Akhetonics Photonic Computing: 1,000x Faster CPUs!

Learn about Akhetonics’ prototype that turns photons into a programmable, terahertz-class processor — and the memory, packaging, and power challenges they still need to overcome.27 min readJun 22, 2026--Akhetonics says their photonic CPU is the beginning of a revolutionary new type of all-optical computer: data would enter as light, be switched as light, circulate through memory as light, and leave as light. Leonardo Del Bino, the company’s co-founder and chief technology officer, reduces its hardest engineering problem to five words: “Light doesn’t like to stop.” Michael Kissner, Akhetonics’ co-founder and chief executive, says the company plans to deploy its first commercial machine with a major customer by the end of 2026. Between those statements lies the entire wager — a published two-bit optical CPU architecture, nearly 30 generations of photonic chips, a compiler intended to accept familiar code, ambitions for terahertz clocking, and unanswered questions about power, packaging, memory, manufacturing, and whether any of it can outperform electronics outside the lab.A Photonic Computer Made Of Fibers And PodsThe first thing to understand about Akhetonics’ optical computer is that it’s not one thing yet. It’s a collection of devices, test structures, packaged chips, software, architectural ideas, and increasingly integrated prototypes. In footage from the company’s Munich operation, Del Bino apologizes for the disorder as he walks through a room where one of the early systems is being assembled. Inside a conventional rack enclosure sits a computing board intended to hold four photonic chips in pods, together with an orchestrator responsible for routing information. Fiber arrays bring light into the chips and carry the output away. Electrical connections power selected devices. Instruments measure whether the components behave as designed.The scene can look familiar until the purpose of all those fibers becomes clear. Photonics is already embedded in the modern digital world: fiber-optic networks carry internet traffic across continents and oceans, optical transceivers connect data-center equipment, and silicon photonics is moving steadily closer to processors. But most of those systems convert light into electrical signals when computation begins. Akhetonics wants to eliminate that boundary. “We don’t have any electronic conversion,” Kissner says when describing the intended data path. He means that the information being processed should remain optical — not that the machine contains no electronics at all.Lasers still need electricity. So do amplifiers, tuners, monitoring circuits, control systems, host machines, and other supporting equipment. The company’s more precise claim is that computational data doesn’t have to cross repeatedly between the optical and electronic domains. In the finished architecture Akhetonics describes, information would arrive through an optical interface, move through optical logic, circulate through optical memory, and pass among specialized optical units. Conventional power would keep the machine alive, but photons would carry the bits that matter.Several stages of development can become blurred when the company discusses “the prototype.” There’s the published two-bit processor architecture designed to demonstrate general-purpose optical computation. There are individual optical logic gates, decoders, memory structures, routing devices, and multi-chip laboratory assemblies. Then there’s the much larger commercial system that Kissner says will consist of tens or hundreds of photonic chips. The essential question isn’t whether Akhetonics has made light perform computation — it has documented a credible architecture and component work. The question is whether all those pieces can become a reliable, programmable, economically useful computer.The Security Question That Led To LightKissner didn’t arrive at photonic computing through the usual path of semiconductor research. He spent 12 years as an airborne infantry officer in the German military and says he worked around special operations, while maintaining a parallel life in computing, programming, cybersecurity, and white-hat hacking. He later completed doctoral work in computer science on a neural-symbolic framework for mental simulation, with research touching inverse graphics, intuitive physics, meta-learning, episodic memory, and systems that combine symbolic reasoning with neural networks. “I’ve always been a nerd,” he says, acknowledging that the résumé doesn’t resemble the standard preparation for running an optical-chip company.His original concern wasn’t how to build the fastest possible processor. It was how to build one that could be trusted. Modern high-performance chips contain billions of transistors, intricate supply chains, layers of proprietary intellectual property, and design details that customers can’t realistically inspect gate by gate. For someone thinking about backdoors and