2026-05-31 21:00:01
In 1987, Richard Greenhill, a British photographer who was fascinated by (but had no actual training in) robotics, decided he wanted to build a life-size humanoid that could do useful things, like carrying luggage. He was working at a startup called Intergalactic Robots, but he couldn’t convince anyone there to build such a machine, so he set about building one himself, in his attic.
To help with his project, he organized a weekly get-together of a dozen or so like-minded folks. Every Wednesday night, his wife, Sally, would make a big pot of spaghetti, and the group would tinker with components scavenged from old printers and picked up from junkyards. They called themselves the Shadow Group. They eventually constructed several different robots, but their main project was the two-legged Shadow Walker.
In 1987, photographer Richard Greenhill organized a weekly gathering of DIY enthusiasts to work on projects in his attic, including the Shadow Walker. Richard Greenhill and David Buckley
Greenhill’s friend David Buckley, a robotics and animatronics expert he’d met at Intergalactic, sketched out a rough design based on medical textbooks of human bone structure and muscle movement. The robot’s skeleton, made of maple, was greatly simplified—only one bone in the lower leg and a single wide toe on each foot. The ankle’s double-axis design allowed for two degrees of movement. The knee had no complicating kneecap.
Greenhill didn’t want the robot to use motors, so its movement was controlled using compressed air to extend and contract 28 “air-muscles”—his version of a McKibben muscle, invented in the 1950s to mimic musculature with pneumatics. The muscles were connected to the bones across eight joints (hips, knees, ankles, toes), which provided 12 degrees of freedom.
The robot’s headless torso held the control valves, electronics, and computer interfaces. It stood 168 centimeters tall and 46 cm wide and weighed about 38 kilograms. The group managed to get the robot to stand up reliably and balance itself; it could even regain its center if pushed a little. But walking turned out to be more of a challenge.
Rich Walker joined the group as a teenager and began writing software to get the robot to stand. He was particularly interested in using neural networks to solve balancing problems, although he ran into a number of hardware obstacles, including the unreliability of the sensors and the valves, and the robot’s overall fragility. Over time, Walker and the team developed a standard library of routines to control the robot. Walker wrote a detailed description of the Shadow Walker in 1999, which is available on David Buckley’s website.
By the time the Shadow Group began developing Shadow Walker, engineers in academia and industry had been working on robotics for several decades. The world’s first industrial robot, the Unimate, debuted in 1961, and in 1967 Donald Michie and others began building a series of Freddy robots to investigate machine intelligence. The IEEE created its first dedicated robotics organization in 1984 when it established the IEEE Robotics and Automation Council, which became the IEEE Robotics and Automation Society in 1987. Also in 1987, the nonprofit International Federation of Robotics was established to promote research, development, use, and cooperation in the field of robotics.
As Shadow Walker pushed the limits for a DIY humanoid robot, industrial humanoids were also gaining ground. In 1986, Honda began working on its experimental (E-series) and later the prototype (P-series) humanoid robots, finally unveiling the P2 in 1996. The P2 stood 183 cm tall and weighed 210 kg. It was the first humanoid capable of stable, autonomous walking. This work eventually led to the development of the groundbreaking ASIMO.
Greenhill’s friend, roboticist David Buckley, consulted medical textbooks to create Shadow Walker’s humanoid design.Richard Greenhill and David Buckley
In the late 1980s, the public was both fascinated and horrified by the potential of robots. Businesses saw robots as a way to increase productivity, while workers worried they would take their jobs. Children viewed them as wondrous toys, while people with disabilities embraced them as tools of liberation. Military experts hoped robots would fight wars without endangering human soldiers, while politicians pondered if robots might eventually get to vote. Philosophers thought robots could challenge our notions of intelligence (and stupidity), while the religious struggled with concerns about the human race in a robot-dominated future.
Shadow Walker’s simplified anatomy included only one bone in the lower leg and a single wide toe on each foot.Science Museum Group
Mowforth himself thought all of the competing visions of robots were overblown. Steeped in machine learning research and robotics development, he knew firsthand the limitations of the state of the art: Robots rarely worked as intended, easily broke down, and glitched over seemingly trivial problems. He envisioned the Robot Olympics as a testbed to assess what the latest generation of robots could and could not do.
