IT Trends into 2017 – or the delusions of Ian Waring

Bowling Ball and Pins

My perception is as follows. I’m also happy to be told I’m mad, or delusional, or both – but here goes. Most reflect changes well past the industry move from CapEx led investments to Opex subscriptions of several years past, and indeed the wholesale growth in use of Open Source Software across the industry over the last 10 years. Your own Mileage, or that of your Organisation, May Vary:

  1. if anyone says the words “private cloud”, run for the hills. Or make them watch There is also an equivalent showing how to build a toaster for $15,000. The economics of being in the business of building your own datacentre infrastructure is now an economic fallacy. My last months Amazon AWS bill (where I’ve been developing code – and have a one page site saying what the result will look like) was for 3p. My Digital Ocean server instance (that runs a network of WordPress sites) with 30GB flash storage and more bandwidth than I can shake a stick at, plus backups, is $24/month. Apart from that, all I have is subscriptions to Microsoft, Github and Google for various point services.
  2. Most large IT vendors have approached cloud vendors as “sell to”, and sacrificed their own future by not mapping customer landscapes properly. That’s why OpenStack is painting itself into a small corner of the future market – aimed at enterprises that run their own data centres and pay support costs on a per software instance basis. That’s Banking, Finance and Telco land. Everyone else is on (or headed to) the public cloud, for both economic reasons and “where the experts to manage infrastructure and it’s security live” at scale.
  3. The War stage of Infrastructure cloud is over. Network effects are consolidating around a small number of large players (AWS, Google Cloud Platform, Microsoft Azure) and more niche players with scale (Digital Ocean among SME developers, Softlayer in IBM customers of old, Heroku with Salesforce, probably a few hosting providers).
  4. Industry move to scale out open source, NoSQL (key:value document orientated) databases, and components folks can wire together. Having been brought up on MySQL, it was surprisingly easy to set up a MongoDB cluster with shards (to spread the read load, scaled out based on index key ranges) and to have slave replicas backing data up on the fly across a wide area network. For wiring up discrete cloud services, the ground is still rough in places (I spent a couple of months trying to get an authentication/login workflow working between a single page JavaScript web app, Amazon Cognito and IAM). As is the case across the cloud industry, the documentation struggles to keep up with the speed of change; developers have to be happy to routinely dip into Github to see how to make things work.
  5. There is a lot of focus on using Containers as a delivery mechanism for scale out infrastructure, and management tools to orchestrate their environment. Go, Chef, Jenkins, Kubernetes, none of which I have operational experience with (as I’m building new apps have less dependencies on legacy code and data than most). Continuous Integration and DevOps often cited in environments were custom code needs to be deployed, with Slack as the ultimate communications tool to warn of regular incoming updates. Having been at one startup for a while, it often reminded me of the sort of military infantry call of “incoming!” from the DevOps team.
  6. There are some laudable efforts to abstract code to be able to run on multiple cloud providers. FOG in the Ruby ecosystem. CloudFoundry (termed BlueMix in IBM) is executing particularly well in large Enterprises with investments in Java code. Amazon are trying pretty hard to make their partners use functionality only available on AWS, in traditional lock-in strategy (to avoid their services becoming a price led commodity).
  7. The bleeding edge is currently “Function as a Service”, “Backend as a Service” or “Serverless apps” typified with Amazon Lambda. There are actually two different entities in the mix; one to provide code and to pay per invocation against external events, the other to be able to scale (or contract) a service in real time as demand flexes. You abstract all knowledge of the environment  away.
  8. Google, Azure and to a lesser extent AWS are packaging up API calls for various core services and machine learning facilities. Eg: I can call Google’s Vision API with a JPEG image file, and it can give me the location of every face (top of nose) on the picture, face bounds, whether each is smiling or not). Another that can describe what’s in the picture. There’s also a link into machine learning training to say “does this picture show a cookie” or “extract the invoice number off this image of a picture of an invoice”. There is an excellent 35 minute discussion on the evolving API landscape (including the 8 stages of API lifecycle, the need for honeypots to offset an emergent security threat and an insight to one impressive Uber API) on a recent edition of the Google Cloud Platform Podcast: see
  9. Microsoft and Google (with PowerApps and App Maker respectively) trying to remove the queue of IT requests for small custom business apps based on company data. Though so far, only on internal intranet type apps, not exposed outside the organisation). This is also an antithesis of the desire for “big data”, which is really the domain of folks with massive data sets and the emergent “Internet of Things” sensor networks – where cloud vendor efforts on machine learning APIs can provide real business value. But for a lot of commercial organisations, getting data consolidated into a “single version of the truth” and accessible to the folks who need it day to day is where PowerApps and AppMaker can really help.
  10. Mobile apps are currently dogged by “winner take all” app stores, with a typical user using 5 apps for almost all of their mobile activity. With new enhancements added by all the major browser manufacturers, web components will finally come to the fore for mobile app delivery (not least as they have all the benefits of the web and all of those of mobile apps – off a single code base). Look to hear a lot more about Polymer in the coming months (which I’m using for my own app in conjunction with Google Firebase – to develop a compelling Progressive Web app). For an introduction, see:
  11. Overall, the thing most large vendors and SIs have missed is to map their customer needs against available project components. To map user needs against axes of product life cycle and value chains – and to suss the likely movement of components (which also tells you where to apply six sigma and where agile techniques within the same organisation). But more eloquently explained by Simon Wardley:

