Hydrogen power and hydrogen fuel cells have been around for decades. Hydrogen power fuel cells as an invention were invented in the 19th century. The modern hydrogen fuel cell was refined before the second world war and have been used in NASA’s space programme since Project Apollo. The space programme’s use of hydrogen power inspired General Motors to create a hydrogen fuel cell powered van in 1966. By the late 1980s, BMW had developed a hydrogen-powered engine which it trialled in its 7-series vehicles a decade later.
By the mid-2010s there were four hydrogen power passenger cars using fuel cells: Honda Clarity, Toyota Mirai, Hyundai ix35 FCEV, and the Hyundai Nexo. BMW is collaborating with Toyota to launch another four models next year.
In addition in commercial vehicles and heavy plant Hyundai, Cummins and JCB have hydrogen power offerings. JCB and Cummins have focused on internal combustion engines, while Hyundai went with hydrogen fuel cells. The aviation industry has been looking to hydrogen power via jet turbines.
Hydrogen power offers greater energy density and lower weight than batteries. Unlike batteries or power lines, hydrogen can be transported over longer distances via tanker. So someplace like Ireland with wind and tidal power potential could become an energy exporter.
The key hydrogen power problem has been investment in infrastructure and an over-reliance on batteries. Batteries bring their own set of problems and a global strategic dependency on China.
Toyota is now warning that if there isn’t imminent international investment, that China will also dominate the supply chain, exports and energy generation in the hydrogen economy as well. It feels like me reaching a historic point of no return.
Skincare you can wear: China’s sunwear boom | Jing Daily – A jacket with a wide-brim hood and built-in face shield. Leggings infused with hyaluronic acid to hydrate while shielding skin from the sun. Face masks with chin-to-temple coverage. Ice-cooling gloves designed to drop skin temperature. In China, UV protection apparel isn’t just functional — it’s fashionable, dermatological, and high-tech. Once a niche category for hikers or extreme sports enthusiasts, China’s sunwear market has exploded into a $13 billion category blending climate adaptation, anti-aging culture, and the outdoor lifestyle wave. While other apparel segments slow, the sunwear sector is projected to reach nearly 95.8 billion RMB ($13.5 billion) by 2026 expanding at a CAGR of 9%, according to iResearch.
MACAU DAILY TIMES 澳門每日時報Beware of Li Kashing’s supersized value trap – But as the initial excitement starts to fade, investors are growing nervous, wary of a billionaire family that has a poor track record on shareholders’ returns. The Li clan takes pride in the myriad of businesses and markets it operates in. But what kind of value-add can a diversified conglomerate offer when globalization is out of favor and geopolitical risks are on the rise? CK’s de-rating has accelerated since Trump’s first term, with the stock trading at just 35% of its book value even after the recent share bump. The complex business dealings have made enterprise valuation an impossible task. In a sign of deep capital market skepticism, CK seems to have trouble monetizing its assets. Its health and beauty subsidiary A.S. Watson is still privately-held, a decade after postponing an ambitious $6 billion dual listing in Hong Kong and London. Softer consumer sentiment in China, once a growth market, has become a drag. Last summer, CK Infrastructure did a secondary listing in the UK, hoping to widen its investor base. – Rare direct criticism of CK Hutchison’s conglomerate discount.
Ghost in the Shell’s Creator Wants to Revisit the Anime, But There’s One Problem – Production I.G’s CEO Mitsuhisa Ishikawa—who produced both Ghost in the Shell films—spoke at the event. Ishikawa revealed a key obstacle preventing a third film: finances. He explained that Innocence had an enormous budget, estimated at around 2 billion yen (approximately $13 million), with profits reaching a similar figure. However, the film was planned with a ten-year financial recovery period, and even after 20 years, it has yet to break even.
‘Gucci’s 25% sales collapse should shock no one’ | Jing Daily – “Gucci is so boring now.” “They’ve lost all their confidence.” “It feels desperate — just influencers and celebrities.” Comparing Gucci’s bold, visionary eras under Tom Ford and Alessandro Michele and today’s safe, uninspired iteration reveals a stark contrast. That classroom moment reflected a broader truth: Gucci’s Q1 2025 is not a temporary dip. It’s the result of a deeper structural identity crisis — arguably one of the worst brand resets in recent luxury history.
Careless People has been a much-discussed book in the circles that I am involved in. In the book, Wynn Williams outlines her career at Facebook / Meta in what was at the time a nascent public policy team. The term careless people is a highly appropriate reference to F. Scott Fitzgerald’s The Great Gatsby.
