May 25: Automation Blackout?

This newsletter is a combination of my own analysis and insights, informed by publicly available information and industry trends. It’s my own manually written work and hope can benefit in term of authentic and creative content creation.

All my comments represent my personal opinions about Digital, Data and Technology trends in enterprises, based on the news we can all read and my correlations for further conversation and exchange always constructive and respectful.

This newsletter is intended to come on a monthly/quarterly cadence based on the relevant topics I believe make sense to share and will keep a structure as much as possible technology and vendor agnostic. I could miss to identify some relevant topics that were somehow not in my radar of regular read. Feel free to suggest further arguments missed or to keep an eye on.

A part “What could happen?“ to give future predictions and correlations for the upcoming months and a section “So what?” to comment on former guesses and how they went.

A part with Revenue Opportunity or Costs Optimization/Ops Excellence or Risk Avoidance as three highlights that can be found in some sections when I see hints in how Technology could be used to achieve those aspects

The hints I recommend represent just a minimal fraction of the possible ideas (the what) I could suggest and there are several ways to apply them (the how) based on different variables, conditions and prerequisites part of my consulting analysis and execution. Each business is different and best practices help us to define the base on which we can build up the extra unique value.

Comments are enabled only on Linkedin to keep all the structure of feedback in a single place.

• Intel is in restructuring and costs cut (20% staff to go) , delayering and accelerating on focusing on businesses units relevant for the future.
• TSMC has the journey to accelerate production in US addressing the AI chipset restrictions from US to China and the tariffs on Taiwan. Anyway the challenge to produce in US, especially the 2nm, with restriction to China export and Taiwan government trying to keep the most advanced technology limited to own country, could be challenges to TSMC that is already raising for example on the limits of control the AI chipset export to China.
• Huawei meanwhile aims to produce AI chipset in China, competing with Nvidia. In general China is working to produce AI chipsets locally and gain independence and there is high tracking from US to look for their progresses even monitoring via satellite for the production lines development in this area.
• Nvidia getting squeezed between US regulations on export of H20 chipsets for AI to China and the China development needs. On top on the base of the chain there is still TSMC in many cases.
• So What?: Collaboration between Intel and TSMC could become reality and I predicted that here where I supposed a piece of Intel to be sold to TSMC.
• What could happen?: China has already proven in the past to be able to step up own supremacy. The fact there is a gap on AI but a company like Huawei is strongly on it and there is also capability to build the underlying semi-conductor part, could make the delay from US to China on the run for AI supremacy maybe not that long. At the end is not a run for the next 12 months but is going to continue for the next many years to come and distances could shrink during such time.

I can only mention this month to be careful on this market and where to invest. I really prefer to avoid continuing speaking about it, especially as I see speculations ongoing on meme coins and people still brutally landing on them as way to invest. Stay curious but also vigilant on this.

• The acceleration in the run for supremacy in energy production is quite big. China recently ranked highest on nuclear power production rate. Much local mini-nuclear productions are ramping up, reducing dependency on big grids. Also on nuclear fusion (being the future target for clean energy) is also a big toss where Europe looks a little bit behind, apart France, against China and US. Just to take in consideration that also in the area of nuclear fusion, there are a lot of semi-conductors needed that could make the chipset war even more aggressive as the AI, chipsets and energy are tight connected and depending each other while increasing demand.
• On the path to balance production and consumption of energy, an interesting decision came from Kuwait that has forbidden mining of crypto recently as cause of blackout from the government perspective.
• Recently we saw also Spain/Portugal struggled with blackout cause, apparently, coming by remodulation problems from renewal energy not balancing as expected in the grid. From what we can read so far, the increase of renewal energy (that happened anyway over years) has caused a different way to react in case of peaks (positive or negative) than the past that has been not properly captured by the expert system, basically an AI system working in automation. Here we are in the phase of assumptions and there is yet not a formal root cause summary and EU just started the investigation formally. Hopefully this will be proof to be NOT cyberattack driven as that would be even more fragile from a perspective of impact.
• What could happen?: Here I like to give some level of predictions and speculations more on the general AI. A grid is a complex engine, with a lot of components, sensors, automations and is acting as complex expert system with several level of robustness and fault tolerance and working within specific tolerances to automatically decide. In this specific case the down happened after 5 seconds from the start of the problem. If the assumptions we read will be right, it could look like that environment over the time changed and the tolerances didn’t adjust. It’s a super over-simplification to make more understandable my reflection. Now, if an unexpected change in the environment caused such problem in a such complex but monitored, maintained and powerful automated engine, we need to take in consideration that automation in day to day jobs in enterprises has also some level of risk if not properly designed and maintained. Autonomous engines, like Agentic have possibilities to adjust automatically on unpredicted situations but will also have possibility to act in unpredictable ways when outside safeguards, so basically boundaries. So will be relevant together with the data training, the reasoning, the maintenance to keep such environments under proper monitor. I give these examples to mention that in some rare cases, behavior can go against the plans and cyber can also force behavior changing perception of environment with false information. I analyze more in the section Job Evolution on the transformation around Agentic for enterprises job activities that are clearly less complex and business critical than such infrastructure of grid. Here my predictions on the blackout will come more real once we will be in clear on the root cause. Like I said, I hope it was not a cyber event that manipulated the environment making the AI engine failing.

