Taiwanese technology industry publication DigiTimes has some some great ‘state of the industry’ analysis pieces. They have placed Taiwan Semiconductor Manufacturing Corp. aka TSMC at the centre of this.
TSMC dossier (6): Possible ways to defeat the world’s top foundry house – TSMC’s concern is not getting surpassed in process technology or losing key customers but being overtaken by someone figuring out how to combine quantum technology and IC design. In-memory computing becoming a reality might be another scenario where TSMC could lose its competitive edge. However, according to Yu-Chung Lin, a DIGITIMES consultant devoted to the study of quantum technology application trends for years, large-scale commercialization of quantum technology is unlikely to happen before 2025. In-memory computing comes with high costs. If Samsung with leading-edge memory technologies really makes a breakthrough, TSMC will have to brace for challenges
Hong Kong’s national security trial debates Malcolm X’s legacy — Quartz – Leung argued that forceful and peaceful resistance can play complementary roles in a social movement, invoking Martin Luther King and Malcolm X as representing the two different styles of resistance in the US civil rights movement. In that 2016 interview, Leung also advocated for protesters to use a level of force that’s equal to that of the police. It’s an idea that evokes another line in Malcolm X’s “The Ballot or the Bullet” speech: “I’m non-violent with those who are non-violent with me.” – so the Hong Kong government is not only oppressing its own people but suggesting that African Americans should have been jailed as national security risks?
Luxury is culture now. Here’s how | Vogue Business – Vetements has teased a rebrand as a platform for young talent. Gucci spotlighted 15 emerging brands including Collina Strada and Bianca Saunders at November’s GucciFest film festival. Saint Laurent recently teamed up with Bang & Olufsen, Baccarat and JL Coquet on a lifestyle product series. Luxury brands no longer sell just their brand. In a new era of consumer expectation for shared values, brands are curating platforms to function as tastemakers to extend their own brand halo into the wider cultural world
Till Jagla adidas Collab King Exclusive Interview: Why He Left – “Now Energy is a new hype creation unit that really puts storytelling at the heart of everything we do,” says Jagla. “It was literally starting a brand within a brand. It was all the bad and ugly of the startup brands.”
GB News in crisis as exec quits and presenter is pulled for ‘taking the knee’ | The Guardian – GB News – set up on the basis that free and open debate is no longer possible in the mainstream media due to an all-pervasive “cancel culture”, was overwhelmed with complaints that after only a few weeks on air it had gone “woke” and its content was not sufficiently rightwing.
Pluralistic: 15 Jul 2021 – Pluralistic: Daily links from Cory Doctorow – interesting thread on how Facebook has disbursed its Crowdtangle analytics team. The reality is that social monitoring tools have been going dark for years. Most of them disproportionately rely on Twitter. Twitter has an outsized importance for its audience size due to it being favoured by journalists. Doctorow’s post however looks specifically at these Facebook moves
Philippines calls claim China influenced 2016 election to favour Duterte ‘nonsense’ | South China Morning Post – Former foreign secretary Albert del Rosario made the claim at a forum on Monday, citing a ‘most reliable international entity’. He said senior Chinese officials were ‘bragging that they had been able to influence the 2016 Philippine elections so that Duterte would be president’ – I wouldn’t be surprised if China did try to influence the election result. However I am surprised that Chinese officials would be so indiscreet. The risks of that getting back to Beijing are too great, unless they wanted to be found out
Threat to UK from hostile states could be as bad as terrorism, says MI5 chief | The Guardian – while the threat from Russia, as demonstrated by the poisoning of the Skripals in Salisbury, is familiar to the British public – the spy chief will argue that threats that typically come from China are not. McCallum will say that universities and researchers risk “having their discoveries stolen or copied” if they are not vigilant and that businesses could be “hollowed out by the loss of advantage they’ve worked painstakingly to build”.
HNA in chaos as internal divisions erupt in public | Financial Times – One investor who sought to buy a large real estate portfolio from HNA in late 2018 said that the deal fell through because it was no longer clear who was in control of the assets – this is interesting when you start about thinking allegations of all Chinese businesses (like Huawei) essentially being state-directed businesses. Especially when you consider it in the context
What really happens to the clothes you donate | Macleans – interesting complex supply chain for fibres and nothing. Also interesting how grading of garments stayed within the Asian diaspora formerly based in the British colonies of East Africa
My thinking on the concept of intelligence per watt started as bullets in my notebook. It was more of a timeline than anything else at first and provided a framework of sorts from which I could explore the concept of efficiency in terms of intelligence per watt.
TL;DR (too long, didn’t read)
Our path to the current state of ‘artificial intelligence’ (AI) has been shaped by the interplay and developments of telecommunications, wireless communications, materials science, manufacturing processes, mathematics, information theory and software engineering.
Progress in one area spurred advances in others, creating a feedback loop that propelled innovation.
Over time, new use cases have become more personal and portable – necessitating a focus on intelligence per watt as a key parameter. Energy consumption directly affects industrial design and end-user benefits. Small low-power integrated circuits (ICs) facilitated fuzzy logic in portable consumer electronics like cameras and portable CD players. Low power ICs and power management techniques also helped feature phones evolve into smartphones.