There are quite a range of “end of 2016” of surveys I’ve seen that reflect quite a few of these trends, albeit from different perspectives (even one that mentioned the end of Java as a legacy language). You can also add overlays with security challenges and trends. But – what have I missed, or what have I got wrong? I’d love to know your views.

Reinventing Healthcare

Comparison of US and UK healthcare costs per capita

A lot of the political effort in the UK appears to circle around a government justifying and handing off parts of our NHS delivery assets to private enterprises, despite the ultimate model (that of the USA healthcare industry) costing significantly more per capita. Outside of politicians lining their own pockets in the future, it would be easy to conclude that few would benefit by such changes; such moves appear to be both economically farcical and firmly against the public interest. I’ve not yet heard any articulation of a view that indicates otherwise. But less well discussed are the changes that are coming, and where the NHS is uniquely positioned to pivot into the future.

There is significant work to capture DNA of individuals, but these are fairly static over time. It is estimated that there are 10^9 data points per individual, but there are many other data points – which change against a long timeline – that could be even more significant in helping to diagnose unwanted conditions in a timely fashion. To flip the industry to work almost exclusively to preventative and away from symptom based healthcare.

I think I was on the right track with an interest in Microbiome testing services. The gotcha is that commercial services like uBiome, and public research like the American (and British) Gut Project, are one-shot affairs. Taking a stool, skin or other location sample takes circa 6,000 hours of CPU wall time to reconstruct the 16S rRNA gene sequences of a statistically valid population profile. Something I thought I could get to a super fast turnaround using excess capacity (spot instances – excess compute power you can bid to consume when available) at one or more of the large cloud vendors. And then to build a data asset that could use machine learning techniques to spot patterns in people who later get afflicted by an undesirable or life threatening medical condition.

The primary weakness in the plan is that you can’t suss the way a train is travelling by examining a photograph taken looking down at a static railway line. You need to keep the source sample data (not just a summary) and measure at regular intervals; an incidence of salmonella can routinely knock out 30% of your Microbiome population inside 3 days before it recovers. The profile also flexes wildly based on what you eat and other physiological factors.

The other weakness is that your DNA and your Microbiome samples are not the full story. There are many other potential leading indicators that could determine your propensity to become ill that we’re not even sampling. The questions of which of our 10^18 different data points are significant over time, and how regularly we should be sampled, are open questions

Experience in the USA is that in environments where regular preventative checkups of otherwise healthy individuals take place – that of Dentists – have managed to lower the cost of service delivery by 10% at a time where the rest of the health industry have seen 30-40% cost increases.

So, what are the measures that should be taken, how regularly and how can we keep the source data in a way that allows researchers to employ machine learning techniques to expose the patterns toward future ill-health? There was a good discussion this week on the A16Z Podcast on this very subject with Jeffrey Kaditz of Q Bio. If you have a spare 30 minutes, I thoroughly recommend a listen:

That said, my own savings are such that I have to refocus my own efforts elsewhere back in the IT industry, and my MicroBiome testing service Business Plan mothballed. The technology to regularly sample a big enough population regularly is not yet deployable in a cost effective fashion, but will come. When it does, the NHS will be uniquely positioned to pivot into the sampling and preventative future of healthcare unhindered.

