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 The since-withdrawn draft policy is the lever arm.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 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. What it has not yet produced is a timeline. No revised draft has been scheduled. South Africa remains without a formal AI governance framework in the interim.2026-05-26 21:00:00
Floppy disks are several decades old—many of the disks are degrading and the data stored on them is at risk of being lost. In response, Leontien Talboom, a technical analyst at Cambridge University Libraries and Archives, led a roughly year-long project preserving floppy disks called “Future Nostalgia,” which concluded in January.
Leontien Talboom is a technical analyst at Cambridge University Libraries and Archives, where she transfers material from a wide range of storage media to make them accessible to archivists.
IEEE Spectrum spoke to Talboom about her work preserving data from Cambridge’s collection of floppy disks and collecting knowledge about the disks themselves.
Why is it important to preserve floppy disks now?
Leontien Talboom: Two reasons. First, the physical media is starting to degrade. Floppy disks are made from plastic, but they’ve got a magnetic layer of iron oxide, and that’s deteriorating. A lot of floppy disks are found in attics or garages, which means they also suffer from mold.
Second, a lot of people who developed floppy disks and systems that use floppy disks are starting to retire or pass away, which means that a lot of tacit knowledge is disappearing.
Whom did you go to for that tacit knowledge?
Talboom: I went to the retro computing community. Their work is more around preserving these machines to keep them running [than] the data that lives on the floppy disk. But they know their stuff about floppy disks.
For example, they know that in a lot of the older disks, the inside of the disk—the doughnut—gets stuck to the top. So if you flex the casing, the doughnut falls down again. If I hadn’t known that, I would have assumed that those disks in our collection were broken or corrupt.
What is the most difficult part of working with floppy disks?
Talboom: Accessing the files can be quite challenging if we don’t understand the file system. Within libraries and archives, we get a lot of material from machines that are not as well loved. Many of the personal computers that you had at home, such as the Amstrad or ZX Spectrum or BBC Micro, are very well documented. But a bunch of our material comes from business or research systems. They’re not as nostalgic for people, so there’s not as big a community preserving this type of material.
Do you have a favorite type of floppy disk?
Talboom: Five and a quarter. The weirder the system, the more frustrating and fun it is. I quite like doing that detective work.
The Amstrad disk has also really stolen my heart. The popularity of floppy disks is very geographically dependent. Our library, for example, has these Amstrad 3-inch disks. But if you go to the U.S., they’re really uncommon. They weren’t able to manufacture enough of these drives, and [3.5-inch disks] took over at a certain point. But they’re really cute.
What’s the best method for sustainably storing data?
Talboom: The main thing is actively looking after it. A lot of the floppy disks we get in the library haven’t been accessed for 20 or 30 years, which means that you need certain special hardware to actually read them, and then work with emulators or other tools to make these file formats accessible.
Now that we’ve done that work and transferred it, we can monitor it and make sure it’s not suffering from anything like bit rot. We can also make decisions around migrating it to other file formats or working on specific file systems or unknown file formats in more detail.
2026-05-26 18:00:01
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Outgassing is the release of volatile substances from a cured adhesive over time. These released materials, which may include residual solvents, unreacted monomers, or other chemical species, can deposit on nearby surfaces, causing contamination that interferes with sensitive components.
The industry standard for measuring outgassing is ASTM E595, developed by NASA. This test exposes a cured sample to 125 °C at high vacuum (10⁻⁵ to 10⁻⁶ torr) for 24 hours, measuring Total Mass Loss (TML) and Collected Volatile Condensable Materials (CVCM). To meet NASA low outgassing requirements, materials must exhibit less than 1 percent TML and less than 0.1 percent CVCM.
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If contamination appears on optical surfaces or outgassing test results are higher than expected, an incomplete cure might be one of the root causes. The first step is to verify that the adhesive has fully hardened to its specified Shore hardness. The next step is to consider adding or extending heat cure to improve cross-linking.
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2026-05-26 02:00:01
The OnCampus program, administered by IEEE Educational Activities, last year expanded its engineering experiences from two to seven universities.
Part of TryEngineering, the program is held at universities around the world, offering preuniversity students hands-on opportunities to solve engineering problems.
The IEEE Innovation Committee provided funding for the additional locations.
The electrical engineering and computing faculty at the University of Zagreb, in Croatia, hosted a two-day program in June. Twenty-five children ages 10 to 14 participated in lectures and workshops on artificial intelligence, computer science, robotics, and astronomy. Tomislav Jagušt, an IEEE senior member and the chair of the IEEE preuniversity coordinating committee, led the program.