In this context, we saw US starting to deep dig in oceans and such action is considered high environment impacting. Even China was against it. From a technology point of view, that remains my focus, the competition is again for semi-conductors minerals as reason to act to deliver for the different areas of need.

One of the major changes coming with the Agentic is the autonomous capability to understand, remember, take decisions and evolve. There is a big dependency on the interpretation of the request, data source for training, restrictions, safeguards and the generation of the output. A special evolution in the area of LLM, that is also influencing Agentic, is the reasoning, that is the capability of making complex correlations and also to describe the steps executed in the chain of decisions. Recently, Anthropic made an analysis on the fact that the reasoning models are sometime hiding some of the steps they used to take a decision. Who follows me from some months, knows that I report often the challenges around governance of Agentic, especially if we start to have them combined in a swarm approach. If we cannot derive the sources (due to distilling approach on LLM), we are definitely limited in our understanding of how an agentic took a decision.

What could happen?: Agentic used in core processes, taking decisions that cannot be fully audited because part of the reasoning process could be not visible. On top deriving source of data trained could be impossible. As consequence, agentic could be considered not reliable and not auditable. I recommend to apply for non critical agentic at this stage and work heavily on the quality of the training data and the boundary to limit where to look for decision paths. On top feedback to AI producers will be relevant to drive toward a direction of completeness.

Microsoft released an interesting article on the future firms marked as Frontier. In such article, based on big amount of linkedin data (they own) and office 365 data of millions of users, they can see the influence that AI will have in agentic mode in many activities based on the actual level of repetitive tasks, the change of demands of skills required over internet (Linkedin) and the general way to operate for people in the offices. They also analyzed some aspects of augmented people with AI. A key aspect of the logic is the shift of the job (and I will speak more in the Job Evolution section in this newsletter) from “I send emails” or “I write documents” to “I create and manage agents“. I will speak more in the section as I mentioned. What I reflect here is the importance that such transformation will be properly regulated because there is an intrinsic difference between sending emails (where we own the action) than building an agent, that is autonomously acting and could execute things not in full alignment with the creator.

The direction toward “off the shelf” AI will be progressively more relevant and that will drive the build of the core components.

Last month we saw also a progressive interest to create action figures by many people and socials were filled up with them. At this stage there are hidden problems of privacy risk and people are often under-estimating the way such information could be used later. I remind here because in the last few months I mentioned in my newsletter how many cases we had of data not formally authorized but used to train AI. This is specific for example for many newspapers, authors and artists that started a fight against AI LLM engines using copyrighted data for their training. The regulations around AI are still quite weak and where EU made an effort, maybe quite formal, many other countries are completely out of such agreement and are not respecting some basic aspects yet.

On this topic, there is a proximity between tariffs war and robots for automating more and more production and reaching even higher cost effectiveness. In this sense China is progressing. I assume that if such process will be fast enough, would make easier for the US to bring back production in US without the need of expert workforces as that could be fully automated and potentially autonomous with the AI capabilities out there meanwhile. Tim Cook some time ago mentioned the level of specialization needed for bringing together the iPhones and that was why would still use local production in China (costs effectiveness and level of precision). In the years he started to build also in India and recently shifted China production for US to India, to come toward the US administration that wants to limit China dependencies. Definitely I agree that robots can mitigate the difference in cost of workforce to operate because would be similar cost in any country where today people have a different cost in different places of the world. Such robots would make offshore productions less convenient. It’s not clear how fast this would really be possible and how the gaps in jobs (where robots would replace people) would be absorbed with new jobs and how fast. There is in general a multifaceted capability in humans that is far from one task robots but also by one task AI, even if those tasks can be chained.