Future Health: DNA is one thing, but 90% of you is not you

One of my pet hates is seeing my wife visit the doctor, getting hunches of what may be afflicting her health, and this leading to a succession of “oh, that didn’t work – try this instead” visits for several weeks. I just wonder how much cost could be squeezed out of the process – and lack of secondary conditions occurring – if the root causes were much easier to identify reliably. I then wonder if there is a process to achieve that, especially in the context of new sensors coming to market and their connectivity to databases via mobile phone handsets – or indeed WiFi enabled, low end Bluetooth sensor hubs aka the Apple Watch.

I’ve personally kept a record of what i’ve eaten, down to fat, protein and carb content (plus my Monday 7am weight and daily calorie intake) every day since June 2002. A precursor to the future where devices can keep track of a wide variety of health signals, feeding a trend (in conjunction with “big data” and “machine learning” analyses) toward self service health. My Apple Watch has a years worth of heart rate data. But what signals would be far more compelling to a wider variety of (lack of) health root cause identification if they were available?

There is currently a lot of focus on Genetics, where the Human Genome can betray many characteristics or pre-dispositions to some health conditions that are inherited. My wife Jane got a complete 23andMe statistical assessment several years ago, and has also been tested for the BRCA2 (pronounced ‘bracca-2’) gene – a marker for inherited pre-disposition to risk of Breast Cancer – which she fortunately did not inherit from her afflicted father.

A lot of effort is underway to collect and sequence the complete Genome sequences from the DNA of hundreds of thousands of people, building them into a significant “Open Data” asset for ongoing research. One gotcha is that such data is being collected by numerous organisations around the world, and the size of each individuals DNA (assuming one byte to each nucleotide component – A/T or C/G combinations) runs to 3GB of base pairs. You can’t do research by throwing an SQL query (let alone thousands of machine learning attempts) over that data when samples are stored in many different organisations databases, hence the existence of an API (courtesy of the GA4GH Data Working Group) to permit distributed queries between co-operating research organisations. Notable that there are Amazon Web Services and Google employees participating in this effort.

However, I wonder if we’re missing a big and potentially just as important data asset; that of the profile of bacteria that everyone is dependent on. We are each home to approx. 10 trillion human cells among the 100 trillion microbial cells in and on our own bodies; you are 90% not you.

While our human DNA is 99.9% identical to any person next to us, the profile of our MicroBiome are typically only 10% similar; our age, diet, genetics, physiology and use of antibiotics are also heavy influencing factors. Our DNA is our blueprint; the profile of the bacteria we carry is an ever changing set of weather conditions that either influence our health – or are leading indicators of something being wrong – or both. Far from being inert passengers, these little organisms play essential roles in the most fundamental processes of our lives, including digestion, immune responses and even behaviour.

Different MicroBiome ecosystems are present in different areas of our body, from our skin, mouth, stomach, intestines and genitals; most promise is currently derived from the analysis of stool samples. Further, our gut is only second to our brain in the number of nerve endings present, many of them able to enact activity independently from decisions upstairs. In other areas, there are very active hotlines between the two nerve cities.

Research is emerging that suggests previously unknown links between our microbes and numerous diseases, including obesity, arthritis, autism, depression and a litany of auto-immune conditions. Everyone knows someone who eats like a horse but is skinny thin; the composition of microbes in their gut is a significant factor.

Meanwhile, costs of DNA sequencing and compute power have dropped to a level where analysis of our microbe ecosystems costs from $100M a decade ago to some $100 today. It should continue on that downward path to a level where personal regular sampling could become available to all – if access to the needed sequencing equipment plus compute resources were more accessible and had much shorter total turnaround times. Not least to provide a rich Open Data corpus of samples that we can use for research purposes (and to feed back discoveries to the folks providing samples). So, what’s stopping us?

Data Corpus for Research Projects

To date, significant resources are being expended on Human DNA Genetics and comparatively little on MicroBiome ecosystems; the largest research projects are custom built and have sampling populations of less than 4000 individuals. This results in insufficient population sizes and sample frequency on which to easily and quickly conduct wholesale analyses; this to understand the components of health afflictions, changes to the mix over time and to isolate root causes.

There are open data efforts underway with the American Gut Project (based out of the Knight Lab in the University of San Diego) plus a feeder “British Gut Project” (involving Tim Spector and staff at University College London). The main gotcha is that the service is one-shot and takes several months to turn around. My own sample, submitted in January, may take up 6 months to work through their sequencing then compute batch process.

In parallel, VC funded company uBiome provide the sampling with a 6-8 week turnaround (at least for the gut samples; slower for the other 4 area samples we’ve submitted), though they are currently not sharing the captured data to the best of my knowledge. That said, the analysis gives an indication of the names, types and quantities of bacteria present (with a league table of those over and under represented compared to all samples they’ve received to date), but do not currently communicate any health related findings.

