What Era of Computing Comes Next?

By now, all of us are aware that rapid reductions in computing and storage cost, with rapid increases in capability, can enable applications, use cases and revenue models that were not feasible in the past because computing or storage costs precluded them. 

So ridesharing is possible because people have capable smartphones and mobile internet access fast enough to support that use case. Netflix and other video streaming services are possible because digital infrastructure capabilities have been improved at Moore’s Law rates. 

Applied artificial intelligence is among the capabilities that benefit directly from rapid processing improvements. A study shows that, “before 2010 training compute grew in line with Moore’s law, doubling roughly every 20 months.”

But “since the advent of deep learning in the early 2010s, the scaling of training compute has accelerated, doubling approximately every six months,” say professors Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn and Pablo Villalobos in a study. 

source: Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn and Pablo Villalobos

source: Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn and Pablo Villalobos

“Our findings seem consistent with previous work, though they indicate a more moderate scaling of training compute,” the researchers say. “In particular, we identify an 18-month doubling time between 1952 and 2010, a six-month doubling time between 2010 and 2022, and a new trend of large-scale models between late 2015 and 2022, which started two to three orders of magnitude over the previous trend and displays a 10-month doubling time.”

Moore's Law and rapid increases in computing power, with corresponding reductions in price, matter hugely. It allows entrepreneurs to innovate by asking the question “ what would my business look like if computing or bandwidth no longer were barriers?” 

Does anybody doubt that near-zero pricing remains among the biggest business threats in the connectivity business? And does anybody really doubt that Moore’s Law has led to substitute products for telco voice and messaging while diminishing the cost of transporting bits? 

Has bandwidth not increased, in lead markets, at the headline level, at about the rate Moore’s Law or Nielsen’s Law predicts? 

Edholm’s Law states that internet access bandwidth at the top end increases at about the same rate as Moore’s Law likewise suggests computing power will increase.

Nielsen's Law essentially is the same as Edholm’s Law, predicting an increase in the headline speed of about 50 percent per year. 

The point is that an inflection point has been reached for applied use of artificial intelligence. As we once might have asked “what does my business look like if computing or bandwidth were essentially free,” we now must start asking questions such as “what does my business look like if artificial intelligence is available to use?” 

As when those earlier questions were asked, the cost of training is nowhere near “free.” But neither was computing or bandwidth when the founders of Microsoft and Netflix laid out their plans. 

The most-startling strategic assumption ever made by Bill Gates was his belief that horrendously-expensive computing hardware would eventually be so low cost that he could build his own business on software for ubiquitous devices. .

How startling was the assumption? Consider that, In constant dollar terms, the computing power of an Apple iPad 2, when Microsoft was founded in 1975, would have cost between US$100 million and $10 billion.

Reed Hastings, Netflix founder, apparently made a similar decision. For Bill Gates, the insight that free computing would be a reality meant he should build his business on software used by computers.

Reed Hastings came to the same conclusion as he looked at bandwidth trends in terms both of capacity and prices. At a time when dial-up modems were running at 56 kbps, Hastings extrapolated from Moore's Law to understand where bandwidth would be in the future, not where it was “right now.”

“We took out our spreadsheets and we figured we’d get 14 megabits per second to the home by 2012, which turns out is about what we will get,” says Reed Hastings, Netflix CEO. “If you drag it out to 2021, we will all have a gigabit to the home." So far, internet access speeds have increased at just about those rates.

Everyone has struggled to define what the next era of computing would look like. We might have found our answer, at least relating to nomenclature. Some say we are in the era of cloud computing. Others might prefer mobile computing or web-based computing.

The point is that we left the mainframe, mini-computer, personal computer, client-server eras. Where we are now might be considered the internet, web, cloud-based or mobile era. We have not yet agreed on a specific term. 

What comes next might well be the AI era.

Name One Legacy Firm That Has Actually "Digitally Transformed" its Business Model

Name one legacy entity that really has "digital transformed itself." Note that better return on investment; happier customers; happier employees; better productivity or higher market share, often said to be ways to meassure digital transformation success, actually can be used to measure success of all other efforts to make any business run better.

If any firm cites better performance on those and other metrics because it uses "digital" or "information technology," how is that in any way different from past applications of IT to improve performance?

In other words, is anybody really pursuing digital transformation, or simply spending more on information technology than they used to, to do more things "using the internet" or "online" or "faster."