While I was reading Careless People, The Great Gatsby was having a moment. It was the 100th anniversary of The Great Gatsby’s publication, and the ‘careless people’ quote was considered apropos for the times we are living in, in particular the Trump administration’s actions.
“They were careless people… they smashed up things and creatures and then retreated back into their money or their vast carelessness, or whatever it was that kept them together, and let other people clean up the mess they had made.”
Anyway, back to Careless People, Wynn Williams outlines her experience from pitching to Facebook for a role that didn’t exist but was needed, through to navigating the global growth Facebook was undertaking at the time. The challenges that Wynn Williams faces can be broadly broken down into four areas (with specific allegations):
Poor management judgement (lying to Congress and providing assistance to authoritarian regimes)
What’s interesting about the book depends on the reader. Much of the experience was relatable to my own earlier experience at Yahoo! The long days, changing priorities, travel (which was much more of a thing pre-COVID), office politics and fiefdoms, the constant unintended consequences of inventing the future. Secondly, the Facebook management have bought into what Barbrook and Cameron termed The Californian Ideology in their 1995 essay of the same name.
“…the Californian Ideology promiscuously combines the free-wheeling spirit of the hippies and the entrepreneurial zeal of the yuppies. This amalgamation of opposites has been achieved through a profound faith in the emancipatory potential of the new information technologies.”
However, I feel fortunate to have worked with a mature leadership who knew what they didn’t know and realised that they lived in a global village. But that might be down to the fact that Yahoo! by the time I had got there had a fair share of ups and downs.
Careless People reminded me a lot of Barbarians Led by Bill Gates, written by Jennifer Edstrom and Martin Eller. Although they are separated by some 25 years between each being written. Both books tell the story of an imperfect organisation scaling to an opportunity— I have written about Barbarians here. Wynn Williams is the better storyteller, providing a coherent and engaging path through the narrative.
I can recommend Careless People as a good book to read, I finished it off over a bank holiday weekend whilst nursing a sprained ankle. For people in advertising and marketing it won’t be that surprising, it confirms many things that were ‘common knowledge’ in the industry. For the general public, it will be shocking given the facts that the author marshals together.
Would I have thought reading Accidental Empires or Barbarians Led by Bill Gates in the late 1990s that Bill Gates would have transformed from the soulless ‘borg leader’ portrayed to the voice of relative reason he is today? Probably not, there might be hope for Zuckerberg at least out of the cast of characters in the book.
Wynn Williams is now exploring the edges of policy and artificial intelligence and is likely someone worth keeping an eye on.
Les Binet did some sterling work thinking about share of search volume as part of his ongoing work looking at marketing effectiveness.
In order to understand share of search volume, we have to go back to 1990 when former advertising veteran and professor John Philip Jones[i] published a paper in the Harvard Business Review[ii] and a subsequent book[iii].
Jones’ research around the linkage between advertising and sales by looking at advertising including tools of his invention STAS (short term advertising strength)[iv] and AIC (advertising intensiveness curve). One of Jones’ key findings was the linkage between a brand’s share of voice and its market share. One of the biggest predictors of brand growth was ESoV (excess share of voice). ESoV is when a brand has a share of voice in excess of the proportion needed to maintain its market share.
During economic good times this might be down to an increase in brand building marketing spend, not only advertising and public relations, but also influencer and sports sponsorships with variable[v] results.
During recessionary times[vi], it might be maintaining brand building marketing spend when the competitors are cutting back.
Part of this brand building work overlaps with increasing marketing penetration through increasing the number of places where the brand is available. During the 95 percent of time that you are not in a buying mindset when you pass a product display in a supermarket it’s a billboard – doing the brand building work.
Jones’ findings were later validated by Peter Field and Les Binet’s work on marketing efficiency[vii], and in the summation of research[viii] from the Ehrenberg-Bass Institute for Marketing Science by Byron Sharp.
Share of search volume
The clever thing that Les Binet[ix] did with share of search volume[x] was find it as a predictor on the likely time when ESoV was likely to impact with a growth in market share AND, he found that the share of search volume change mapped neatly on to market share change.
The challenge is that different sectors have different times between a change in share of search volume and the corresponding change in market share[xi].
“For mobile phone handsets, Binet further ventured, share of search leads market share “by about six months” as a performance indicator – offering marketers a chance to adapt their strategies if needed if a decline is expected.
“If the brand … had access to the share of search data at the time, it would have had a six-month warning that share of market was about to turn around,” Binet said. “That’s an incredibly useful metric.”