My own uBiome measures suggest my gut ecosystem is more diverse than 83% of folks they’ve sampled to date, which is an analogue for being more healthy than most; those bacteria that are over represented – one up to 67x more than is usual – are of the type that orally administered probiotics attempt to get to your gut. So a life of avoiding antibiotics whenever possible appears to have helped me.

However, the gut ecosystem can flex quite dramatically. As an example, see what happened when one person contracted Salmonella over a three pay period (the green in the top of this picture; x-axis is days); you can see an aggressive killing spree where 30% of the gut bacteria population are displaced, followed by a gradual fight back to normality:

Salmonella affecting MicroBiome PopulationUnder usual circumstances, the US/UK Gut Projects and indeed uBiome take a single measure and report back many weeks later. The only extra feature that may be deduced is the delta between counts of genome start and end sequences, as this will give an indication to the relative species population growth rates from otherwise static data.

I am not aware of anyone offering a faster turnaround service, nor one that can map several successively time gapped samples, let alone one that can convey health afflictions that can be deduced from the mix – or indeed from progressive weather patterns – based on the profile of bacteria populations found.

My questions include:

  1. Is there demand for a fast turnaround, wholesale profile of a bacterial population to assist medical professionals isolating a indicators – or the root cause – of ill health with impressive accuracy?
  2. How useful would a large corpus of bacterial “open data” be to research teams, to support their own analysis hunches and indeed to support enough data to make use of machine learning inferences? Could we routinely take samples donated by patients or hospitals to incorporate into this research corpus? Do we need the extensive questionnaires the the various Gut Projects and uBiome issue completed alongside every sample?
  3. What are the steps in the analysis pipeline that are slowing the end to end process? Does increased sample size (beyond a small stain on a cotton bud) remove the need to enhance/copy the sample, with it’s associated need for nitrogen-based lab environments (many types of bacteria are happy as Larry in the Nitrogen of the gut, but perish with exposure to oxygen).
  4. Is there any work active to make the QIIME (pronounced “Chime”) pattern matching code take advantage of cloud spot instances, inc Hadoop or Spark, to speed the turnaround time from Sequencing reads to the resulting species type:volume value pairs?
  5. What’s the most effective delivery mechanism for providing “Open Data” exposure to researchers, while retaining the privacy (protection from financial or reputational prejudice) for those providing samples?
  6. How do we feed research discoveries back (in English) to the folks who’ve provided samples and their associated medical professionals?

New Generation Sequencing works by splitting DNA/RNA strands into relatively short read lengths, which then need to be reassembled against known patterns. Taking a poop sample with contains thousands of different bacteria is akin to throwing the pieces of many thousand puzzles into one pile and then having to reconstruct them back – and count the number of each. As an illustration, a single HiSeq run may generate up to 6 x 10^9 sequences; these then need reassembling and the count of 16S rDNA type:quantity value pairs deduced. I’ve seen estimates of six thousand CPU hours to do the associated analysis to end up with statistically valid type and count pairs. This is a possible use case for otherwise unused spot instance capacity at large cloud vendors if the data volumes could be ingested and processed cost effectively.

Nanopore sequencing is another route, which has much longer read lengths but is much more error prone (1% for NGS, typically up to 30% for portable Nanopore devices), which probably limits their utility for analysing bacteria samples in our use case. Much more useful if you’re testing for particular types of RNA or DNA, rather than the wholesale profiling exercise we need. Hence for the time being, we’re reliant on trying to make an industrial scale, lab based batch process turn around data as fast we are able – but having a network accessible data corpus and research findings feedback process in place if and when sampling technology gets to be low cost and distributed to the point of use.

The elephant in the room is in working out how to fund the build of the service, to map it’s likely cost profile as technology/process improvements feed through, and to know to what extent it’s diagnosis of health root causes will improve it’s commercial attractiveness as a paid service over time. That is what i’m trying to assess while on the bench between work contracts.

Other approaches

Nature has it’s way of providing short cuts. Dogs have been trained to be amazingly prescient at assessing whether someone has Parkinson’s just by smelling their skin. There are other techniques where a pocket sized spectrometer can assess the existence of 23 specific health disorders. There may well be other techniques that come to market that don’t require a thorough picture of a bacterial population profile to give medical professionals the identity of the root causes of someone’s ill health. That said, a thorough analysis may at least be of utility to the research community, even if we get to only eliminate ever rarer edge cases as we go.

