology might benefit them. The tweet implies EBMs were "waiting for the right chips."My next step is to briefly explain EBMs and how Extropic's thermodynamic computing might be the "right chips" for them, or for AI in general, given the context of the "hardware lottery." I will perform one more targeted search for "Energy-Based Models hardware requirements" or "Extropic AI Energy-Based Models" to solidify this connection.Action: web_searchAction Input: {"query": "Extropic AI Energy-Based Models thermodynamic computing"}

San Francisco, CA – A recent announcement from Extropic AI, led by founder Gill Verdon, regarding the successful tape-out of their first silicon chip for thermodynamic computing, has sparked discussions within the artificial intelligence community. The development, aimed at significantly boosting energy efficiency and speed for AI computations, has prompted industry observers, including Rohan Pandey, to revisit the concept of the "Hardware Lottery" and its implications for advanced AI models like Energy-Based Models (EBMs).Rohan Pandey, commenting on the news, stated in a tweet, "surprised that no one has posted @sarahookr's hardware lottery essay since @GillVerd's TSU announcement maybe EBMs were just waiting for the right chips to come along." This highlights a growing sentiment that specialized hardware could unlock the full potential of certain AI paradigms. Gill Verdon's company, Extropic AI, is pioneering a novel approach to computing that leverages the physics of electrons for highly efficient AI processing, a departure from traditional digital and even quantum computing.Sarah Hooker's influential "Hardware Lottery" essay posits that the success of many AI research ideas is often dictated by their compatibility with existing hardware and software, rather than their intrinsic merit. This can lead to promising algorithms being overlooked or delayed simply because the "right" computational infrastructure isn't yet available. The essay argues that a significant portion of deep learning's recent success can be attributed to the fortuitous repurposing of GPUs, originally designed for graphics, which proved highly efficient for parallel matrix multiplications.Energy-Based Models (EBMs) represent a class of generative AI models that define a scalar energy function, where lower energy corresponds to more probable configurations. While theoretically powerful, EBMs have historically faced computational challenges, often requiring extensive sampling and optimization that traditional hardware struggles to perform efficiently. Extropic AI's thermodynamic computing, which aims to embed AI algorithms directly into the physics of the hardware for greater energy efficiency and speed, could potentially offer the specialized architecture that EBMs need to thrive.The successful tape-out of Extropic's silicon chip suggests a potential shift in the AI hardware landscape. If this new class of hardware can indeed provide the necessary computational substrate, it could lead to a renaissance for EBMs and other AI models that have been "waiting for the right chips," thereby reshaping the trajectory of AI research and development.