Finding the signal in an increasingly noisy technological world

My dear readers,

Sorry for the long absence. For the past few months, I have been travelling around while spending plenty of time creating various teaching materials, primarily on AI-related topics.

You know how it goes: You have the best intentions to keep up with friends and family but something, no matter how small, always happens to delay things. It is pretty annoying really, but then again, sometimes that is the best way to describe life in general so, I have learned to take it as it comes and stop moaning about it!

Anyway, one good thing coming out of my intense “content-generation” work has been the realization that, given the crazy pace of change we are witnessing these days, keeping up with technology has become strangely difficult.

Now, as a technologist, this is great news for me as I am never short of new reading materials but even for someone of my background it has gradually become a real effort to remain informed and relevant.

Every day seems to bring another breakthrough: A more capable AI model, a humanoid robot doing something remarkable, a quantum-computing milestone, a new semiconductor bottleneck (is it a signal for a new investment opportunity?), an energy project driven by data centers, or a promising biotechnology discovery. And I did not even mention the next planet (or is it still Mars?) that Elon Musk is going to populate with, well, whatever he wants to populate it with today!

Some of these developments genuinely matter. Then again, many of them don’t. And even the important ones are often reported in isolation, when their real significance only becomes apparent once we connect them to what is happening elsewhere.

Anyway, the bottom line is that:

1. I have spent a lot of time during the past few months generating content, which has forced me to read plenty of content, and
2. I realized there is too much content to go through, some of which are critical, and
3. I noticed that to make sense of it all it is not enough to just consume this content (even when related to critical topics) in isolation, and I found myself thinking even harder to link various topics together.
4. Finally, it occurred to me that at this stage of my life and career (oh, in December 2025, I finally, and happily, left corporate life to concentrate on more interesting activities, such as research, content generation, teaching, writing, advisory roles, running, travelling, and sleeping!) I actually enjoy sharing what I know with others, especially the younger folks out there who may need some advice to make them less fearful of AI takeover and more hopeful about the future.

And all these have now given birth to my Weekly Technology Brief.

But “what is the Weekly Technology Brief?”, I hear you wondering.

Each Thursday, I will look across seven areas that I believe are particularly important to understanding the technological changes ahead:

• Artificial Intelligence
• Robotics
• Quantum Computing
• Semiconductors
• Energy
• Space
• Biotechnology

Now, let me be clear:

This will not be a comprehensive news digest. There are already plenty of places to find technology news, and I have little interest in reproducing another list of everything that happened during the week. I would rather identify four developments that genuinely changed my understanding than twenty or thirty that merely generated headlines. And boy, do we have plenty of headlines to go through every day!

No. I am not that kind of technologist!

The questions I am interested in are different. For example:

• What actually changed this week?
• Why does it matter?
• What evidence supports the claim and what remains speculation?
• How does it connect with developments in other fields?
• And what might it tell us about where technology is heading next?

Looking for the Connections

Increasingly, the most interesting stories don’t fit neatly into a single category (maybe that’s because life has become too connected and, therefore, exceedingly complex?).

Artificial Intelligence, for example, may appear to begin with algorithms and models, but its rapid expansion is creating enormous demand for advanced semiconductors. Those chips require increasingly sophisticated memory and manufacturing. Data centers require electricity, cooling, land and financing. Electricity demand affects grids, nuclear power and renewable-energy investment.

Follow the chain far enough and what initially looked like an AI story becomes a semiconductor story, an energy story, an infrastructure story and eventually an economic and political story.

The same is happening elsewhere.

AI is entering laboratories and helping scientists generate hypotheses and design molecules. Robotics can then automate experiments and generate new biological data, which in turn can train better AI models.

Quantum Computing depends not merely on increasing the number of qubits (don’t worry, keep checking my blog and I will soon put in some teaching materials to explain what on earth a qubit even is!), but on error correction, algorithms, verification and ultimately demonstrating that a quantum computer can solve something economically useful.

Robotics is not simply a race to build the most impressive humanoid (mind you, with so many dumb humans in charge of world affairs, it does not take me too long to feel impressed about any humanoid out there). The more interesting question may be which combinations of intelligence, mobility, manipulation and autonomy actually produce useful machines.

These connections are where I think some of the most important signals can be found.

Evidence before Excitement

Technology also has a hype problem. Now, I believe strongly that technological hype is, arguably, as old as time itself! Apparently, generating hype (some call it marketing) is a perfectly respectable and useful activity that has long been ingrained in our economy. But (and here is a BIG but):

• A company announcing that its new AI model beats a competitor is not the same as independent testing (what does “beating a competitor” even mean and based on what criteria?).
• A robot performing an impressive demonstration is not necessarily autonomous (you know a human operator may be managing this remotely, right?).
• A promising biological mechanism is not a successful medicine (has anyone even told us whether this medicine has actually produced a meaningful clinical benefit in patients yet?).
• A quantum company raising hundreds of millions of dollars is not evidence of useful quantum computation (does the company show their ability to solve at least part of a real problem?).
• And finally, an enormous investment in AI infrastructure does not guarantee an enormous economic return (although it certainly tells us something about the current level of investor sentiment!).

So, in my Weekly Technology Brief I will try to distinguish carefully between what we know, what the evidence suggests, what remains uncertain, and what is mostly speculation.

And the combination of what we know, the actual evidence and uncertainties is key here. If there is not enough meat to suggest a piece of technological news is worthy of more attention, that is what my report will emphasize. As a result, you will be exposed to conclusions such as “Interesting, but we don’t know enough yet!”. Well, I believe that is a perfectly acceptable conclusion when reviewing so much information coming at you from so many angles. At the very least, it signals that there is no point spending too much precious time on the topic unless something drastically changes.

Following the Bottlenecks

There is another question I will return to frequently in the brief:

If this technology succeeds, what becomes the bottleneck next?

The thinking is, remarkably, straightforward:

• Build more powerful AI models and compute becomes scarce,
• Build more accelerators and advanced memory may become scarce,
• Solve memory and networking (or electricity) may become the constraint,
• Build more data centers and the bottleneck may move to the grid, permits, financing or even local acceptance.
• Build this and that becomes the next bottleneck!

As some of you may already know from direct experience, technology rarely advances in a straight line. Solving one constraint often exposes another, and on and on we go.

However, following those moving bottlenecks can tell us a great deal about where innovation, investment and scientific effort may move next.

An Important Point to Stress:

Remember that I just said “…may move next”. Even with all the information coming our way, we should never “guarantee” an outcome in a world full of uncertainty. Just wanted to make sure your expectation of this brief is not sky-high😊.

What This Brief Is, and Isn’t

I hope it is clear by now that the Weekly Technology Brief is not intended to predict the future with false precision. Nor is it an investment newsletter, a collection of product announcements, or a catalogue of technological wonders.

Instead, it is an attempt to build a continuously evolving picture of how several powerful technologies are developing. And, especially, how these technologies are beginning to interact.

There will also be occasional Deep Dives on certain topics, which I will share on this blog as separate pieces. When do I publish these deep dives? When a subject deserves more than a few paragraphs. The next one (which will be the first one) could be about the AI memory bottleneck, autonomous laboratories, humanoid robotics, quantum error correction, AI governance, energy constraints, or whatever else emerges as genuinely important (hint: the first one is likely about the AI memory bottleneck😊).

The objective of the brief (and the deep dives that appear from time to time) is simple:

Less news, more signal. More relevant context, more insightful questions.

Every Thursday, I will ask one overarching question:

What happened this week that meaningfully changed our understanding of where technology is going?

That will be my job.

Your job? Follow the leader, leader, leader, follow the leader…