Coming full circle

One thing that’s become eerily apparent to date is some of the common terminology between MicroBiome conditions and terms i’ve once heard used by Chinese Herbal Medicine (my wife’s psoriasis was cured after seeing a practitioner in Newbury for several weeks nearly 20 years ago). The concept of “balance” and the existence of “heat” (betraying the inflammation as your bacterial population of different species ebbs and flows in reaction to different conditions). Then consumption or application of specific plant matter that puts the bodies bacterial population back to operating norms.

Lingzhi Mushroom

Wild mushroom “Lingzhi” in China: cultivated in the far east, found to reduce Obesity

We’ve started to discover that some of the plants and herbs used in Chinese Medicine do have symbiotic effects on your bacterial population on conditions they are reckoned to help cure. With that, we are starting to see some statistically valid evidence that Chinese and Western medicine may well meet in the future, and be part of the same process in our future health management.

Until then, still work to do on the business plan.

Crossing the Chasm on One Page of A4 … and Wardley Maps

Crossing the Chasm Diagram

Crossing the Chasm – on one sheet of A4

The core essence of most management books I read can be boiled down to occupy a sheet of A4. There have also been a few big mistakes along the way, such as what were considered at the time to be seminal works, like Tom Peter’s “In Search of Excellence” — that in retrospect was an example summarised as “even the most successful companies possess DNA that also breed the seeds of their own destruction”.

I have much simpler business dynamics mapped out that I can explain to fast track employees — and demonstrate — inside an hour; there are usually four graphs that, once drawn, will betray the dynamics (or points of failure) afflicting any business. A very useful lesson I learnt from Microsoft when I used to distribute their software. But I digress.

Among my many Business books, I thought the insights in Geoffrey Moores Book “Crossing the Chasm” were brilliant — and useful for helping grow some of the product businesses i’ve run. The only gotcha is that I found myself keeping on cross referencing different parts of the book when trying to build a go-to-market plan for DEC Alpha AXP Servers (my first use of his work) back in the mid-1990’s — the time I worked for one of DEC’s Distributors.

So, suitably bored when my wife was watching J.R. Ewing being mischievous in the first UK run of “Dallas” on TV, I sat on the living room floor and penned this one page summary of the books major points. Just click it to download the PDF with my compliments. Or watch the author himself describe the model in under 14 minutes at an O’Reilly Strata Conference here. Or alternatively, go buy the latest edition of his book: Crossing the Chasm

My PA (when I ran Marketing Services at Demon Internet) redrew my hand-drawn sheet of A4 into the Microsoft Publisher document that output the one page PDF, and that i’ve referred to ever since. If you want a copy of the source file, please let me know — drop a request to:

That said, i’ve been far more inspired by the recent work of Simon Wardley. He effectively breaks a service into its individual components and positions each on a 2D map;  x-axis dictates the stage of the components evolution as it does through a Chasm-style lifecycle; the y-axis symbolises the value chain from raw materials to end user experience. You then place all the individual components and their linkages as part of an end-to-end service on the result. Having seen the landscape in this map form, then to assess how each component evolves/moves from custom build to commodity status over time. Even newest components evolve from chaotic genesis (where standards are not defined and/or features incomplete) to becoming well understood utilities in time.

The result highlights which service components need Agile, fast iterating discovery and which are becoming industrialised, six-sigma commodities. And once you see your map, you can focus teams and their measures on the important changes needed without breeding any contradictory or conflict-ridden behaviours. You end up with a well understood map and – once you overlay competitive offerings – can also assess the positions of other organisations that you may be competing with.

The only gotcha in all of this approach is that Simon hasn’t written the book yet. However, I notice he’s just provided a summary of his work on his Bits n Pieces Blog yesterday. See: Wardley Maps – set of useful Posts. That will keep anyone out of mischief for a very long time, but the end result is a well articulated, compelling strategy and the basis for a well thought out, go to market plan.

In the meantime, the basics on what is and isn’t working, and sussing out the important things to focus on, are core skills I can bring to bear for any software, channel-based or internet related business. I’m also technically literate enough to drag the supporting data out of IT systems for you where needed. Whether your business is an Internet-based startup or an established B2C or B2B Enterprise focussed IT business, i’d be delighted to assist.