At the 1990 Robot Olympics, held in Glasgow, Shadow Walker wore pants to conceal its pneumatic “air-muscles” from competitors.Adam Hart-Davis/Science Source
The call for participation was wide open. Instead of having predetermined categories of competition, the organizers opted to see who applied to compete and then group them based on their claimed capabilities. In addition to picking the winners of individual events, the judges would select an overall Olympic champion based on the quality of the hardware, the sophistication of behavior, and novelty. Other prizes were given for young competitors, technologies that showed commercial potential, and design. In the end, more than 50 robots were entered, from a mix of universities, industry, and hobbyist groups from Canada, France, India, Japan, Mexico, the Soviet Union, the United States, the United Kingdom, and Yugoslavia.
There were plenty of disappointments. Trolleyman, a golf-cart-like wheeled robot, suffered a power failure while carrying the opening Olympic torch through the streets of Glasgow. The pile rug in the arena tripped up many robots that had been trained only on flat, smooth floors. David Buckley later concluded that the events were too difficult, and that the Olympics didn’t push development forward.
Of course, there were winners. In a surprise triumph for vintage technology, the fully mechanical 19th-century Japanese Archer from the Museum of Automata in York, England, won gold in javelin, beating out competitors more than 100 years its junior. The overall Olympic Champion was Yamabico, Shoji Suzuki’s entry from the University of Tsukuba, in Japan, which won bronze in obstacle avoidance and gold in wall following, but was disqualified in the talking category for not speaking English.
The Shadow Group had high hopes for Shadow Walker. Unfortunately, though, it failed to take a step, and the biped race was won by the Cardiff University Biped. Shadow Walker now resides in the collections of the Science Museum in London.
In 1997, a paying customer in search of a robotic leg compelled the Shadow Group to get serious and become a registered company. Shadow Robot is now Britain’s oldest robotics company. Rich Walker, who had left the Shadow Group to earn a B.A. in mathematics and a diploma in computer science at the University of Cambridge, joined Shadow Robot in 1999 as technical director. Today he’s the director of the company.
Shadow Robot specializes in durable robot hands rather than walking robots. But the focus on hands is also a legacy of the Shadow Group. Walker remembers that the Shadow Group’s first humanoid hand in the late 1990s was impressive simply for being able to pick up a pint of beer (a smooth-sided, thin-walled glass). Today, Shadow Robot’s hands are testbeds for dexterity. Gone are the pneumatic muscles, replaced by actuators that move each finger with precision. The classic model contains 20 motors, allowing for abductive and adductive movement with 24 degrees of freedom.
Shadow Walker’s operator wore a data suit that captured his movements and allowed the robot to copy them.Richard Greenhill
In a recent blog post, Sejal Parsotomo, senior marketing executive at Shadow Robot, wrote that while humanoid robots are great for public relations, specialized dexterity is key for success: A robot that can walk into your factory may be impressive, but a robot that can reliably manipulate objects is transformative.
In its struggles to take more than a few steps, the Shadow Walker showed the inherent difficulty that robots had in mastering even low-level skills. In August 2025, Beijing hosted the World Humanoid Robot Games. Competing in sports such as gymnastics, soccer, and track events, as well as more “useful” tasks like hotel cleaning and sorting medicine, these robots could literally have run circles around the competitors in the first Robot Olympics 35 years earlier. And yet, there is still so much work needed in order for robots to navigate the human-built environment. Despite the astonishing progress, we’re still not all that close to actually useful humanoid robots.
Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology.
An abridged version of this article appears in the June 2026 print issue as “Learning to Walk.”
Richard Greenhill gives an overview of his life and the founding of the Shadow Group in a post on Shadow Robot’s corporate website.
David Buckley has a compilation of resources on the Shadow Biped Walker, including specifications from the 1999 iteration and a brochure from the 1st International Robot Olympics.
There is coverage of the Robot Olympics worthy of a gossip sheet in La Repubblica and lovely footage of the competition in this TV-am interview of Peter Mowforth by Lorraine Kelly.2026-05-30 23:13:01
This is the place where you face yourself,
the you that could be you with a few
different parts, a pump for your heart,
eyes off color, and fresh off the shelf
fake hair (a bit obvious), skin smoothed.