Share of search’s predictive quality for energy brands, Binet explained, was noticeably shorter, at just “nought to three months.”
For automakers, by contrast, share of search anticipates market share by “nine to 12 months,” he said – a significant timeframe for marketers to potentially refine strategies.
Breaking out data for Volkswagen, the auto marque, provided corroboration that sales forecasts based on share of search “are incredibly close to what actually happened,” Binet said.”
Search considerations
Much earlier in my career I worked on the Yahoo! Search business, back when the company had its own search technology and sold its own search advertising. One of the things that we found was that while overall search volume could be modelled accurately for the year based just on January’s search data – unexplained search volume peaks still needed to be ironed out by looking at rolling three-month values instead.
I found it interesting that Binet’s findings didn’t seem the same degree of ‘peakiness’ and was a much more valuable predictor once the time lag factor between share of search volume and market effects were known.
Share of search makes sense from a logical perspective. Many below-the-line activities have been focused on search in terms of aiding SEO to increase share of market opportunity, rather than an explicit appreciation of the impact on the share of search volume and consequently change in market share. My friend James Warren used to talk about public relations and related earned media activities such as organic social media as ‘offline SEO’. This thinking was incorporated into Interpublic’s ‘inline’ concept[xii].
Future search
Share of search volume is complicated by a number of factors that are down to changing consumer behaviour.
Google’s focus on mobile upended the precision that we could search with and what we could search for, out went Boolean operators that could track down a highly relevant web page from 12 years ago. But we could now find the nearest coffee shop with wi-fi. YouTube[xiii] due to its explanatory content became the second largest search engine globally (excluding China).
A good deal of product search has migrated to sites like eBay, Walmart[xiv] and Amazon[xv]. Part of the reason being is that their site search is good enough, they have a wide range of stock and speedy delivery. Amazon also benefits from Amazon Prime which drives customer purchase, but isn’t without controversy[xvi].
Social and generative AI have unlocked new challengers to Google. Search on social platforms has become the go-to approach for many young people. Google acknowledged this when asked by Business Insider[xvii].
“we face robust competition from an array of sources, including general and specialized search engines, as well as dedicated apps.”
The move to social is about tapping what we called back in my Yahoo! days ‘knowledge search’[xviii]. Search startups like Gigabrain have tried to tap into this market by providing a better search function of Reddit forums.
Finally, the move towards consumer usage of generative AI tools based on large language models has created new competitors to Google including Perplexity and ChatGPT Search. Google itself has adopted LLMs in its own search offering and seen an increase in both revenue and profit from search advertising[xix].
Share of model vs. share of search volume
In order to try and understand new LLM-driven search, innovator agencies like Jellyfish and Deft[xx] have looked towards understanding share of model. Share of model tries to understand how LLMs perceive a given brand, in a similar way to the way SEO rankings held a similar place in search engine marketing. Like SEO, they look to understand whether the brand has sufficient optimisation of their digital properties to feature in recommendations by the models.
What share of model doesn’t give us is the consumer insight provided by share of search volume. Share of search volume is consumer behaviour driven and advertising influenced; share of model is algorithmic behaviour driven and training influenced.
Welcome to my April 2025 newsletter, this newsletter marks my 21st issue.
21 marks a transition to full adulthood in various countries, hence ‘keys to the door’ in bingo slang. In Chinese numbers, symbolism is often down to phrases that numbers sound like. 21 sounds like “easily definitely fine” – indicating an auspicious association with the number.
For some reason this month I have had Bill McClintock’sMotor City Woman on repeat. It’s a mash-up of The Spinners – I’ll be Around, Queensrÿche – Jet City Woman and Steely Dan – Do it Again. It’s a bit of an ear worm – you’re welcome.
New reader?
If this is the first newsletter, welcome! You can find my regular writings here and more about me here.
Things I’ve written.
Cleaned up copy of an interview I did as a juror for the PHNX Awards. More here.
From the challenges faced by Apple Intelligence to drone deliveries and designing in lightness.
I thought about how computing tends towards efficiency along the story arc of its history and its likely impact on our use of AI models.
Books that I have read.
The Leftover Woman by Jean Kwok. The book is a complex thriller. The story is straight forward, but the books covers complex, fraught issues with aplomb from misogyny, the male gaze to the white saviour complex.
The Tiger That Isn’t by Michael Blastland and Andrew Dilnot focused on the use of numbers in the media. But it’s also invaluable for strategists reading and interrogating pre-existing research. As a book is very easy-going and readable. I read it travelling back-and-forth to see the parents.