A second-order effect of optimising for intelligence per watt is reducing power consumption across multiple applications. This spurs yet more new use cases in a virtuous innovation circle. This continues until the laws of physics impose limits.
Energy storage density and consumption are fundamental constraints, driving the need for a focus on intelligence per watt.
As intelligence per watt improves, there will be a point at which the question isn’t just what AI can do, but what should be done with AI? And where should it be processed? Trust becomes less about emotional reassurance and more about operational discipline. Just because it can handle a task doesn’t mean it should – particularly in cases where data sensitivity, latency, or transparency to humans is non-negotiable. A highly capable, off-device AI might be a fine at drafting everyday emails, but a questionable choice for handling your online banking.
Good ‘operational security’ outweighs trust. The design of AI systems must therefore account not just for energy efficiency, but user utility and deployment context. The cost of misplaced trust is asymmetric and potentially irreversible.
Ironically the force multiplier in intelligence per watt is people and their use of ‘artificial intelligence’ as a tool or ‘co-pilot’. It promises to be an extension of the earlier memetic concept of a ‘bicycle for the mind’ that helped inspire early developments in the personal computer industry. The upside of an intelligence per watt focus is more personal, trusted services designed for everyday use.
While not a computer, but instead to integrate several radio parts in one glass envelope vacuum valve. This had three triodes (early electronic amplifiers), two capacitors and four resistors. Inside the valve the extra resistor and capacitor components went inside their own glass tubes. Normally each triode would be inside its own vacuum valve. At the time, German radio tax laws were based on the number of valve sockets in a device, making this integration financially advantageous.
Post-war scientific boom
Between 1949 and 1957 engineers and scientists from the UK, Germany, Japan and the US proposed what we’d think of as the integrated circuit (IC). These ideas were made possible when breakthroughs in manufacturing happened. Shockley Semiconductor built on work by Bell Labs and Sprague Electric Company to connect different types of components on the one piece of silicon to create the IC.
Credit is often given to Jack Kilby of Texas Instruments as the inventor of the integrated circuit. But that depends how you define IC, with what is now called a monolithic IC being considered a ‘true’ one. Kilby’s version wasn’t a true monolithic IC. As with most inventions it is usually the child of several interconnected ideas that coalesce over a given part in time. In the case of ICs, it was happening in the midst of materials and technology developments including data storage and computational solutions such as the idea of virtual memory through to the first solar cells.
Kirby’s ICs went into an Air Force computer[ii] and an onboard guidance system for the Minuteman missile. He went on to help invent the first handheld calculator and thermal printer, both of which took advantage of progress in IC design to change our modern way of life[iii].
TTL (transistor-to-transistor logic) circuitry was invented at TRW in 1961, they licensed it out for use in data processing and communications – propelling the development of modern computing. TTL circuits powered mainframes. Mainframes were housed in specialised temperature and humidity-controlled rooms and owned by large corporates and governments. Modern banking and payments systems rely on the mainframe as a concept.
AI’s early steps
What we now thing of as AI had been considered theoretically for as long as computers could be programmed. As semiconductors developed, a parallel track opened up to move AI beyond being a theoretical possibility. A pivotal moment was a workshop was held in 1956 at Dartmouth College. The workshop focused on a hypothesis ‘every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it’. Later on, that year a meeting at MIT (Massachusetts Institute of Technology) brought together psychologists and linguists to discuss the possibility of simulating cognitive processes using a computer. This is the origin of what we’d now call cognitive science.
Out of the cognitive approach came some early successes in the move towards artificial intelligence[iv]. A number of approaches were taken based on what is now called symbolic or classical AI:
Reasoning as search – essentially step-wise trial and error approach to problem solving that was compared to wandering through a maze and back-tracking if a dead end was found.
Natural language – where related phrases existed within a structured network.
Micro-worlds – solving for artificially simple situations, similar to economic models relying on the concept of the rational consumer.
Single layer neural networks – to do rudimentary image recognition.
By the time the early 1970s came around AI researchers ran into a number of problems, some of which still plague the field to this day:
Symbolic AI wasn’t fit for purpose solving many real-world tasks like crossing a crowded room.
Trying to capture imprecise concepts with precise language.
Commonsense knowledge was vast and difficult to encode.
Intractability – many problems require an exponential amount of computing time.
Limited computing power available – there was insufficient intelligence per watt available for all but the simplest problems.
By 1966, US and UK funding bodies were frustrated with the lack of progress on the research undertaken. The axe fell first on a project to use computers on language translation. Around the time of the OPEC oil crisis, funding to major centres researching AI was reduced by both the US and UK governments respectively. Despite the reduction of funding to the major centres, work continued elsewhere.
Mini-computers and pocket calculators
ICs allowed for mini-computers due to the increase in computing power per watt. As important as the relative computing power, ICs made mini-computers more robust, easier to manufacture and maintain. DEC (Digital Equipment Corporation) launched the first minicomputer, the PDP-8 in 1964. The cost of mini-computers allowed them to run manufacturing processes, control telephone network switching and control labouratory equipment. Mini-computers expanded computer access in academia facilitating more work in artificial life and what we’d think of as early artificial intelligence. This shift laid the groundwork for intelligence per watt as a guiding principle.