You’re not perfect, but it’s a good start.
Down to small digits, you’ll be improved.
Memory maintained by small motors,
as long as these gizmos don’t glitch.
What’s before you? Full replacement or
a constant game of test and switch,
pieces peeled off, disconnected, removed,
until you are not yourself, at least,
not the self you knew. That self has ceased,
bit by bit less you at each release.
2026-05-29 21:00:01
Electrons are great. We use them to move vehicles, illuminate cities, and, of course, compute. But computation is not confined to the world of electronics. And shifting to alternative nonelectronic realms can unlock unique advantages: Photonic chips, for instance, process information with light while generating little heat. Another compelling alternative is fluidics, which uses pressurized gases or liquids to build logic circuits. Pioneered in the 1960s but sidelined by microchips, the field reemerged in the 1990s as “microfluidics.” This approach aims to shrink laboratories onto a single chip by creating microscopic fluid channels with integrated micropneumatic control systems.
Today, there is a second fluidic revival, this time in the domain of soft robotics. Scaling microfluidic designs up to the millimeter-scale range (millifluidics) enables the higher flow rates necessary to drive robotic actuators. These robots exploit the nonlinear behaviors of soft materials to create lifelike motion and safer interactions, often utilizing pressurized air.
By building systems that “think” with the same air that powers them, we can drastically reduce the need for bulky electronic-to-pneumatic interfaces. This is the focus of my Soiboi Studio robotics lab. With millifluidic logic, I have steadily scaled the complexity of my designs. What began with a simple oscillator has most recently evolved into a clock featuring a soft, four-digit, seven-segment display.
Building on microfluidics research from the early 2000s and recent developments from the Grover Lab at the University of California, Riverside, I’ve developed millifluidic devices using standard 3D printing and silicone casting. The basic architecture is simple: A flexible membrane is sandwiched between rigid layers embedded with networks of air channels.
Just as electronics rely on differing voltage potentials, these fluidic circuits operate on the pressure difference between atmospheric pressure (logical 0) and a near-vacuum at around −60 kilopascals of relative pressure (logical 1). Using negative pressure means the membrane is pulled into openings. This creates robust seals that allow me to replicate electronic building blocks.
A cast silicone membrane forms the face of the clock [top], while behind it sits 3D-printed millifluidic blocks [middle rows]. An Arduino Uno controls driver boards that operate solenoids, which are connected to valves that are attached to a vacuum pump [bottom row].James Provost
While fluidic resistors are easily realized by adjusting the channel geometry, the heart of the system is a valve that mimics a metal-oxide-semiconductor field-effect transistor, or MOSFET. This vacuum “transistor” features a flow layer with two chambers (the source and drain) divided by a central valve seat and a control layer containing a cavity (the gate). A membrane runs between the control and flow layers and normally prevents airflow between the source and drain chambers. To switch the transistor on, a vacuum is applied to the gate chamber, sucking the membrane into the cavity and lifting it off the seat. This opens a path for airflow, equivalent to closing an electric circuit. By adding a small aperture to the membrane, I created a check valve—the fluidic equivalent of a diode. By combining transistors and resistive “pull-down” channels, I can build a full suite of logic gates.
The original microfluidic designs that inspired me were fabricated from etched glass and milled acrylic. Adapting them for a standard 3D printer required reengineering the logic elements and mastering two critical fabrication techniques.
First, I need airtight prints, yet printed plastic is notoriously porous. By printing at elevated temperatures, slow speeds, and slight overextrusion, I was able to fill microscopic gaps. When you’re using transparent filament, there’s a handy visual indicator: The more transparent the plastic appears, the lower its porosity.
Second, I used glass for my print bed. By printing the upper and lower chambers directly against this bed, I got the interface surface to become mirror smooth. This finish is essential for creating reliable, airtight seals. A 0.3-millimeter silicone membrane is placed between the layers and secured with screws.
The clockface is a cast silicone membrane. Each digit segment is formed by a small underlying cavity. When air is evacuated from this cavity, the membrane is sucked inward to create a concave hollow; when atmospheric pressure is restored, the silicone pops back flush with the surface. The result is a mesmerizing, organic motion.
