As data and AI are major value creation drivers, mastering the related resources and capabilities leads to competitive advantage. With the usage growth of neural networks, a new resource is critical: electricity. That’s probably why the battle on AI will be also a battle on energy.

Data and AI value chain, resources and capabilities

From raw data to value capture, 4 main blocks compose the data and AI value chain.

• Data generation refers to activities and assets necessary to capture and record data. It blends data accessible directly by the company (ERP, social media, website and applications, connected device data, ….) and data the company sources from other companies or organisations (open data sets, bi-lateral data procurement agreements, …).
• Data collection consists in activities and assets to collect, validate and store data. E.g.: cleansing, reduction, integration, storage infrastructure and models, security, …
• Data analysis refers to activities and assets to analyse and generate insights. E.g.:Semantic analysis, models (Predictive, Descriptive, Prescriptive), visualisation (graph, maps, 3D, …), …
• Data exchange refers to activities to expose outputs internally and externally. E.g.: decision making, trading.

Companies who capture value from data master directly or indirectly all four blocks.Technology and solution providers are either specialised in one sub-components or integrators of several of them. Make-or-buy decisions embody strategic positioning and bargaining power within this value chain.

Through this value chain, different type of resources are leveraged. Some are tangible (data, technology, infrastructure) while other are related to the workforce  (technical skills, managerial skills, domain expertise, relational knowledge) and others are intangible (data-driven culture, organizational learning). These resources are used through technical capabilities (text mining, web mining, social networks analysis) and analytical capabilities (statistics, optimisation, modeling, machine learning).

Defining a distinctive positioning requires to answer a set of strategic questions:

• what are the threshold resources and capabilities we need to master to be on par with the other players
• which part of the value chain do we decide to master internally (with the corresponding investments) and which part should we transfer to supplier or partners
• for which capabilities do we decide to outperform the competition to create an edge
• how can we secure an exclusivity to a valuable resource to increase competitive advantage
• how do we control the maximum value of the chain

With neural networks electricity becomes critical

Kate Saenko, associate professor of computer science at Boston University, warns. “AI is getting more expensive in terms of power to train the newer models.” For a detailed explanation of why neural networks are so power intensive, look at this article.

In a nutschell the power consumption of neural networks is due to two main reasons: the computing used to train the model and the computing used to infer new data from the model. Training the model takes a lot of computing. According to researchers associated with OpenAI it increases by a factor of 10 each year. The search for a maximum accuracy of the model requires a lot of training, A language processing model might be able to understand 95 per cent of what people say, but wouldn’t it be great if it could handle exotic words that hardly anyone uses? More importantly, your autonomous vehicle must be able to stop in dangerous conditions that rarely ever arise.

Neural networks require a lot of data to be trained, which requires a lot of electricity to store the data and to build and use the model.

As electricity consumption grows bigger, the ability to pay the energy bill and benefit from the most efficient energy production capabilities is critical for the most intense users of neural networks.

This topic is not totally new and it’s been years now that big tech companies have invested for developing their own electricity production facilities and in technologies to reduce electricity cost and consumption in their data centers. These investments can be interpreted from various angles.

• First, as electricity cost grows bigger, optimizing it is strategic.
• Second, the ability to access to a cheaper or more efficient solution than the direct competitors is a source for competitive advantage.
• Third, developing these solutions and capabilities increases the gap with non-digital companies which have no other choice than partnering on their terms with them to access to AI capabilities.

A cynical observer would even conclude that promoting the use of neural networks is a way for big tech to make non-digital companies dependent on a critical resource they master much better.

In AI, the battle on data, talents and chips is already pretty fierce, a new front has started with the battle on electricity. In this battle, incumbents have some valuable cards to play: they have been implementing strategies for resource control for decades.

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