A second development helped drive mass production of ICs – the pocket calculator, originally invented at Texas Instruments. It demonstrated how ICs could dramatically improve efficiency in compact, low-power devices.
LISP machines and PCs
AI researchers required more computational power than mini-computers could provide, leading to the development of LISP machines—specialised workstations designed for AI applications. Despite improvements in intelligence per watt enabled by Moore’s Law, their specialised nature meant that they were expensive. AI researchers continued with these machines until personal computers (PCs) progressed to a point that they could run LISP quicker than LISP machines themselves. The continuous improvements in data storage, memory and processing that enabled LISP machines, continued on and surpassed them as the cost of computing dropped due to mass production.
The rise of LISP machines and their decline was not only due to Moore’s Law in effect, but also that of Makimoto’s Wave. While Gordon Moore outlined an observation that the number of transistors on a given area of silicon doubled every two years or so. Tsugio Makimoto originally observed 10-year pivots from standardised semiconductor processors to customised processors[v]. The rise of personal computing drove a pivot towards standardised architectures.
PCs and workstations extended computing beyond computer rooms and labouratories to offices and production lines. During the late 1970s and 1980s standardised processor designs like the Zilog Z80, MOS Technology 6502 and the Motorola 68000 series drove home and business computing alongside Intel’s X86 processors.
Personal computing started in businesses when office workers brought a computer to use early computer programmes like the VisiCalc spreadsheet application. This allowed them to take a leap forward in not only tabulating data, but also seeing how changes to the business might affect financial performance.
Businesses then started to invest more in PCs for a wide range of uses. PCs could emulate the computer terminal of a mainframe or minicomputer, but also run applications of their own.
Typewriters were being placed by word processors that allowed the operator to edit a document in real time without resorting to using correction fluid.
A Bicycle for the Mind
Steve Jobs at Apple was as famous for being a storyteller as he was for being a technologist in the broadest sense. Internally with the Mac team he shared stories and memetic concepts to get his ideas across in everything from briefing product teams to press interviews. As a concept, a 1990 filmed interview with Steve Jobs articulates the context of this saying particularly well.
In reality, Jobs had been telling the story for a long time through the development of the Apple II and right from the beginning of the Mac. There is a version of the talk that was recorded some time in 1980 when the personal computer was still a very new idea – the video was provided to the Computer History Museum by Regis McKenna[vi].
The ‘bicycle for the mind’ concept was repeated in early Apple advertisements for the time[vii] and even informed the Macintosh project codename[viii].
Jobs articulated a few key concepts.
Buying a computer creates, rather than reduces problems. You needed software to start solving problems and making computing accessible. Back in 1980, you programmed a computer if you bought one. Which was the reason why early personal computer owners in the UK went on to birth a thriving games software industry including the likes of Codemasters[ix]. Done well, there should be no seem in the experience between hardware and software.
The idea of a personal, individual computing device (rather than a shared resource). My own computer builds on my years of how I have grown to adapt and use my Macs, from my first sit-up and beg Macintosh, to the MacBook Pro that I am writing this post on. This is even more true most people and their use of the smartphone. I am of an age, where my iPhone is still an appendage and emissary of my Mac. My Mac is still my primary creative tool. A personal computer is more powerful than a shared computer in terms of the real difference made.
At the time Jobs originally did the speech, PCs were underpowered for anything but data processing (through spreadsheets and basic word processor applications). But that didn’t stop his idea for something greater.
Jobs idea of the computer as an adjunct to the human intellect and imagination still holds true, but it doesn’t neatly fit into the intelligence per watt paradigm. It is harder to measure the effort developing prompts, or that expended evaluating, refining and filtering generative AI results. Of course, Steve Jobs Apple owed a lot to the vision shown in Doug Engelbart’s ‘Mother of All Demos’[x].
Networks
Work took a leap forward with office networked computers pioneered by Macintosh office by Apple[xi]. This was soon overtaken by competitors. This facilitated work flow within an office and its impact can still be seen in offices today, even as components from print management to file storage have moved to cloud-based services.
At the same time, what we might think of as mobile was starting to gain momentum. Bell Labs and Motorola came up with much of the technology to create cellular communications. Martin Cooper of Motorola made the first phone call on a cellular phone to a rival researcher at Bell Labs. But Motorola didn’t sell the phone commercially until 1983, as a US-only product called the DynaTAC 8000x[xii]. This was four years after Japanese telecoms company NTT launched their first cellular network for car phones. Commercial cellular networks were running in Scandinavia by 1981[xiii].
In the same way that the networked office radically changed white collar work, the cellular network did a similar thing for self-employed plumbers, electricians and photocopy repair men to travelling sales people. If they were technologically advanced, they may have had an answer machine, but it would likely have to be checked manually by playing back the tape.
Often it was a receptionist in their office if they had one. Or more likely, someone back home who took messages. The cell phone freed homemakers in a lot of self-employed households to go out into the workplace and helped raise household incomes.
Fuzzy logic
The first mainstream AI applications emerged from fuzzy logic, introduced by Lofti A. Zadeh in 1965 mathematical paper. Initial uses were for industrial controls in cement kilns and steel production[xiv]. The first prominent product to rely on fuzzy logic was the Zojirushi Micom Electric Rice Cooker (1983), which adjusted cooking time dynamically to ensure perfect rice.