The “brain” of the clock is an Arduino Uno, while the fluidics significantly reduce the hardware footprint. A four-digit, seven-segment display with two separator dots would require 29 solenoid valves to control directly. My clock needs just 11 valves.
A pneumatic transistor is off when its upper control chamber is at atmospheric pressure [top]. When air is removed from the control chamber, it lifts a membrane, which allows air to flow between lower flow chambers and turns the transistor on [bottom]. James Provost
To understand how it works, consider a standard electronic four-digit, seven-segment LED display. This also uses 11 pins to drive its digits. (In clockface displays, an additional pin is required to drive the separator dots.) Every digit is connected to a shared data bus with seven lines, one per segment. The four control lines select individual digits. Only one digit is illuminated at time, and strobing the digits at least 50 times per second creates the illusion that all four are simultaneously illuminated.
Such high-speed switching is not possible with air. Instead, I rely on memory. Each segment acts like a capacitor: By evacuating its cavity (logic 1), you “charge” the segment; by restoring atmospheric pressure (logic 0), you discharge it. Hence, each digit acts as an independent 7-bit memory. If the system is sufficiently airtight, the segments maintain their state for several seconds.
Like the electronic display, the system utilizes a seven-line data bus. Each line connects to a solenoid valve that provides either vacuum or atmospheric pressure. To selectively address the individual digits, I placed a fluidic transistor between each segment and its data line. All the transistors’ control inputs for a given digit are combined into one “write enable” line connected to its own solenoid valve. Activating this valve allows me to write data into the corresponding digit’s memory.
The clock updates one digit per second, meaning a full cycle across the face takes 4 seconds. This cycle also drives the separator dots: A set of fluidic diodes connects the enable lines to the dots’ cavities. Consequently, as each digit is addressed, the dots pulse automatically.
This display is more than a clock; it is a soft robot that happens to tell time. By offloading computation to the same air that powers movement, the clock approaches a new class of machines that are simpler, lighter, and more integrated. I’m now developing a guide for getting started with vacuum-powered logic and may release a refined version of this clock in the future. Watching the silicone skin morph serves as a fascinating reminder that not all logic needs silicon; sometimes, all you need is flexible silicone and a flow of air.
This article appears in the June 2026 print issue as “The Soft Clock.”
2026-05-28 21:00:01
I have been an application-specific IC (ASIC) designer for almost three decades. Over that time, I’ve moved through the full academic trajectory, from graduate student to full professor; later, I transitioned to industry after an unsuccessful stint at entrepreneurship. When I made the switch to the private sector in 2019, I began focusing on a critically important aspect of the electronic industry: silicon intellectual property.
As much as 80 percent of the physical area in today’s most advanced chips is occupied by blocks that aren’t made for specific products or even designed by the consumer-facing companies that built them. Instead, chipmakers draw heavily on established silicon IP from companies like Arm, Cadence, Rambus, Synopsys, and the company I work for, Silicon Creations.
Throughout my career, I’ve designed chips for very different purposes, including enabling the research program in my academic lab and expanding the IP portfolio of my company. When I joined Silicon Creations, I had no idea how differently the industry approaches IC design and encountered a steep learning curve. Initially, it seemed that much of my two decades of academic research and training did not directly translate to the role. I had to learn new skills and adopt a new mindset.
Today, demand for ASICs is rapidly growing, driven by the need for specialized chips in the automotive sector, AI applications, and more. By one market estimate, the ASIC market is expected to grow from US $23.4 billion to $38.8 billion by 2033, and the semiconductor industry as a whole is projected to hit $1 trillion by 2030. The industry needs more chip designers—but if you’re coming from an academic background as I did, there are a few things you’ll need to know.
The differences between industry and academe begin with a divergence in purpose. In academia, my primary objective was to generate new knowledge: to propose a novel circuit technique, validate an unconventional architecture, or explore the limits of performance in a given domain. A successful chip is one that demonstrates a concept. In industry, it is not nearly enough to prove that something can work. The goal is to ensure that it works reliably, repeatedly, and at scale. Success is measured not by novelty but by whether the silicon meets specifications, yields as expected in production, and supports a competitive product delivered on schedule.