A Spy Alone was written by former MI6 officer Charles Beaumont. I was reminded of the dreary early 1970s of George Smiley’s Britain in Tinker, Tailor, Soldier, Spy by the tone of the book. However A Spy Alone is alarmingly contemporary, with oblique references to UK infrastructure investments in the UK attached to a hostile foreign power, private sector intelligence, open source intelligence a la Bellingcat, nihilistic entrepreneurs and a thoroughly corrupted body politic. Beaumont’s story features a post cold-war spy ring in Oxford University echoing the cold war Cambridge spy ring. Beaumont touches on real contemporary issues through the classic thriller, in the same way that Mick Herron uses satire.
Things I have been inspired by.
Big brand advertising isn’t as digital as we think.
Trends in TV 2025 by Thinkbox threw up some interesting data points and hypotheses.
Advertising is eating retail property. A good deal of search and social advertising gains is not from traditional advertising, but traditional retailing, in place of a real-world shop front. This is primarily carried out by small and medium-sized enterprises. I imagine a lot of this is Chinese direct-to-consumer businesses. 80% of Meta’s revenue is not from the six largest advertising holding companies.
Viewership across video platforms both online and offline have stabilised in the UK. (Separately I heard that ITV were getting the same viewership per programme, but it’s been attenuated with the rise of time-shifted content via the online viewership.
World views
WARC highlighted research done by Craft Human Intelligence for Channel 4 where they outlined six world views for young adults. While it was couched in terms of ‘gen-z’, I would love to see an ongoing inter-cohort longitudinal study to see how these world views change over time in young people. This would also provide an understanding of it it reflects wider population world views. BBH Labspast work looking at Group Cohesion Score of gen-Z – implies that this is unlikely to be just a generational change but might have a more longitudinal effect across generations to varying extents.
Anyway back to he six world views outlined:
‘Girl power’ feminists. 99% identified as female. About 21% of their cohort. “While they’re overwhelmingly progressive, their focus tends to be on personal goals rather than macro-level politics. They underindex heavily on engagement with UK politics and society.”
‘Fight for your rights’. 12% of cohort, 60% female, educated and engaged with current affairs. “Although they consider themselves broadly happy, they believe the UK is deeply unfair – but believe that progress is both necessary and achievable.”
‘Dice are loaded’ are 15% of their cohort. 68% female. “Feeling left behind, they perceive themselves to lack control over their future, and are worried about finances, employment, housing, mental health, or physical appearance.”
‘Zero-sum’ thinkers comprise 18% of their cohort. Over-index at higher end of social-economic scale, gender balanced. “…they lean toward authoritarian and radical views on both sides of the political spectrum.”
‘Boys can’t be boys’ are 14% of the cohort and 82% male. Supporters of traditional masculinity.
‘Blank slates’. 20% of their cohort, all of them male. “They aren’t unintelligent or unambitious, but they pay little attention to matters beyond their own, immediate world. While some follow the news, their main focus is on just getting on with life”.
At the beginning of March, Unilever abruptly replaced its CEO. Hein Schumacher was out, and in the space of a week CFO Fernando Fernandez became CEO. That showed a deep internal dissatisfaction with Unilever’s performance that surprised shareholders AND the business media. Over the past decade Unilever has leaned hard into premium products and influencer marketing.
“There are 19,000 zip codes in India. There are 5,764 municipalities in Brazil. I want one influencer in each of them,” Fernandez said. “That’s a significant change. It requires a machine of content creation, very different to the one we had in the past . . . ”
Fernandez wants to lean even harder into influencer marketing. But I thought that there was a delta on this approach given his goal to have higher margin premium brands that are highly desirable.
“Desirability at scale and marketing activity systems at scale will be the fundamental principles of our marketing strategy”
Meanwhile Michael Farmer’s newsletter had some datapoints that were very apropos to the Unilever situation.
“…for the fifty years from 1960 to 2010, the combined FMCG sales of P&G, Unilever, Nestle and Colgate-Palmolive grew at about an 8% compounded annual growth rate per year. The numbers associated with this long-term growth rate are staggering. P&G alone grew from about $1 billion (1960) to $79 billion in 2010. Throughout this period, P&G was the industry’s advocate for the power of advertising, becoming the largest advertiser in the US, with a focus on traditional advertising — digital / social advertising had hardly begun until 2010. Since 2010, with the advent of digital / social advertising, and massive increases in digital / social spend, P&G, Unilever, Nestle and Colgate-Palmolive have grown, collectively, at less than 1% per year, about half the growth rate of the US economy (2.1% per year). They are not the only major advertisers who have grown below GDP rates. At least 20 of the 50 largest advertisers in the US have grown below 2% per year for the past 15 years. Digital and social advertising, of course, have come to dominate the advertising scene since 2010, and it represents, today, about 2/3rds of all advertising spend.”