Mobile Phone User Interfaces and Chinese Genius

Most of my interactions with the online world use my iPhone 6S Plus, Apple Watch, iPad Pro or MacBook – but with one eye on next big things from the US West Coast. The current Venture Capital fads being on Conversational Bots, Virtual Reality and Augmented Reality. I bought a Google Cardboard kit for my grandson to have a first glimpse of VR on his iPhone 5C, though spent most of the time trying to work out why his handset was too full to install any of the Cardboard demo apps; 8GB, 2 apps, 20 songs and the storage list that only added up to 5GB use. Hence having to borrow his Dad’s iPhone 6 while we tried to sort out what was eating up 3GB. Very impressive nonetheless.

The one device I’m waiting to buy is an Amazon Echo (currently USA only). It’s a speaker with six directional microphones, an Internet connection and some voice control smarts; these are extendable by use of an application programming interface and database residing in their US East Datacentre. Out of the box, you can ask it’s nom de plume “Alexa” to play a music single, album or wish list. To read back an audio book from where you last left off. To add an item to a shopping or to-do list. To ask about local outside weather over the next 24 hours. And so on.

It’s real beauty is that you can define your own voice keywords into what Amazon term a “Skill”, and provide your own plumbing to your own applications using what Amazon term their “Alexa Skill Kit”, aka “ASK”. There is already one UK Bank that have prototyped a Skill for the device to enquire their users bank balance, primarily as an assist to the visually impaired. More in the USA to control home lighting and heating by voice controls (and I guess very simple to give commands to change TV channels or to record for later viewing). The only missing bit is that of identity; the person speaking can be anyone in proximity to the device, or indeed any device emitting sound in the room; a radio presenter saying “Alexa – turn the heating up to full power” would not be appreciated by most listeners.

For further details on Amazon Echo and Alexa, see this post.

However, the mind wanders over to my mobile phone, and the disjointed experience it exposes to me when I’m trying to accomplish various tasks end to end. Data is stored in application silos. Enterprise apps quite often stop at a Citrix client turning your pocket supercomputer into a dumb (but secured) Windows terminal, where the UI turns into normal Enterprise app silo soup to go navigate.

Some simple client-side workflows can be managed by software like IFTTT – aka “IF This, Then That” – so I can get a new Photo automatically posted to Facebook or Instagram, or notifications issued to be when an external event occurs. But nothing that integrates a complete buying experience. The current fad for conversational bots still falls well short; imagine the workflow asking Alexa to order some flowers, as there are no visual cues to help that discussion and buying experience along.

For that, we’d really need to do one of the Jeff Bezos edicts – of wiping the slate clean, to imagine the best experience from a user perspective and work back. But the lessons have already been learnt in China, where desktop apps weren’t a path on the evolution of mobile deployments in society. An article that runs deep on this – and what folks can achieve within WeChat in China – is impressive. See:

I wonder if Android or iOS – with the appropriate enterprise APIs – could move our experience on mobile handsets to a similar next level of compelling personal servant. I hope the Advanced Development teams at both Apple and Google – or a startup – are already prototyping  such a revolutionary, notifications baked in, mobile user interface.

Future of Transport: is it electric?

I keep seeing inferences that the economics of making and running a car will drive a wholesale move to electric power in less than 10 years time. A Tesla can accelerate 0-60 mph in 2.8 seconds. A full tank in a BMW i3 gives 100 miles range and costs £2.00 to fill. The simplification of the drive train should drive manufacturing and maintenance costs right down. I’m starting to see more Nissan Leafs, BMW i3s, Teslas and Renault electric cars on the road – the latter reminding me of the old 2 seater Messerschmidt Sausage Cars that routinely drove around UK roads in the 1960s. And rumours abound that even Apple are looking at offering their own electric car in 2020 or so.

Meanwhile, I wonder if there are even more compelling transport simplifications. The two wheels in front, one behind of 1950s Morgan cars look like they’d be great fun for one seater transport like these from Toyota or Sway models. Lean like skiing.

Kudos to Jean-Louis Gassee for doing something I’ve not seen done elsewhere; a back of the envelope calculation of how much electricity capacity needs to be added if you assume a wholesale switch to electric power. For the USA, with a current capacity of circa 1 Terrawatt per year, the extra load would amount to an extra 1.3 Terrwatt capacity needed. See: Monday Note. I just wonder if the UK Government are agonizing over future Electricity Capacity needs – as well as tax implications – if the shift to electric power really is as compelling as folks believe.

