Repost from my post to Medium Jun 2024.

Introduction:

I wanted to repost this from 2024 mainly to reflect what ha happened in the intervening year, where I was wrong and what we need to learn to move real gains in Productivity.

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How technology strategy, leadership and being the inventor does not associate with leadership.

I have been considering the relevance of AI in context of more than just another invention, rather the catalyst for a step change in productivity. I believe AI, AGI and beyond will bring significant and consequential changes to countries, companies and people. But only if done right. Being an inventor does not mean leadership. That comes from deployment of structural change as discussed here, and with the best example.

My belief comes from consideration of the depth of potential change AI can bring. The changes we have seen to date builds on many years of work in machine learning which was invisible to the general population. ChatGPT enhanced the early versions of customer service attempts using online chat. AI and innovations such as the transformer method took the chat interface to far higher levels through treatment of the entire Internet as a dataset which could be interpreted for patterns and research in real-time. AI has moved quickly in a few short months, and produces output results which to us seem almost magical. It produces multi modal results of documents, document correction, graphics, detailed plans whether security, business, family or more than my imagination perceives. The best AI is a trusted, objective mentor who confirms, advises, and suggests all with no financial benefit expected. This depth of capability when considered in a future context makes it easy to see a roadmap to an all knowing entity for us. This goes far, far beyond mere automation.

The business context will produce immense leaps in productivity which is what I am begnning to explore here.

Productivity at its core reflects an increase in revenue for each dollar of expense. The typical measurement for a country is Gross Domestic Product. For this to be achieved requires structural change of a level not seen since the Industrial Revolution several hunndred years ago.

This is my conviction wnd why I shifted my blogging focus to The Productivity Era.

ANALYSIS

The issue of Productivity for me is to satisfy the burning platform of weak productivity on average both in Canada and most western countries. In North America Productivity is generally considered within individual companies. Productivity at todays rates really mean we are going backwards and is not sustainable. AI offers at its minimum a business alternative.

The British Industrial Revolution famously displays relatively slow economic growth.

The issue of why productivity growth during the British industrial revolution was slow despite the arrival of famous inventions is revisited using a growth accounting methodology based on an embodied innovation model. The results highlight the relatively small and long-delayed impact of steam on productivity growth even when capital deepening is taken into account. Even so, technological change including embodiment effects accounted entirely for the acceleration in labor productivity growth that allowed the economy to achieve “modern economic growth.”

CRAFTS N. Productivity Growth in the Industrial Revolution: A New Growth Accounting Perspective. The Journal of Economic History. 2004;64(2):521–535. doi:10.1017/S0022050704002785

But note the true change lay in the associated structural change producing a fresh baseline platform that brought new capabilities and this was spread (diffused) across the entire economy and country. This baseline was a powerful shift which brought amongst many, the change from agriculture to factories and assembly lines.

Six centuries of British economic growth: a time-series perspective — Our main findings are as follows. First, when Hodrick-Prescott (1997) trends are fitted to real GDP per person, we find that, on average, trend growth was 0.2 percent per year over the 500 years from 1270 to 1770. Nevertheless, growth performance before the Industrial Revolution differed between sub-periods, with trend growth approximately zero on average between 1400 and the mid-seventeenth century, after which trend growth accelerated during the eighteenth century to about 0.3 percent per year. Following the industrial revolution, trend growth in real GDP per person peaked at about 1.25 percent per year in the mid-nineteenth century

This paper …. we also find that the hallmark of the industrial revolution is a substantial increase in the trend rate of growth of industrial output rather than being an episode of difference stationary growth.

The diffusion effect explains that the growth patterns are widely ‘diffused’ throughout the country and social structure. In the case of Britain there already existed cooperatives, guilds and a social structure which was local to every City and Town that naturally supported, encouraged and fully engaged in the new capabilities on a country wide scale.

I recall growing up in a town of 10K and the town was dominated by the Jute factory despite also being a centre for farmers, cattle markets. In relatively short time history showed that town and the surrounding county saw population movement of 200,000 leaving the country and shifting to towns to work in factories. Simultaneously the farms became more automated and operated with horses and steam operated equipment.

Structural change components required for AI to succeed

I always consider these catorgies in green field questions like this; People, process, technology and risk.