Fuzzy logic reacted to changing conditions in a similar way to people. Through the 1980s and well into the 1990s, the power of fuzzy logic was under appreciated outside of Japanese product development teams. In a quote a spokesperson for the American Electronics Association’s Tokyo office said to the Washington Post[xv].
“Some of the fuzzy concepts may be valid in the U.S.,”
“The idea of better energy efficiency, or more precise heating and cooling, can be successful in the American market,”
“But I don’t think most Americans want a vacuum cleaner that talks to you and says, ‘Hey, I sense that my dust bag will be full before we finish this room.’ “
The end of the 1990s, fuzzy logic was embedded in various consumer devices:
Air-conditioner units – understands the room, the temperature difference inside-and-out, humidity. It then switches on-and-off to balance cooling and energy efficiency.
CD players – enhanced error correction on playback dealing with imperfections on the disc surface.
Dishwashers – understood how many dishes were loaded, their type of dirt and then adjusts the wash programme.
Toasters – recognised different bread types, the preferable degree of toasting and performs accordingly.
TV sets – adjust the screen brightness to the ambient light of the room and the sound volume to how far away the viewer is sitting from the TV set.
Vacuum cleaners – vacuum power that is adjusted as it moves from carpeted to hard floors.
Video cameras – compensate for the movement of the camera to reduce blurred images.
Fuzzy logic sold on the benefits and concealed the technology from western consumers. Fuzzy logic embedded intelligence in the devices. Because it worked on relatively simple dedicated purposes it could rely on small lower power specialist chips[xvi] offering a reasonable amount of intelligence per watt, some three decades before generative AI. By the late 1990s, kitchen appliances like rice cookers and microwave ovens reached ‘peak intelligence’ for what they needed to do, based on the power of fuzzy logic[xvii].
Fuzzy logic also helped in business automation. It helped to automatically read hand-written numbers on cheques in banking systems and the postcodes on letters and parcels for the Royal Mail.
Decision support systems & AI in business
Decision support systems or Business Information Systems were being used in large corporates by the early 1990s. The techniques used were varied but some used rules-based systems. These were used in at least some capacity to reduce manual office work tasks. For instance, credit card approvals were processed based on rules that included various factors including credit scores. Only some credit card providers had an analyst manually review the decision made by system. However, setting up each use case took a lot of effort involving highly-paid consultants and expensive software tools. Even then, vendors of business information systems such as Autonomy struggled with a high rate of projects that failed to deliver anything like the benefits promised.
Three decades on, IBM had a similar problem with its Watson offerings, with particularly high-profile failure in mission-critical healthcare applications[xviii]. Secondly, a lot of tasks were ad-hoc in nature, or might require transposing across disparate separate systems.
The rise of the web
The web changed everything. The underlying technology allowed for dynamic data.
Software agents
Examples of intelligence within the network included early software agents. A good example of this was PapriCom. PapriCom had a client on the user’s computer. The software client monitored price changes for products that the customer was interested in buying. The app then notified the user when the monitored price reached a price determined by the customer. The company became known as DealTime in the US and UK, or Evenbetter.com in Germany[xix].
The PapriCom client app was part of a wider set of technologies known as ‘push technology’ which brought content that the netizen would want directly to their computer. In a similar way to mobile app notifications now.
Web search
The wealth of information quickly outstripped netizen’s ability to explore the content. Search engines became essential for navigating the new online world. Progress was made in clustering vast amounts of cheap Linux powered computers together and sharing the workload to power web search amongst them. As search started to trying and make sense of an exponentially growing web, machine learning became part of the developer tool box.
Researchers at Carnegie-Mellon looked at using games to help teach machine learning algorithms based on human responses that provided rich metadata about the given item[xx]. This became known as the ESP game. In the early 2000s, Yahoo! turned to web 2.0 start-ups that used user-generated labels called tags[xxi] to help organise their data. Yahoo! bought Flickr[xxii] and deli.ico.us[xxiii].
All the major search engines looked at how deep learning could help improve search results relevance.
Given that the business model for web search was an advertising-based model, reducing the cost per search, while maintaining search quality was key to Google’s success. Early on Google focused on energy consumption, with its (search) data centres becoming carbon neutral in 2007[xxiv]. This was achieved by a whole-system effort: carefully managing power management in the silicon, storage, networking equipment and air conditioning to maximise for intelligence per watt. All of which were made using optimised versions of open-source software and cheap general purpose PC components ganged together in racks and operating together in clusters.
General purpose ICs for personal computers and consumer electronics allowed easy access relatively low power computing. Much of this was down to process improvements that were being made at the time. You needed the volume of chips to drive innovation in mass-production at a chip foundry. While application-specific chips had their uses, commodity mass-volume products for uses for everything from embedded applications to early mobile / portable devices and computers drove progress in improving intelligence-per-watt.
Makimoto’s tsunami back to specialised ICs
When I talked about the decline of LISP machines, I mentioned the move towards standardised IC design predicted by Tsugio Makimoto. This led to a surge in IC production, alongside other components including flash and RAM memory. From the mid-1990s to about 2010, Makimoto’s predicted phase was stuck in ‘standardisation’. It just worked. But several factors drove the swing back to specialised ICs.