This leads to a stark contrast in risk tolerance. Academic designs often deliberately push into unproven territory, where even partial success can yield valuable insight. In industry, however, we systematically minimize risk. The cost of failure makes first-time silicon success a central requirement—especially at advanced technology nodes, where the lithography masks used to transfer circuit designs onto silicon wafers alone can cost tens of millions of dollars. As a result, industry design flows are built around eliminating uncertainty through conservative margins, extensive validation, and careful reuse of proven solutions.
“Academia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale.”
This paradigm has existed since the 1970s, when application-specific chip design was established. However, the gulf between academia and industry has expanded since the mid-2010s, when FinFET technology, a 3D architecture using vertical “fins” of silicon, was widely adopted in industry. System designs are also becoming increasingly modular with the advent of chiplets. This fundamentally altered the economics and complexity of ASIC development, with design costs rising by almost an order of magnitude. Initiatives like Taiwan Semiconductor Manufacturing Co.’s University FinFET Program and new government-funded chip-design hubs now let some well-resourced universities design for more advanced architectures, but the technology is still out of reach for many academics.
Consider a startup developing an ASIC. Its engineering team may have deep expertise in a particular algorithm, sensor interface, or system architecture, the features that define its competitive advantage. But it is unlikely to possess world-class expertise in every supporting function. Developing each of these blocks internally would require significant time, capital, and specialized talent. Doing so could delay market entry beyond the startup’s viability.
Even large semiconductor companies face similar constraints. Advanced-node development demands intense focus. Allocating a team to redesign a standard interface block that has already been implemented elsewhere may be difficult to justify when differentiation lies at the system level, such as an inference chip’s ability to speed up neural network computations. The time it takes to move a new chip from conception to market and risk mitigation, not self-sufficiency, govern most decisions about in-house development versus outsourcing.
The economics of advanced IC manufacturing reinforce this reality. When the development cost of a leading-edge chip reaches hundreds of millions of dollars, minimizing risk becomes a central design imperative.
In this context, silicon IP emerged as a practical solution. Similar to how software developers rely on preexisting libraries rather than writing every function from scratch, ASIC designers license predesigned, preverified silicon blocks—such as processor cores, memory interfaces, and security engines—from highly specialized IP vendors. These blocks can then be integrated into larger, increasingly complex systems.
With the use of silicon IP, industry is able to widen the scope of its designs. Academic efforts tend to focus on block-level innovation: a new analog-to-digital converter architecture or an ultralow-noise amplifier, for instance. These designs typically abstract away many of the complexities of bringing a chip to market, such as packaging constraints, long-term reliability, and manufacturing yield.
In industry, the focus shifts to system-level integration. Modern systems on chips, or SoCs, incorporate dozens or even hundreds of functional blocks. Managing signal integrity, timing, firmware interaction, and system-level validation becomes as critical as the design of any individual block.
Verification philosophy also diverges sharply. In academia, the goal of verification is to demonstrate that the concept works under nominal conditions, which may not always reflect how it would perform in real applications. Even if only a fraction of fabricated chips from a multiproject wafer operates correctly, the design may still be considered a success if it validates the underlying idea.
At my academic lab for instance, we used to receive 40 chips from a TSMC prototyping service and started testing them in batches of five. If the first five or 10 chips proved functional, we had already collected more than enough data for a publication. If some of them failed, we weren’t required to mention this when publishing the results.
In industry, verification is exhaustive, critical, and often dominates the development schedule. Failures are measured in parts per million, and even rare anomalies are carefully analyzed and documented to identify root causes and prevent recurrence. When I started at Silicon Creations, I was surprised by the level of detail and scrutiny designs face.
Differences in time horizons and economic constraints reinforce each of these contrasts. Academic projects operate on flexible timelines aligned with research and funding cycles. If I missed a deadline, I just had to wait for the next cycle. Industry projects are driven by fixed product schedules and market windows, frequently targeting costly leading-edge nodes to achieve competitive performance, power, and area efficiency. Missing a deadline can negate the value of an entire design and may have major financial consequences along the entire supply chain.