I recap what I started to mention in the AI section. Microsoft Work Index Report is mentioning the evolution toward Frontier enterprises where the need of distinct roles in the organization is reducing thanks to AI. In such model, the humans with AI get more capable to execute actions in different areas and there is a transformation from “send an email” to “create and manage an agent” in the way to operate several tasks. This paradigm shift is justified by the fact that each human works with augmented AI in a sort of modern RPA, low code or better no code way.

Now, an other article linked to that is the Harvard study around Cybernetic Teammate. This study, quite interesting even if limited in the number of examples and length of the test, is showing that an human with AI is beating humans without AI and even teams (of two people) without AI.

In the study the team is based on one commercial and one R&D and if one human of the two types without AI would make decisions quite influenced by his/her own skills, one human + AI is able to take decisions similar to a team composed by the two different competences and even beating them (if the team is without AI). So AI is leveling competences and reducing gaps, allowing to take decisions not influenced by the specific only competent of the human but working more cross functions.

My 2 cents on the study is about the fact that has been quite short in time and only on 2 member teams so the correlation and mutual feeling that a team could build would be too limited in time to proof the augmented value from the team with or without AI versus the individual.

Definitely clear instead is that an individual without AI is definitely less performing than an individual with AI in such test and that is a key aspect why the upskilling is going to be fundamental in the coming future as I mention from many months also referring to official reports like the one from the WEF 2025.

There are many challenges to understand. First of all reliability of AI answers, the training data, the governance and regulations that we already mentioned in the AI section and finally, most relevant for me the accountability. We can build an agent and make it autonomous to operate, replacing some actions we do, take even the example of the send email or write a document. However if we send a wrong email or write a document with wrong contents, we are accountable for the contents and if we create an agent doing on our behalf a reply that one day reacts unexpected, we are still responsible for the contents we send out and that is based on the engine selected by the enterprise we work in, the proper safeguards and training, the reasoning (as well) and all the possible unpredictability coming from an autonomous agent. On top we need to take care that an agentic could answer to emails on our behalf on a base of knowledge but could be manipulated by who is requesting information to get around the safeguards. That is making even more relevant to make such agentic progressively properly maintained.

The other challenge I see is about the fact that an AI leveling the contents and knowledge is reducing the difference and specialization between the departments. In this sense the Microsoft article speaks about future new blueprint for organizations. That has to be clearly foreseen in advance by companies when they like to design the future way to operate as specialization is anyway something bringing variety of perspectives and the AI need to be seen to complete the human perspective, not to make it irrelevant.

Other aspect related to the same topic is about the way the new automations will transform in term of resources. There is blur area in the way humans act that is combining chain of sequential skills and that is not typically immediate to be AI replaced. No-one is doing just a task, but is often a mix of some repetitive tasks (automation capable), some reasoning and some other action as effect of own correlations. If we take only some specific skills and we automate or make autonomous, we can believe in the 1 hour saving per day that could be achieved for example by repetitive tasks we do regularly and that level of efficiency need to be balanced by more productivity (more volume or higher quality or both). At the same time, the effort to maintain our agentic reliable, requires some level of evaluation, especially in the first phase, of their reasoning, like we are following a junior employee in some basic tasks in the daily job, then we leave him/her more free but from time to time we check if everything is right.

Key aspects going forward are to describe key processes where scaling capabilities are needed and be able to design which parts will be more relevant to be automated to accelerate the scaling. Clearly enterprises of smaller size will have more agility to transform but the impact in big enterprises will be highly beneficial as more structured and formal processes are in place and can be better estimated the improvement from automation.

One final aspect that I consider relevant in the workforce upskilling that will have to happen in the near and middle future is about the junior and in general GenZ employees. As many of them will enter in enterprises with already AI integrated in the way to operate, they will be requested to have more complex decisions from the early stage to take but will miss part of the initial experience that would help them to mature on taking decisions. I believe the job of the higher management will have to include the effort to train such workforce to balance their missed experience and will be an opportunity to create long lasting relationship as part of a generation development that each enterprise will have to execute.