Lithography processes got harder: standardisation got its performance and intelligence per watt bump because there had been a steady step change in improvements in foundry lithography processes that allowed components to be made at ever-smaller dimensions. The dimensions are a function wavelength of light used. The semiconductor hit an impasse when it needed to move to EUV (extreme ultra violet) light sources. From the early 1990s on US government research projects championed development of key technologies that allow EUV photolithography[xxv]. During this time Japanese equipment vendors Nikon and Canon gave up on EUV. Sole US vendor SVG (Silicon Valley Group) was acquired by ASML, giving the Dutch company a global monopoly on cutting edge lithography equipment[xxvi]. ASML became the US Department of Energy research partner on EUV photo-lithography development[xxvii]. ASML spent over two decades trying to get EUV to work. Once they had it in client foundries further time was needed to get commercial levels of production up and running. All of which meant that production processes to improve IC intelligence per watt slowed down and IC manufacturers had to start about systems in a more holistic manner. As foundry development became harder, there was a rise in fabless chip businesses. Alongside the fabless firms, there were fewer foundries: Global Foundries, Samsung and TSMC (Taiwan Semiconductor Manufacturing Company Limited). TSMC is the worlds largest ‘pure-play’ foundry making ICs for companies including AMD, Apple, Nvidia and Qualcomm.
Progress in EDA (electronic design automation). Production process improvements in IC manufacture allowed for an explosion in device complexity as the number of components on a given size of IC doubled every 18 months or so. In the mid-to-late 1970s this led to technologists thinking about the idea of very large-scale integration (VLSI) within IC designs[xxviii]. Through the 1980s, commercial EDA software businesses were formed. The EDA market grew because it facilitated the continual scaling of semiconductor technology[xxix]. Secondly, it facilitated new business models. Businesses like ARM Semiconductor and LSI Logic allowed their customers to build their own processors based on ‘blocs’ of proprietary designs like ARM’s cores. That allowed companies like Apple to focus on optimisation in their customer silicon and integration with software to help improve the intelligence per watt[xxx].
Increased focus on portable devices. A combination of digital networks, wireless connectivity, the web as a communications platform with universal standards, flat screen displays and improving battery technology led the way in moving towards more portable technologies. From personal digital assistants, MP3 players and smartphone, to laptop and tablet computers – disconnected mobile computing was the clear direction of travel. Cell phones offered days of battery life; the Palm Pilot PDA had a battery life allowing for couple of days of continuous use[xxxi]. In reality it would do a month or so of work. Laptops at the time could do half a day’s work when disconnected from a power supply. Manufacturers like Dell and HP provided spare batteries for travellers. Given changing behaviours Apple wanted laptops that were easy to carry and could last most of a day without a charge. This was partly driven by a move to a cleaner product design that wanted to move away from swapping batteries. In 2005, Apple moved from PowerPC to Intel processors. During the announcement at the company’s worldwide developer conference (WWDC), Steve Jobs talked about the focus on computing power per watt moving forwards[xxxii].
Apple’s first in-house designed IC, the A4 processor was launched in 2010 and marked the pivot of Makimoto’s wave back to specialised processor design[xxxiii]. This marked a point of inflection in the growth of smartphones and specialised computing ICs[xxxiv].
New devices also meant new use cases that melded data on the web, on device, and in the real world. I started to see this in action working at Yahoo! with location data integrated on to photos and social data like Yahoo! Research’s ZoneTag and Flickr. I had been the Yahoo! Europe marketing contact on adding Flickr support to Nokia N-series ‘multimedia computers’ (what we’d now call smartphones), starting with the Nokia N73[xxxv]. A year later the Nokia N95 was the first smartphone released with a built-in GPS receiver. William Gibson’s speculative fiction story Spook Country came out in 2007 and integrated locative art as a concept in the story[xxxvi].
Real-world QRcodes helped connect online services with the real world, such as mobile payments or reading content online like a restaurant menu or a property listing[xxxvii].
I labelled the web-world integration as a ‘web-of-no-web’[xxxviii] when I presented on it back in 2008 as part of an interactive media module, I taught to an executive MBA class at Universitat Ramon Llull in Barcelona[xxxix]. In China, wireless payment ideas would come to be labelled O2O (offline to online) and Kevin Kelly articulated a future vision for this fusion which he called Mirrorworld[xl].
Deep learning boom
Even as there was a post-LISP machine dip in funding of AI research, work on deep (multi-layered) neural networks continued through the 1980s. Other areas were explored in academia during the 1990s and early 2000s due to the large amount of computing power needed. Internet companies like Google gained experience in large clustered computing, AND, had a real need to explore deep learning. Use cases include image recognition to improve search and dynamically altered journeys to improve mapping and local search offerings. Deep learning is probabilistic in nature, which dovetailed nicely with prior work Microsoft Research had been doing since the 1980s on Bayesian approaches to problem-solving[xli].
A key factor in deep learning’s adoption was having access to powerful enough GPUs to handle the neural network compute[xlii]. This has allowed various vendors to build Large Language Models (LLMs). The perceived strategic importance of artificial intelligence has meant that considerations on intelligence per watt has become a tertiary consideration at best. Microsoft has shown interest in growing data centres with less thought has been given on the electrical infrastructure required[xliii].