Mr Fernandez has quite the Gordian knot to try and solve, one-way or another.
Automated communications and AI influencers
Thanks to Stephen Waddington‘s newsletter highlighted a meta-analysis of research papers on the role of automation and generative AI in communications. What’s interesting is the amount of questions that the paper flags, which are key to consideration of these technologies in marketing and advertising. More here.
LinkedIn performance
Social Insider has pulled together some benchmarking data on LinkedIn content performance. It helps guide what good looks like and the content types to optimise for on LinkedIn. Register and download here.
Chart of the month.
The FT had some really interesting data points that hinted at a possible longitudinal crisis in various aspects of reasoning and problem solving. There has been few ongoing studies in this area, and it deserves more scrutiny.
In his article Have humans past peak brain power, FT data journalist John Burn-Murdoch makes the case about traits which would support intelligence and innovation from reading, to mathematical reasoning and problem solving have been on a downward trends. The timing of this decline seems to correlate with the rise of the social web.
If true, over time this may work its way into marketing effectiveness. My best guess would be that rational messages are likely to be less effective in comparison to simple emotional messages with a single-minded intent over time. This should show up in both short term and long term performance. A more cynical view might be that the opportunity for bundling and other pricing complexities could facilitate greater profit margins over time.
Things I have watched.
Akira Kurosawa’s Stray Dog is a film that I can watch several times over despite the film being over 75 years old now. Detective Murakami’s trek through the neighbourhoods of occupation-era Tokyo and all the actors performances are stunning. The storytelling is amazing and there are set pieces in here that are high points in cinema history. I don’t want to say too much more and spoil it for you, if you haven’t already seen it.
Ghost in the Shell: Standalone Complex – Solid State Society – this is a follow on to the original GiTS manga and anime films touches directly on the challenges faced looking after Japan’s aging society. Central to the story is the apparent kidnapping over time of 20,000 children who can’t remember who their parents are. The plot is up to the usual high standard with government intrigue, technical and societal challenges.
The Wire series one – I stopped and started watching The Wire. Films better suited my focus at the time. I finally started into series one this month. The ensemble cast are brilliant. The show is now 22 years old, yet it has aged surprisingly well. While technology works miracles, the slow methodical approach to building a case is always the same.
How Much Does Your Building Weigh, Mr Foster – is a fantastic documentary covering the career of architect Sir Norman Foster. I remember watching it at the ICA when it originally came out and enjoyed watching it again on DVD. Foster brings a similar approach to architecture that Colin Chapman brought to his Lotus cars. When we are now thinking about efficiency and sustainability, their viewpoints feel very forward-thinking in nature.
Useful tools.
Fixing the iOS Mail app
You know something is up when media outlets are writing to you with instructions on how they can remain visible in your inbox. The problem is due to Apple’s revamp of the iPhone’s Mail.app as part of its update to iOS 18.2.
So how do you do this?
Open Mail.app and you can see the categorised folders at the top of your screen, under the search bar.
Find each tab where an a given email has been put. Open the latest edition. Tap the upper right hand corner. Select ‘Categorise Sender’. Choose ‘Primary’ to make sure future emails from this sender are in your main inbox view.
That’s going to get old pretty soon. My alternative is to toggle between views as it makes sense. Apple’s inbox groupings are handy when you want to quickly find items you can delete quickly. Otherwise the single view makes sense.
Inspiration for strategists
Questions are probably the most important tool for strategists. 100 questions offers inspiration so you can focus on the right ones to ask for a given time.
The sales pitch.
I have been worked on the interrogation process and building responses to a couple of client new business briefs for friends (Red Robin Ventures and Craft Associates) and am now working a new brand and creative strategy engagement as part of an internal creative agency at Google.
If you’re thinking about strategy needs in Q4 (October onwards) – keep me in mind; or discussions on permanent roles. Contact me on YunoJuno and LinkedIn; get my email from Spamty to drop me a line.
Ok this is the end of my April 2025 newsletter, I hope to see you all back here again in a month. Be excellent to each other and onward into spring, and enjoy the May bank holidays.
Don’t forget to share if you found it useful, interesting or insightful.
Get in touch if there is anything that you’d like to recommend for the newsletter.
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?