In essence, academia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale. Both are indispensable, but they operate under fundamentally different definitions of success. As ASIC complexity continues to grow, understanding both perspectives will be essential for the next generation of engineers navigating the evolving semiconductor landscape.
This article appears in the June 2026 print issue.
2026-05-28 18:00:01
A practical introduction to phase noise concepts, explaining how oscillator instability affects RF systems and how phase noise is measured, analyzed, and reported.
What Attendees will Learn
2026-05-27 21:00:01
This article is adapted by the author with permission from Tech Policy Press. Read the original article.
South Africa is not just another developing country struggling to govern artificial intelligence; it is the exception with leverage, and the window to act on it is closing. It holds approximately 88 percent of global platinum-group metal reserves, critical inputs to parts of the semiconductor and data-center supply chains that make AI infrastructure possible. It hosts the largest data-center market on the continent. Its existing hyperscaler relationships give it procurement leverage that most African states will never have. And a major geopolitical contest over AI infrastructure is being fought on its soil right now, between Chinese and American technology companies competing for control of the systems that will underpin an entire continent’s public sector.
In physics, leverage requires three things: a fulcrum, a lever arm, and the ability to apply force. The Bushveld Complex, the world’s largest platinum-group metal deposit, is the fulcrum: a mineral endowment that gives South Africa a position in the semiconductor supply chain that no other African state holds. The since-withdrawn draft policy is the lever arm. The unresolved “OPTION” provisions in the policy are where force would be applied. Without a policy that specifies what South Africa wants in return for market access, the lever arm sits unused, and the weight of two of the world’s largest technology ecosystems settles exactly where those ecosystems want it to settle.
This makes South Africa a global test case. Not because its proposed means of governance is exemplary, but because it is the one developing country with enough structural leverage to negotiate genuinely different terms, and the one that is choosing, through inaction, not to. The recent announcement of a new panel to update the draft policy by January 2027 is an important opportunity. But the deeper failure is not that an AI policy contained bad references. It is that no verification process caught them before the document entered the public domain. That is a systems problem, not merely a political one. It points to a missing layer in how governments are adopting AI.
Last year, Huawei pitched an emerging-product bundle to tech executives across the continent. Huawei was now bundling access to DeepSeek’s large language model with its own cloud and storage infrastructure. The price differential was stark—in some cases by more than 90 percent.
At the same time, Microsoft announced plans to spend ZAR 5.4 billion ($300 million) by the end of 2027 on cloud and AI infrastructure in South Africa, building on a prior ZAR 20.4 billion investment. Google, Amazon Web Services, and Oracle already have cloud regions in the country. According to one analysis, the country’s data-center market was valued at US $2.16 billion in 2024, the largest in Africa.
These are not commercially neutral investments. Huawei’s infrastructure reach has been explicitly linked to Chinese strategic objectives, including a documented track record of providing governments with surveillance infrastructure through its Safe Cities network. U.S. hyperscaler investment comes with its own dependency structure: closed models, pricing set unilaterally, and terms of access that no African government has meaningfully shaped. South Africa is being asked to choose between these dependency models without a policy that specifies what it wants in return.
There is a particular irony in South Africa’s position. The country whose mines supply platinum-group metals essential to semiconductor manufacturing, and through them to AI compute, has drafted a policy that treats it as a consumer of AI systems rather than a stakeholder in their governance. South Africa digs up the minerals that make AI possible. It has no say over the AI built from them.
The AI triad framework covers algorithms, compute, and data. South Africa has no frontier model development capacity. South Africa holds significant data assets in financial services, health care, and agriculture, with no clear framework for their sovereign management. South Africa possesses PGM (Platinum Group Metals) leverage of global significance on the compute axis, currently being transferred without meaningful condition. It also has exceptionally high solar irradiance and significant renewable-energy potential. A country that can offer both critical mineral inputs and the energy to power the infrastructure those minerals help build occupies a negotiating position of unusual strength.
The Draft Policy proposes no minimum terms for hyperscaler investment, no data sovereignty requirements, no technology transfer conditions and no compute visibility mechanism. Multiple provisions are explicitly left unresolved, marked “OPTION,” including the most consequential choices about how governance will function. Infrastructure decisions made now determine what is renegotiable later, and the answer is: very little.