In September the Arab Academy for Science, Technology, and Maritime Transport’s engineering college held a two-day session at its Abu Kir, Egypt, campus. Fifty students participated in hands-on activities on Ohm’s law, radio communications, and circuit building. They also learned from professors about engineering careers and job opportunities.
Also in September, the Majan University College, in Muscat, Oman, hosted 40 high school students who competed in six challenges to design and build circuits. These include an IoT design and an LED brightness control using a potentiometer, a three-terminal, manually adjustable resistor that functions as a variable voltage divider.
The program also highlighted AI and quantum computing technologies and introduced students to job opportunities in the fields.
The workshop transformed curiosity into creation, empowering students with technical skills and confidence in emerging technologies.
In November at the Universiti Malaysia Perlis, in Arau, 50 students explored the fundamentals of quantum computational intelligence and AI through hands-on activities and interactive simulations. IEEE Senior Member Mohd Hafiz Ismail, a professor of electronic engineering and technology, gave an introduction about quantum computing intelligence technology.
The Hellenic Robotics Center of Excellence at the National Technical University of Athens hosted a two-day session in December. Twenty-five students explored robotics and AI through hands-on design challenges such as TryEngineering’s AI and machine learning methods. They also toured the university’s research facilities.
The City University and St. Francis University in Hong Kong, and the University of Ioannina, Arta campus, Greece, participated in the program for a second year.
Under the leadership of IEEE Senior Member Paulina Chan and volunteers from the IEEE Hong Kong Section, the City and St. Francis universities jointly held the program in July. They welcomed 55 students ages 12 to 18 from 41 schools.
The students attended tutorials on foundational concepts and theories of AI. They worked in small teams on projects using AI-generated images, voice, and music manipulations. They were coached by students from St. Francis and Imperial College London. The participants presented their projects to judges, teachers, and parents.
The students also visited a nearby semiconductor equipment manufacturer to learn about technology careers from engineers working there.
The results of a post-program survey showed strong satisfaction with OnCampus, with nearly 75 percent of participants giving it a rating of 4 or higher out of 5.
“I enjoyed getting to know about deep learning and its application,” one student participant said. “The content of the activity matched my interest, and I gained new knowledge.”
“OnCampus is led by a strong team with lots of experts in the field,” another said. “It’s a rare chance for students to use software, learn about the theory behind how deep learning works, and get a glance at future possibilities.”
The University of Ioannina hosted the program in Arta in July with support from IEEE Senior Member Stamatis Dragoumanos and IEEE members Nikos Giannakeas and Eleftheria Kallinikou. Nearly 50 students, ages 12 to 16, attended the seven-day event, supported by 17 instructors and six volunteers from the university’s IEEE student branch.
The students learned about AI, augmented reality, microchip design, microcontrollers, and 3D printing. They also attended presentations by engineers from the industry. To give the students exposure to real-world engineering, they visited two hydroelectric power plants and a green data center.
At the end of the program, students presented their projects and showcased the technical skills they had developed.
Those involved in the TryEngineering OnCampus program are proud of the impactful experiences students have gained. The opportunities are possible because universities open their doors, share their expertise, and invest in the next generation of innovators.
The University of Zagreb, the Arab Academy for Science, Technology, and Maritime Transport, the Majan University College, and The City University and St. Francis University will be participating again this year.
To learn how you can bring the OnCampus program to your educational institution, send a request to [email protected].2026-05-25 21:00:01
“Social engineering” sounds like something out of a conspiracy thriller, charged with totalitarian control and fringe paranoia. More mundanely, it’s come to be associated with phishing and other scams, in which fraudsters manipulate people into disclosing personal information.
Yet the concept is older and more benign: it is the deliberate shaping of human behavior, often at scale. It predates silicon—and became pervasive, and ungoverned, especially once its practitioners learned to hide it. Authoritarian regimes and more recently scammers and big companies have profited from it. To defend ourselves from bad actors, and to benefit from social engineering’s good side, we need to reclaim the name, and govern it prudently.
In 1894, Dutch entrepreneur Jacques van Marken urged companies to hire “social engineers” to manage human systems such as insurance, education, and profit sharing for workers as carefully as they did mechanical ones. Fifteen years later, reformer William H. Tolman published Social Engineering, describing how U.S. industrialists optimized workers’ conditions alongside manufacturing methods. If industrialists could shape steel and electricity on demand, why not society itself?