Google’s conference paper on attention mechanisms[xliv] highlighted the development of the transformer model. As an architecture it got around problems in previous approaches, but is computationally intensive. Even before the paper was published, the Google transformer model had created fictional Wikipedia entries[xlv]. A year later OpenAI built on Google’s work with the generative pre-trained transformer model better known as GPT[xlvi].
Since 2018 we’ve seen successive GPT-based models from Amazon, Anthropic, Google, Meta, Alibaba, Tencent, Manus and DeepSeek. All of these models were trained on vast amounts of information sources. One of the key limitations for building better models was access to training material, which is why Meta used pirated copies of e-books obtained using bit-torrent[xlvii].
These models were so computationally intensive that the large-scale cloud service providers (CSPs) offering these generative AI services were looking at nuclear power access for their data centres[xlviii].
The current direction of development in generative AI services is raw computing power, rather than having a more energy efficient focus of intelligence per watt.
Technology consultancy / analyst Omdia estimated how many GPUs were bought by hyperscalers in 2024[xlix].
Company
Number of Nvidia GPUs bought
Number of AMD GPUs bought
Number of self-designed custom processing chips bought
Amazon
196,000
–
1,300,000
Alphabet (Google)
169,000
–
1,500,000
ByteDance
230,000
–
–
Meta
224,000
173,000
1,500,000
Microsoft
485,000
96,000
200,000
Tencent
230,000
–
–
These numbers provide an indication of the massive deployment on GPT-specific computing power. Despite the massive amount of computing power available, services still weren’t able to cope[l] mirroring some of the service problems experienced by early web users[li] and the Twitter ‘whale FAIL’[lii] phenomenon of the mid-2000s. The race to bigger, more powerful models is likely to continue for the foreseeable future[liii].
There is a second class of players typified by Chinese companies DeepSeek[liv] and Manus[lv] that look to optimise the use of older GPT models to squeeze the most utility out of them in a more efficient manner. Both of these services still rely on large cloud computing facilities to answer queries and perform tasks.
Agentic AI
Thinking on software agents went back to work being done in computer science in the mid-1970s[lvi]. Apple articulated a view[lvii]of a future system dubbed the ‘Knowledge Navigator’[lviii] in 1987 which hinted at autonomous software agents. What we’d now think of as agentic AI was discussed as a concept at least as far back as 1995[lix], this was mirrored in research labs around the world and was captured in a 1997 survey of research on intelligent software agents was published[lx]. These agents went beyond the vision that PapriCom implemented.
A classic example of this was Wildfire Communications, Inc. who created a voice enabled virtual personal assistant in 1994[lxi]. Wildfire as a service was eventually shut down in 2005 due to an apparent decline in subscribers using the service[lxii]. In terms of capability, Wildfire could do tasks that are currently beyond Apple’s Siri. Wildfire did have limitations due to it being an off-device service that used a phone call rather than an internet connection, which limited its use to Orange mobile service subscribers using early digital cellular mobile networks.
Almost a quarter century later we’re now seeing devices that are looking to go beyond Wildfire with varying degrees of success. For instance, the Rabbit R1 could order an Uber ride or groceries from DoorDash[lxiii]. Google Duplex tries to call restaurants on your behalf to make reservations[lxiv] and Amazon claims that it can shop across other websites on your behalf[lxv]. At the more extreme end is Boeing’s MQ-28[lxvi] and the Loyal Wingman programme[lxvii]. The MQ-28 is an autonomous drone that would accompany US combat aircraft into battle, once it’s been directed to follow a course of action by its human colleague in another plane.
The MQ-28 will likely operate in an electronic environment that could be jammed. Even if it wasn’t jammed the length of time taken to beam AI instructions to the aircraft would negatively impact aircraft performance. So, it is likely to have a large amount of on-board computing power. As with any aircraft, the size of computing resources and their power is a trade-off with the amount of fuel or payload it will carry. So, efficiency in terms of intelligence per watt becomes important to develop the smallest, lightest autonomous pilot.
As well as a more hostile world, we also exist in a more vulnerable time in terms of cyber security and privacy. It makes sense to have critical, more private AI tasks run on a local machine. At the moment models like DeepSeek can run natively on a top-of-the-range Mac workstation with enough memory[lxviii].
This is still a long way from the vision of completely local execution of ‘agentic AI’ on a mobile device because the intelligence per watt hasn’t scaled down to that level to useful given the vast amount of possible uses that would be asked of the Agentic AI model.
Maximising intelligence per watt
There are three broad approaches to maximise the intelligence per watt of an AI model.
Take advantage of the technium. The technium is an idea popularised by author Kevin Kelly[lxix]. Kelly argues that technology moves forward inexorably, each development building on the last. Current LLMs such as ChatGPT and Google Gemini take advantage of the ongoing technium in hardware development including high-speed computer memory and high-performance graphics processing units (GPU). They have been building large data centres to run their models in. They build on past developments in distributed computing going all the way back to the 1962[lxx].
Optimise models to squeeze the most performance out of them. The approach taken by some of the Chinese models has been to optimise the technology just behind the leading-edge work done by the likes of Google, OpenAI and Anthropic. The optimisation may use both LLMs[lxxi] and quantum computing[lxxii] – I don’t know about the veracity of either claim.