My Apple Watch: one year on

Apple Watch Clock Face

I have worn my Apple Watch every day for a year now. I still think Apple over complicated the way they sell them, and didn’t focus on the core use cases that make it an asset to its users. For me, the stand outs are as follows:

  1. It tells the time, super accurately. The proof point is lining up several Apple watches next to each other; the second hands are always in perfect sync.
  2. You can see the time in the dark, just looking at your wrist. Stupidly simple, I know.
  3. When a notification comes in, you get tapped on the wrist. Feeling the device do that to you for the first time is one of the things that gives someone their first “oh” moment; when I demo the watch to anyone, putting it on their wrist and invoking the heart Taptic pulse is the key feature to show. Outside of message notifications, having the “Dark Sky” app tell me it’s going to rain outside in 5 or 10 minutes is really helpful.
  4. I pay for stuff using my Nationwide Debit or Credit cards, without having to take them out of my wallet. Outside my regular haunts, this still surprises people to this day; I fear that the take up is not as common as I expected, but useful nonetheless.
  5. I know it’s monitoring some aspects of my health (heart rate, exercise) and keeps egging me on, even though it’s not yet linked into the diet and food intake site I use daily (
  6. Being able to see who’s calling my phone and the ability to bat back a “busy, will call you back” reply without having to drag out my phone.

The demo in an Apple Store doesn’t major on any of the above; they tend to focus on aesthetics and in how you can choose different watch faces, an activity most people ever do once. On two occasions while waiting in the queue to be served at an Apple Store, someone has noticed I’m wearing an Apple Watch and has asked what it’s like day to day; I put mine on their wrist and quickly step through the above. Both went from curiosity to buying one as soon as the Apple rep freed up.

I rarely if ever expose the application honeycombe. Simplicity sells, and I’m sure Apple’s own usage stats would show that to them. I also find Siri unusable every time I try to use it on the watch.

As for the future, what would make it even more compelling for me? Personally:

  • In the final analysis, an Apple Watch is a Low Energy Bluetooth enabled WiFi hub with a clock face on it. Having more folks stringing other health sensors, in addition to ones on the device itself, to health/diet related apps will align nicely with future “self service” health management trends.
  • Being able to tone down the ‘chatty-ness’ of notifications. I’m conscious that keeping looking at my watch regularly when in a meeting is a big no-no, and having more control on what gets to tap my wrist and what just floats by in a stream for when I look voluntarily would be an asset.
  • When driving (which is when I’m not wearing glasses), knowing who or what is notifying me in a much bigger font would help. Or the ability to easily throw a text to voice of the from and subject lines onto my phone or car speakers, with optional read back of the message body.
  • Siri. I just wish it worked to a usable standard. I hope there are a few Amazon Echos sitting in the dev labs in Cupertino and that someone there is replicating its functionality in a wrist form factor.

So, the future is an eclectic mix between a low energy Bluetooth WiFi hub for health apps, a super accurate watch, selective notification advisor and an Amazon Echo for your wrist – with integrated card payments. Then getting the result easily integrated into health apps and application workflows – which I hope is what their upcoming WorldWide Developers Conference will cover.

Apple Watch: My first 48 hours

To relate my first impressions of my Apple Watch (folks keep asking).  I bought the Stainless Steel one with a Classic Black Strap.

The experience in the Apple Store was a bit too focussed on changing the clock face design; the experience of using it, for accepting the default face to start with, and using it for real, is (so far) much more pleasant. But take it off the charger, put it on, and you get:

Apple Watch PIN Challenge

Tap in your pin, then the watch face is there:

Apple Watch Clock Face

There’s actually a small (virtual) red/blue LED just above the “60” atop the clock – red if a notification has come in, turning into a blue padlock if you still need to enter your PIN, but otherwise what you see here. London Time, 9 degrees centigrade, 26th day of the current month, and my next calendar appointment underneath.

For notifications it feels deserving of my attention, it not only lights the LED (which I only get so see if I flick my wrist up to see the watch face), but it also goes tap-tap-tap on my wrist. This optionally also sounds a small warning, but that’s something I switched off pretty early on. The taptic hint is nice, quiet and quite subtle.