secure computation, complexity itself becomes a risk. “How do you build a CPU that’s ultra-secure?” Kissner recalls asking. Any useful answer also had to preserve enough performance to run demanding cryptography rather than turning security into an unbearable slowdown.Fully homomorphic encryption illustrates the problem. It can allow computation on encrypted data without exposing the underlying information to the machine performing the work, offering a conceptual path toward cloud services that never see their customers’ unencrypted data. But the computational cost can be enormous. The mathematics exists; the hardware frequently makes it impractical. Kissner’s search for architectures that could combine inspectability, security, and speed eventually led him to optical computing — and to a laser scientist who had already spent years trying to make light behave like logic.Del Bino had worked on lasers for ultracold atoms and quantum simulation before turning toward all-optical logic gates and memory during research at the National Physical Laboratory in Britain and the Max Planck Institute for the Science of Light in Germany. Kissner contacted him on LinkedIn during the COVID era. Del Bino’s first reaction was suspicion: “Who’s this crazy guy?” When restrictions eased, they met in a Bavarian beer garden, made rough calculations, and concluded that no obvious law of physics killed the idea. More important, they recognized that Del Bino’s device-level approach and Kissner’s architecture-level approach would have to be developed together.When Faster Computers Stopped Getting FasterFor decades, computer users experienced progress as a simple rhythm: new processors arrived, software became more demanding, and the next generation of hardware made everything feel fast again. Shrinking transistors improved density, performance, and economics so consistently that the industry could treat scaling almost as a law of nature. A program that strained one generation of hardware might run effortlessly a few years later. The transistor count rose, clock speed climbed, and programmers could expect tomorrow’s machine to rescue today’s inefficient code.That pattern broke in the mid-2000s. As voltage and transistor dimensions stopped scaling together as neatly as before, pushing clock frequencies higher became thermally expensive. Processor makers turned increasingly to dual-core, quad-core, and many-core designs. Parallelism preserved overall progress, but it changed the bargain. A single sequence of instructions didn’t automatically become dramatically faster just because a new chip contained more cores. Software had to expose work that could be performed simultaneously, and some problems stubbornly remained serial.Modern processors are incomparably more capable than their ancestors, yet mainstream CPU clocks are still measured in a few gigahertz. Gains now come through better microarchitecture, speculation, larger caches, vector instructions, additional cores, specialized units, advanced packaging, chiplets, and faster memory interfaces. Much of a processor die is devoted not to arithmetic but to retaining and moving data: caches, interconnects, controllers, and mechanisms intended to prevent fast execution units from waiting on slower memory.This doesn’t mean Moore’s Law simply ended, or that electronic computing stopped improving. Nor should modern node names such as “3 nanometer” be interpreted as one literal feature on every transistor. Those labels now describe technology generations rather than a single physical dimension. The more consequential point is that leading-edge fabrication has become extraordinarily difficult and expensive, while shrinking devices no longer guarantees the effortless performance and efficiency gains of earlier eras. Akhetonics is betting that a different physical medium can open a different scaling path.AI’s Hidden Tax Is Moving DataArtificial intelligence makes the limits of the current architecture unusually visible. GPUs can perform vast numbers of mathematical operations in parallel, particularly the matrix calculations that dominate neural-network training and inference. But the arithmetic is only part of the job. Model weights, activations, intermediate results, prompts, context, and generated outputs must move constantly among accelerator cores, high-bandwidth memory, host processors, networking devices, and storage systems.A GPU can be ready to calculate while waiting for the next data. A cluster of GPUs can be ready while network switches move information between racks. An AI agent can call a model, wait for an answer, decide which tool to invoke, retrieve more data, and send another job back to an accelerator. Every transition imposes latency. Every electrical trace and cable has physical limits. Every conversion, retimer, switch, and memory access adds energy to a process whose total cost can be dominated by movement rather than multiplication.That’s why photonics has become central to the plans of companies