Specialised models. Developing models by use case can reduce the size of the model and improve the applied intelligence per watt. Classic examples of this would be fuzzy logic used for the past four decades in consumer electronics to Mistral AI[lxxiii] and Anduril’s Copperhead underwater drone family[lxxiv].
Even if an AI model can do something, should the model be asked to do so?
We have a clear direction of travel over the decades to more powerful, portable computing devices –which could function as an extension of their user once intelligence per watt allows it to be run locally.
Having an AI run on a cloud service makes sense where you are on a robust internet connection, such as using the wi-fi network at home. This makes sense for general everyday task with no information risk, for instance helping you complete a newspaper crossword if there is an answer you are stuck on and the intellectual struggle has gone nowhere.
A private cloud AI service would make sense when working, accessing or processing data held on the service. Examples of this would be Google’s Vertex AI offering[lxxv].
On-device AI models make sense in working with one’s personal private details such as family photographs, health information or accessing apps within your device. Apps like Strava which share data, have been shown to have privacy[lxxvi] and security[lxxvii] implications. ***I am using Strava as an example because it is popular and widely-known, not because it is a bad app per se.***
While businesses have the capability and resources to have a multi-layered security infrastructure to protect their data most[lxxviii]of[lxxix] the[lxxx] time[lxxxi], individuals don’t have the same security. As I write this there are privacy concerns[lxxxii] expressed about Waymo’s autonomous taxis. However, their mobile device is rarely out of physical reach and for many their laptop or tablet is similarly close. All of these devices tend to be used in concert with each other. So, for consumers having an on-device AI model makes the most sense. All of which results in a problem, how do technologists squeeze down their most complex models inside a laptop, tablet or smartphone?
I took this picture almost two decades ago on a visit to Hong Kong of ghost signs.
I was reminded of this picture when I watched the below documentary on ghost signs. Specifically it reminded me of the former industrial units I saw in Fotan, which is in the New Territories of Hong Kong. Their structure used as a giant billboard advertising their former uses making fur coats or plastic flowers. The ghost signs of Hong Kong were fast-fading evidence of an industrial golden age in Hong Kong extinguished by China’s entrance into the World Trade Organisation (WTO) at the end of 2001.
The UK ghost signs highlighted in the documentary benefit from a slower rate of building replacement and a more temperate climate that helped preserve lead paint over a century old.
Ghost signs show that history is all around us, if we care to look around us.
Volkswagen China CMO deported from China for drug use | News | Campaign Asia – Volkswagen Group China’s chief marketing officer, Jochen Sengpiehl, has been expelled from China following a positive drug test upon his return from a holiday in Thailand. This development has caught significant attention on Chinese social media, as reported by the German tabloid Bild-Zeitung. AFP reported that German officials confirmed the news on Tuesday. Sengpiehl was detained for over 10 days and instructed to leave the country immediately after Chinese officials detected traces of cannabis and cocaine in his blood, according to AFP’s coverage. He was held in custody before Volkswagen and officials from the German embassy managed to secure his release. However, he was required to leave the country instantly, as reported by Bild. Campaign Asia-Pacific reached out to Volkswagen Group for comment. A global spokesperson offered a terse response: “We ask for your understanding that we will not comment further on the content of your questions in light of our contractual and data protection confidentiality obligations.” The incident throws a harsh spotlight on the differing legal landscapes around drug use. While Germany legalised cannabis use earlier this year, and Thailand became the first Asian country to decriminalise it for medical purposes in 2022 (though recreational use is slated for prohibition by the end of the year), China maintains extremely strict anti-narcotics laws, with severe penalties for violations. – This also says a lot about how little China needs Volkswagen in the country now.
Gen Z’s joy in chaos: Why maximalism is back | Jing Daily – at odds with the sleek pared down looks currently driving Chinese fashion. Not really that much of a surprise given how young people over the years have rated thrift shops, army surplus stores and shopped while travelling in search of authenticity and a story behind their eclecticism.
54: Double 11 (Is Ralph Lauren a victim?), The fall of Will’s and ClassPass | Following the Yuan – Chinese consumers using returns policies to hit ‘boycotted’ western companies in the pocket by exploiting the elevated business costs of returns in e-commerce. Double 11 or singles day is one of the premier shopping days in China. If this movement is real, the results for targeted brands like Ralph Lauren would be exceptionally brutal.
Culture
Camcorders are now going through a ‘lomography‘ phase now – where creators love their limitations and flaws.
Luxury beliefs is a term that I came across from the writings of Rob Henderson. Henderson has a similar kind of story to JD Vance. Addiction in the family and escaping his home environment by enlisting in the US Air Force.
After his service Henderson used funding via the GI Bill to go to Yale. He then got a scholarship to go to Cambridge to do a doctorate. Like Vance he had written a memoir: Troubled: A Memoir of Foster Care, Family, and Social Class that highlights the challenges faced in working class American society including violence and addiction. In his book Henderson explores the idea of luxury beliefs, how they benefit the privileged and harm the most vulnerable in society.
What are examples of luxury beliefs?