Most of the set-up for apps and settings is done on the Apple iPhone you have paired up to the watch. Apps reside on the phone, and ones you already have that can talk to your watch are listed already. You can then select which ones you want to appear on the watches application screen, and a subset you want to have as “glances” for faster access. The structure looks something like this:

Apple Watch No NotificationsApple Watch Clock Face

Apple Watch Heart Rate Apple Watch Local Weather Amazon Stock Quote Apple Watch Dark Sky


Hence, swipe down from the top, you see the notification stream, swipe back up, you’re back to the clock face. Swipe up from the bottom, you get the last “glance” you looked at. In my case, I was every now and then seeing how my (long term buy and hold) shares in Amazon were doing after they announced the size of their Web Services division. The currently selected glance stays in place for next time I swipe up unless I leave the screen having moved along that row.

If I swipe from left to right, or right to left, I step over different “glances”. These behave like swiping between icon screens on an iPhone or iPad; if you want more detail, you can click on them to invoke the matching application. I have around 12 of these in place at the moment. Once done, swipe back up, and back to the clock face again. After around 6 seconds, the screen blacks out – until the next time you swing the watch face back into view, at which point it lights up again. Works well.

You’ll see it’s monitoring my heart rate, and measuring my movement. But in the meantime, if I want to call or message someone, I can hit the small button on the side and get a list of 12 commonly called friends:

Apple Watch Friends

Move the crown around, click the picture, and I can call or iMessage them directly. Text or voice clip. Yes, directly on the watch, even if my iPhone is upstairs or atop the cookery books in the kitchen; it has a microphone and a speaker, and works from anywhere over local WiFi. I can even see who is phoning me and take their calls on the watch.

If I need to message anyone else, I can press the crown button in and summon Siri; the accuracy of Siri is remarkable now. One of my sons sent an iMessage to me when I was sitting outside the Pharmacy in Boots, and I gave a full sentence reply (verbally) then told it to send – 100% accurately despite me largely whispering into the watch on my wrist. Must have looked strange.

There are applications on the watch but these are probably a less used edge case; in my case, the view on my watch looks just like the layout i’ve given in the iPhone Watch app:

Apple Watch Applications

So, I can jump in to invoke apps that aren’t set as glances. My only surprise so far was finding that FaceBook haven’t yet released their Watch or Messenger apps, though Instagram (which they also own) is there already. Eh, tap tap on my wrist to tell me Paula Radcliffe had just completed her last London Marathon:

BBC News Paula Radcliffeand a bit later:

Everton 3 Man Utd 0

Oh dear, what a shame, how sad (smirk – Aston Villa fan typing). But if there’s a flurry of notifications, and you just want to clear the lot off in one fell swoop, just hard press the screen and…

Clear All Notificatios

Tap the X and zap, all gone.

There are a myriad of useful apps; I have Dark Sky (which gives you a hyper local forecast of any impending rain), City Mapper (helps direct you around London on all different forms of Transport available), Uber, and several others. They are there in the application icons, but also enabled from the Watch app on my phone (Apps, then the subset selected as Glances):

Ians Watch Apps Ians Watch Glances

With that, tap tap on my wrist:

Apple Watch Stand Up!

Hmmm – i’ve been sitting for too long, so time to get off my arse. It will also assess my exercise in the day and give me some targets to achieve – which it’ll then display for later admiration. Or disgust.

There is more to come. I can already call a Uber taxi directly from the watch. The BBC News Glance rotates the few top stories if selected. Folks in the USA can already use it to pay at any NFC cash terminal with a single click (if the watch comes off your wrist, it senses this and will insist on a PIN then). Twitter gives notifications and has a glance that reports the top trend hashtag when viewed.

So far, the battery is only getting from 100% down to 30% in regular use from 6:00am in the morning until 11:30pm at night, so looking good. Boy, those Amazon shares are going up; that’ll pay for my watch many times over:

Watch on Arm

Overall, impressed so far, very happy with it, and i’m sure the start of a world where software steps submerge into a world of simple notifications and responses to same. And i’m sure Jane (my wife) will want one soon. Just have to wean her out of her desire for the £10,000+ gold one to match her gold coloured MacBook.

Can’t vs Don’t 

 I seem to find good articles in my daily feed from Medium these days. This one about the psychology of habits, and how to build a stronger will when you – like me – come off a long diet. This written by the guy who wrote the book in my reading queue about building habit forming products.

It’s a good read. My friend Annika was right all along; the habit is the key thing to establish. Saying “I don’t” is a much stronger push back than “I can’t”. Further reading Here

Apple Watch: it’s Disneys MagicBand, for the world outside the theme park


A 500 word article that rings true to me. It’ll also be the central hub for all the health sensors that will spring to prominence in the coming months. I’ll put my order in next week. In the meantime, read some wise words here