To understand better Innovation capacity, diffusion surplus and diffusion deficit alongside how we can think about our example, AI, and its early, medium and long term contribution to growth. This is all about the how AI will produce a long term Era impact.

The genius inventor experiences a Eureka moment. An awesome engineering marvel reaches completion. A new theorem changes everything. These are the images that come to mind when most people picture scientific and technological advance. Neglected in the collective imagination is the toil of diffusion: an invention becomes a standardized product, an engineering marvel is re-constructed in another context, a theory spreads from one institution to another

Again, technology leadership is cultural. The ‘diffusion deficit’ refers to the lagging multiplier effect of spreading the benefit of technology so that it becomes mainstream within that country. Where the effect is greater than expected this language would refer to ‘diffusion surplus’.

While core inventions in S&T (Scientific and Technological) produced steam power as a propellant, it can be argued it was Iron production which created the capacity to “diffuse, or widely adopt, innovations” and this generated the revolution. In this context we can think of the widespread adoption rates of factories and rail networks driven by that invention.

The concept of diffusion deficit or diffusion surplus lies in the country’s capacity to lever the invention. This takes the form of widespread entrepreneurial adoption. When we look at the centres of industrial development in England they cover entire counties across swathes of the country even today.

“In cases when the emerging power has a strong innovation capacity but weak diffusion capacity (diffusion deficit), it is less likely to sustain its rise than innovation-centric assessments depict. Conversely, when the emerging power possesses a strong diffusion capacity but weak innovation capacity (diffusion surplus), it is more likely to sustain its rise than innovation-centric assessments portray.”

Artificial intelligence (AI) currently has limited adoption in a narrow group of technology companies. Success in leadership countries needs to diffuse AI across the economy and the population in order to achieve systemic productivity gains, thus step change in economic growth for the country.

If we loook at the efforts of Meta, Google, Amazon, Microsoft (I will leave Apple for now) their AI approach is 100% self serving. I prefer to look at the example of Bloomberg News where 50% of their data set is Bloomberg specific, thus producing AI outcomes than introduce structural shifts in their employment, job descriptions and most importantly business process shifts that accomodate greater more efficient news reports, and are extendable as greater shifts improve ChatGPT and futher extend. I call this bespoke AI.

Predictions based on early study of ‘Diffusion Deficit’

Artificial intelligence (AI) currently has limited adoption in a narrow group of technology companies. Success in leadership countries needs to diffuse AI across the economy and the population in order to achieve systemic productivity gains, thus step change in economic growth for the country.

DeepMind talk about possible industrial policy shifts to engage mechanical engineers, computer engineers electronic engineering, and possibly new types of disciplines. The key point was to engage those disciplines to view AI with a new and different lens than their training to explore and lever the transformational benefits of AI. These transformational benefits would ideally be of the order of magnitude seen in Britain during the 19th Century shifting from manual labour and horses to a world driven by steam power, use of iron on a commercial scale and creation of factories. This would further go on to introduce early social mobility as the economy shifted from agriculture to industrial centred in towns.

The lens through which AI ought to be viewed is in this context; step changes to how economic growth is generated over the coming decades. Any early understandings could produce a future trajectory beyond imagination and certainly beyond the current view of AI as producing smarter search. To disregard diffision deficits will hinder productivity benefits and thus GDP scaling up.

In particular, “In cases when the emerging power has a strong innovation capacity but weak diffusion capacity (diffusion deficit), it is less likely to sustain its rise than innovation-centric assessments depict. Conversely, when the emerging power possesses a strong diffusion capacity but weak innovation capacity (diffusion surplus), it is more likely to sustain its rise than innovation-centric assessments portray.”

Mainstream narratives, meanwhile, only consider the U.S. ability to produce new innovations, neglecting their ability to effectively use and adopt emerging technologies. By revealing the gap between innovation capacity and diffusion capacity, innovation-centric assessments mistakenly inflate Scientific and Technological power.

Conclusion

These early conclusions lead me to see improved rollout approaches are required. The concepts of people, process, technology and risk must be amalgamated by smart companies, and if any government engagement is need it should be encouragment based through tax incentives and not restrictive based as the current AI direction from Government.