By the 1920s, that confidence had spread. The architect Le Corbusier declared that dwellings were “machines for living in,” imagining cities as orderly lattices where people moved like parts on a conveyor belt. Civilization would run like a Swiss watch.
The idea soon darkened. Authoritarian regimes pushed it to extremes, promising to fashion “the New Man.” In Nazi Germany, engineer Fritz Todt founded Organization Todt, a vast state engineering enterprise that emerged from the autobahn highway system and later operated concentration camps using slave labor.
In the Soviet Union, leaders adopted U.S. scientific management techniques to plan factory-worker movements and classify populations through centralized records, feeding both rapid industrialization drives and the gulag system of forced labor. The same tools and managerial methods used to build highways and enact five-year plans worked for repression and mass control.
By the 1950s, “social engineering” had become a contaminated phrase. The revelations of Nazi and Soviet abuses, along with Cold War critiques of grand social planning turned the term from a progressive slogan into a warning label. Banishing the words pushed the practice underground, making it harder to recognize when it resurfaced in new forms—such as organizational psychology and systems management that still relied on classification and behavioral influence techniques but under softer, less loaded labels.
In the postwar years, the new social-engineering lexicon included “human factors” and “urban planning,” all promising integration rather than command. As computing advanced, the language shifted again: “customer journey mapping” to track interactions, “user experience” to script them. Engineering, which began as a means of reshaping physical space, set its sights on shaping behavior. Digital design features embedded in our smartphones now target our attention and desire.
Language helps conceal these modern forms of social engineering. “Data analytics” sounds neutral beside “surveillance.” “Personalization” flatters individuality while still sorting users into predictable categories. “Behavioral nudges” guide decisions without the sense of intrusion. We attach “social” as a favorable modifier to sciences, capital, and media, yet recoil when it meets “engineering.”
That discomfort is a clue. Engineering implies control, and control prompts us to ask who directs whom, toward what ends, and with whose permission.
Not all social engineering these days is hidden. Hackers don’t need to break a firewall if someone hands over their password. Romance scammers cultivate intimacy the way farmers cultivate crops. They succeed not through force but by exploiting trust. If even these obvious attacks work, the invisible kind, with roots in social engineering, are a shoo-in.
Most of the social engineering we encounter is proprietary and beyond our control. Firms build recommendation algorithms tuned to boost engagement and profit with no hearings or right of appeal. Browser and cookie defaults decide what data we surrender. A single autoplay toggle can cost users hours and build unhealthy habits. These are acts of engineering as deliberate as laying a road or redrawing an electoral district. They create a kind of curated itch by which boredom never settles, and satisfaction never arrives. The results are predictable—users click on targeted ads, make purchases, form habits, and lock in opinions.
Consent has transformed along with it. Once straightforward and revocable, it is now subtle and persistent, buried in defaults or opaque terms of service too quickly accepted. You remain free to opt out, much as you are free to refuse roads or electricity. Consent has become the preselected setting of modern life.
When social engineering operated more in the open, citizens could contest it, at least in societies with responsive government. Today’s invisible version diffuses accountability so thoroughly that scrutiny becomes hard to direct. Despite recent congressional hearings on social media’s impact on youth mental health and juries agreeing that firms are knowingly designing algorithms that cause harm, pinpointing responsibility remains elusive. When the mechanism is buried inside a system used by billions, we cannot easily point to a single decision-maker or trace the precise moment of manipulation.
Today’s social engineering is less overt and theatrical than its predecessors. Earlier versions arrived on public posters and loudspeakers for mass audiences. Today’s version is more intimate, delivered through personal devices and constant feeds tailored to the individual. The model succeeds because participation feels like freedom, not control.
Not all social engineering is dystopian. Well-kept parks foster community, accessible buildings extend dignity, vaccines and seatbelts save lives. Even in the digital realm, positive examples exist: browser extensions that automatically block hidden trackers, search engines that refuse to build personalized surveillance profiles, and decentralized social platforms that give users greater control over their own data and feeds.
The term “social engineering” still unsettles, though. But “asocial” engineering, which ignores human consequences entirely, is worse. Recognition of the human dimension to engineering is the beginning of repair. Only by seeing the machinery clearly and naming it honestly can we decide who engineers what and why. The machinery will not dismantle itself. Once named, it becomes subject to choice. That negotiation of purpose, power, and process are the defining political questions of any real democracy. We cannot ensure that social engineering serves and sustains society so long as we dodge the words.
2026-05-25 18:00:01
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