Many note that “Digital transformation” (DX) efforts fail at about 70 percent rates. In truth, failure to achieve the fullest and deepest meaning of digital transformation might be virtually 100 percent. 

Many argue that DX is, in principle, different from earlier uses of information technology, which were mostly about efficiency and automation. That might be overstating matters, but failure rates for IT projects are often as high as 70 percent.

Perhaps we should simply admit that change efforts fail most of the time, in any sphere. 

Perhaps we also should admit that what people now call “DX” is not what most entities are attempting.

Digital transformation is almost-always said to involve big changes in culture, technology, external and internal processes to achieve a revolution in business models. Indeed, the term “transformation” virtually requires it. 

Most often, most entities saying they are engaged in digital transformation actually are doing something else. Which is to say, they are applying IT mostly to improve or modify existing processes, without fundamentally changing their revenue models, customers or value chains. 

The point is that, no matter what they say, most entities claiming they are doing “digital transformation” really are not doing so. They are applying digital technology, yes. Trying to improve customer experience; product features; response times and efficiency. 

Few really aim to revolutionize their revenue models, change customers, sell products that are not what their legacy entails. 

Digital transformation is said to be an effort to change business models, while applied information technology earlier was mostly about efficiency and automation. 

That claim, like most generalizations, has to be qualified. 

One would be hard pressed to argue that Apple Computer and Microsoft in the early days were engaged in applying technology to improve the efficiency of their businesses. Instead, they were doing transformation: using technology to create new business models and products. 

More recently, one can make the same argument about Amazon, eBay, Airbnb, Uber, Meta, Netflix or Alphabet: each is a transformation, not an effort to be more efficient, in a direct sense. 

To the extent the definition of DX is true, the former aims to create new and different value; new products and services. In this view, earlier uses of applied information technology mostly aimed to produce lower operating costs. 

In a sense, this is the classic distinction between “effectiveness” and “efficiency.” Some might argue that earlier uses of information technology mostly aimed to “do things faster and at less cost.”

In that analogy, digital transformation aims to “do the right things, not existing things faster.”

Still, it is arguably correct to say that most organizations and firms will never move beyond the older “efficiency” focus, because most entities will never really change their business models. They will keep doing what they have been doing, in that regard. 

If one believes that the crucial attributes of DX are changed business models, and not “merely” better customer experience, profit margins, product features and attributes, then few firms will ever achieve DX. 

source: ElevateIQ

So perhaps it is not surprising that 70 percent to 74 percent of DX projects and efforts fail. 

Of the $1.3 trillion that was spent on digital transformation--using digital technologies to create new or modify existing business processes--in 2018, it is estimated that $900 billion went to waste, say Ed Lam, Li & Fung CFO, Kirk Girard is former Director of Planning and Development in Santa Clara County and Vernon Irvin Lumen Technologies president of Government, Education, and Mid & Small Business. 

That should not come as a surprise, as historically, most big information technology projects fail. BCG research suggests that 70 percent of digital transformations fall short of their objectives. 

From 2003 to 2012, only 6.4 percent of federal IT projects with $10 million or more in labor costs were successful, according to a study by Standish, noted by Brookings.

source: BCG 

IT project success rates range between 28 percent and 30 percent, Standish also notes. The World Bank has estimated that large-scale information and communication projects (each worth over U.S. $6 million) fail or partially fail at a rate of 71 percent. 

McKinsey says that big IT projects also often run over budget. Roughly half of all large IT projects—defined as those with initial price tags exceeding $15 million—run over budget. On average, large IT projects run 45 percent over budget and seven percent over time, while delivering 56 percent less value than predicted, McKinsey says. 

Beyond IT, virtually all efforts at organizational change arguably also fail. The rule of thumb is that 70 percent of organizational change programs fail, in part or completely. 

The big observation, however, is that digital transformation--in the sense of new business models--will rarely succeed, in the broad sense of creating entirely-new revenue models. A firm that makes its money selling autos and trucks will rarely become something else, no matter how much technology is embedded in the business. Airlines never become something else, no matter how intensive their IT efforts. 

Name any legacy firm, in any industry, that has truly “transformed” its business model by becoming something else. Try it. You will find scarcely a handful of firms that could make the claim. Perhaps you cannot name even one firm that has achieved a transformation of business models. 

Most firms can only say they are better able to customize or personalize, extend modes of customer interaction or change processes faster. Few, if any, legacy firms can make the claim they now sell to different customers, earn their money in different ways, by selling different types of products.