The luxury beliefs Henderson cites are seen to be widely held progressive views including:
Defunding the police
Defunding the prison system
Decriminalising or legalising drugs
Getting rid of standardised exams – Henderson sees these as helping less privileged children get into college
Rejecting marriage as a pointless concept. – Henderson claims that one of the strongest predictors of success was if they were brought up in a nuclear family.
Henderson believes that the common thread that holds luxury beliefs together is that they are held by privileged people, the beliefs make them look good (and feel good about themselves), but harm the marginalised.
Luxury beliefs allow the privileged to look good by:
Playing the victim
Protest without penalty – which is less likely to happen to more marginalised protestors
Push the less privileged down
Henderson labelled this ‘saviour theatre’. Henderson reminded of previous generation protestors like Patty Hearst and participants in the Weather Underground’s Days of Rage which would seem to fit Henderson’s definition of holding luxury beliefs.
The store, which recently went viral on Chinese social media platform Xiaohongshu, not only sells house-made essential oils – must-have souvenirs for visitors from mainland China thanks to the exposure – but recreates the signature scents of popular malls and other venues in Hong Kong.
On its shelves are familiar – sometimes odd – concoctions. Bottle labels reference K11, a shopping mall in Tsim Sha Tsui, the five-star Rosewood Hotel, and the Hong Kong International Airport. Sportswear brand Lululemon has one too.
The consequences of the psychoboom are both logical and contradictory. As the Chinese economy has expanded and citizens have grown wealthier, the demands of everyday life have grown in number and kind, expanding from physiological and safety concerns to a desire for love, esteem, and self-actualization. At the same time, such desires run counter to traditional Chinese values like the age-old concept of Confucian filial piety and the relatively new ideology imposed by the Chinese Communist Party (CCP), both of which place the well-being of the collective above the happiness of the individual.
Morgan Stanley, Goldman Sachs Say Recovery in Private Equity Deals and Fees – Bloomberg – Morgan Stanley and Goldman Sachs Group Inc. are confident that their most important clients are about to get active after a long spell on the sidelines and help goose the long-awaited revival in investment banking fees. The private equity deal machine has been mostly jammed up for the past two years, leaving many investment bankers twiddling their thumbs while their bosses talked up green shoots that failed to flourish. There are plenty of potential road bumps ahead, but there’s reason to put more weight on the better outlook now even compared with just three months ago: The wave of debt refinancing that has led banks’ revenue recovery this year has also been helping to fix the prospects of many companies owned by private equity firms
Sony is killing off recordable Blu-ray, bidding farewell to disc burning | TechSpot – Sony admitted it’s going to “gradually end development and production” of recordable Blu-rays and other optical disc formats at its Tagajo City plants in Miyagi Prefecture, Japan. Essentially, 25GB BD-REs, 50GB BD-RE DLs, 100GB BD-RE XLs, or 128GB BD-R XLs will soon not be available to consumers. Professional discs for video production and optical archives for data storage are also being discontinued. – the big shocker is the issue for archival formats
“When launching products back then, we didn’t have to have a profit timeline for them,” said a former longtime devices executive. “We had to get the system in people’s homes and we’d win. Innovate, and then figure out how to make money later.”
To do that, the team had to keep prices low. Amazon sometimes even gave away versions of the smart speaker as part of promotions in a bid to get a larger base of users.
Another Danish biotech can help investors’ hunger for obesity drugs | FT – this probably explains why Zealand pivoted from taking its medications to market to becoming research and selling on as its not big enough to exploit this opportunity on its own. (Full disclosure, I worked briefly on the diabetic emergency injection product until the company pivoted).
Age of Ozempic: Predictions for the luxury industry | Vogue Business – Analysts agree that the pop culture influence of weight loss drugs is giving luxury labels and mass-market brands, alike, licence to refocus on straight-size. “Luxury brands have long been staunchly unwilling to cater to plus-sizes outside of the occasional token representation, but typically premium and mass players would invest more readily in plus-size,” says Marci. “Now we’re seeing the effects of Ozempic and weight loss culture on retail as a whole.”
Already, a host of US-based retailers and fashion companies including Rent the Runway are seeing boosted demand for smaller clothing sizes, and falling demand for larger sizes, according to The Wall Street Journal. Retailers have been investing in fewer products that offer larger sizing
EssilorLuxottica expands into streetwear with $1.5bn Supreme deal – the deal was a “no brainer” and had happened “very quickly” because VF was under pressure to divest its most “iconic asset”. EssilorLuxottica planned to use Supreme’s wealth of customer data and its Gen Z fans in China, Japan and South Korea to target new consumers – it shows how good a deal James Jebbia got with private equity and VF Corporation
Lewis Hamilton Named Dior Ambassador | BoF – formula 1 driver and pit lane dandy has also worked with Dior men’s artistic director Kim Jones to guest design a collection of clothing and accessories set to launch in October
Even Disinformation Experts Don’t Know How to Stop It | New York Times – Researchers have learned a great deal about the misinformation problem over the past decade: They know what types of toxic content are most common, the motivations and mechanisms that help it spread and who it often targets. The question that remains is how to stop it.
A critical mass of research now suggests that tools such as fact checks, warning labels, prebunking and media literacy are less effective and expansive than imagined, especially as they move from pristine academic experiments into the messy, fast-changing public sphere.