The three infrastructure futures on offer each create a structurally different form of dependency, and only one creates sovereign capability. The Huawei-hosted DeepSeek integration offers low cost and open-source weights, but with data stored on infrastructure potentially accessible under Chinese legal frameworks, creating surveillance dependency in a pattern already documented across Africa. The second is U.S. closed-model dependency: higher capability, more reliable data protection, but complete API dependency on developers abroad. The third is locally hosted open-weight infrastructure: models governed under South African data-sovereignty rules, on infrastructure subject to minimum terms, developed with South African data. As Nathan Lambert at Interconnects has observed, open-weight models are likely the only realistic way to get sovereign AI off the ground as a real effort, enabling local communities and economies to integrate meaningfully with the technology. But this requires procurement conditions, not goodwill.
The GovAI “Governing Through the Cloud” framework identifies four roles compute providers should accept as conditions of operating at scale: securers (protecting model weights and training data), record keepers (maintaining infrastructure usage logs), verifiers (confirming customer compliance with safety standards) and enforcers (restricting access when violations occur). These are operational requirements, not theoretical categories—specific, enforceable, and well within the bargaining power of a market of South Africa’s size and mineral position.
A detailed policy analysis submitted to the Department of Communications and Digital Technologies (DCDT) identifies the specific provisions the final policy must contain: mandatory minimum terms for foreign compute infrastructure investments above ZAR 500 million (~$30 million); a compute reporting threshold; a National AI Safety Institute mandate covering defensive monitoring of AI capability accumulation; and National AI Champion Sector designations to create data assets for domestic model development. Each provision converts a structural advantage into a governance instrument before that advantage is foreclosed by market reality. Just as modern software security increasingly depends on knowing what components are inside a system—model provider, training data, compute environment, evaluation methods, update cadence, human review points, and failure-reporting procedures—public-sector AI governance requires a clear account of the stack before deployment, not after a problem surfaces. A public institution that cannot verify the sources in its own AI policy is unlikely to be ready to verify the AI systems it procures, deploys, or regulates.
South Africa’s choices will establish a regional precedent for what is commercially negotiable in AI infrastructure. If South Africa negotiates data-sovereignty guarantees and technology-transfer conditions as requirements for hyperscaler investment, it creates a replicable model. If Microsoft’s $300 million investment and Huawei’s infrastructure expansion proceed on standard commercial terms, as they are currently, it normalizes extractive AI infrastructure across the continent. The lesson is not specific to Africa. Governments everywhere are producing AI strategies while lacking AI assurance infrastructure. South Africa is an early warning, not an isolated case.
The public comment period closed when the policy was withdrawn. But a parallel process remains live: the National Treasury’s Draft General Public Procurement Regulations—the legal instrument that will govern every government AI contract—closes for comment on June 15. Those regulations contain no AI-specific provisions.
South Africa has more AI leverage than any country on the continent. Some argue, with force, that governance requirements risk deterring the infrastructure investment South Africa urgently needs: compute capacity, reliable energy, venture capital, and talent retention. That concern deserves a direct answer. Minimum procurement terms, compute reporting thresholds, and technology transfer conditions are not barriers to investment. They are the conditions under which investment serves the host country rather than extracting from it. Infrastructure built without minimum terms produces dependency. Infrastructure built with them produces leverage. To serve the public interest, its AI policy must use it.
When late last month News24 reported AI-hallucinated references in the draft AI policy, Minister of Communications and Digital Technologies Solly Malatsi withdrew the draft policy. That was a mistake that could cost South Africa and the rest of the continent the initiative on this urgent issue. His more recent constitution of an independent panel is a belated step in the right direction, if it can turn South Africa’s leverage into policy. The panel—chaired by Professor Benjamin Rosman of the Wits Machine Intelligence and Neural Discovery Institute, and including Professors Vukosi Marivate and Alison Gillwald of Research ICT Africa and Dr. Jabu Mtsweni of the Council for Scientific and Industrial Research—has the technical and governance credibility to produce a stronger document. A revised draft is due to be ready for public comment by January 2027. South Africa remains without a formal AI governance framework in the interim.