Table of contents

Introduction

AI is one of the hottest topics for discussion over the last few years. It has been a prominent subject at major technological conferences and has driven sales worth billions of dollars in stock markets worldwide.

It is being hailed as one of the major breakthroughs of our time, and many people believe that computers will soon gain consciousness and stand alongside humans as fully sentient beings.

The long-promised Generative AI (GenAI) is expected to usher in a new era of prosperity for humanity.

Or is it?

This is what we will try to analyze in this article:

  • Is AI really what it’s advertised to be?
  • Will it deliver on all these promises?
  • What can we use it for?

An Attempt to See Through the Smoke Screen

First of all, we need a clarification: there is no such thing as AI as a distinct technology—it’s not a singular entity or a finished product. Rather, AI (Artificial Intelligence) is an umbrella term—a marketing label—that groups together a wide range of computational techniques aimed at mimicking aspects of human-like cognition.

For decades, we have used machines to perform non-deterministic computational tasks—tasks where outcomes aren’t fully predictable from the input alone. Over time, different terms have emerged to describe these capabilities: expert systems, neural networks, deep learning, machine learning, and so on—all significant milestones in the evolution of what is now branded AI.

Each of these technologies has its own significance and has contributed meaningfully to today’s AI revolution. The underlying approaches have evolved continuously: algorithms have become more sophisticated, data availability has surged, and hardware has scaled dramatically—yet substantial variation remains across methods, architectures, and use cases.

That said, the surge in public interest—and the popularization of the term AI—has largely been driven by Large Language Models (LLMs). These systems are capable of simulating human-like conversation in natural language, generating coherent text, code, and more.

LLMs—and AI systems broadly—come in many forms: some are general-purpose, others highly specialized. Some require massive computational resources (e.g., training or running billion-parameter models), while others can run efficiently on a standard laptop—even a mobile device.

There is still enormous variation among these systems in terms of:

  • underlying architecture (e.g., transformers vs. decision trees),
  • trained capabilities (e.g., reasoning vs. pattern recall),
  • data requirements,
  • and computational footprint.

Yet, despite all these differences, we collectively refer to them as AI.

Is It Actually Useful?

Absolutely yes.

As we mentioned in the previous section, we have been using it for years. AI or ML (Machine Learning) has been in continuous use for many, many years.

It has been used for detecting trends (market or social), for optimizing search results, for predicting our next word and providing alternatives and spelling suggestions, for transcribing audio, and for translating text.

It has been used in improving our photos and beautifying our selfies.

It has been used everywhere. We just did not call it AI then; we usually called it “the algorithm.”

The algorithm of Facebook, of Google, of TikTok, of Snapchat, and so on and so forth.

It was built into almost all online platforms and almost all phones. All financial institutions used it to create profiles for their customers; all trading houses used it to predict the behavior of the markets.

All retail and hospitality used it to predict what the next best trend would be.

All advertising companies used it both to profile people and to sell ads.

It will eventually be used everywhere, from consumer devices such as mobile phones (already happening), all the way to industrial equipment where it will be used as part of production, quality assurance and manufacturing.

If you are a farmer you could have a weeding robot detecting individual weed plants and zap them.

The influence of AI in modern professional environments can not be ignored.

Is It Actually Sentient?

Absolutely not.

All AI does is try to identify the most likely answer to a question, within a certain level of uncertainty, using some initial training datasets.

This also applies to all scientific fields that use statistics to do their jobs, from social sciences to engineering, medicine, etc.

When working with stochastic models, you can never guarantee a 100% success rate. You can often provide pretty good results, though.

But in order to do so, you need to control all the parameters of the experiment.

If you have garbage in, you will most likely have garbage out. Many of the AI models available today were trained on unsafe datasets—datasets that contained factually incorrect information and implicit or explicit bias.

As such, even though the technology may be sound and could potentially produce good results, the final result cannot be trusted.

This means that the original promise of many AI companies—that it could replace their workforce—is false, and those people were hyping and mis-selling what AI could do.

AI is not sentient, and it cannot be held accountable for its actions. If it gives false health advice to people and those people are harmed because they followed the AI’s advice, it cannot be held accountable for its actions.

Even though these weaknesses can be addressed, and there are quite a few clever tricks a model can use to minimize the uncertainty of its answers, the above statement cannot be bypassed.

Will It Take Our Jobs?

Unfortunately, the answer to this question is a partial yes.

It is the same question that people asked during the Industrial Revolution and also when the first automated loom was introduced.

AI is most likely the next major productivity enhancer, which means that it will allow a user to do more with less.

This inevitably means that someone is losing business. Someone’s sales will drop, which means that someone will lose their job because of this drop in sales.

Is this a bad thing?

Of course it is.

How do we avoid it?

The answer is still the same as with all those previous times this question was asked. We need to adopt AI in our workflows and focus on training to do what AI cannot do.

Focus on solving problems people have that AI cannot solve independently.

Retrain and refocus.

Environmental and ethical concerns

Environmental Issues

AI companies are being accused of consuming too much power and too many resources.

This is absolutely true.

But then again, this is true of many industrial enterprises. It is interesting that it is only now that people have started wondering about datacenter power consumption.

They did not think to challenge the power consumption an Amazon, Google, or Microsoft datacenter was using five years ago, when it was still using AI—but under a different label.

People calculate today how much energy an AI-based Google search has consumed but never bothered calculating this 5 years ago, even though Google was using AI even then to optimize search results.

Or they never asked how much energy went into curating their algorithmic Facebook or Twitter feed.

But then again, Machine Learning was not a hot topic then. AI—which, I might add, will become sentient any time now—is hot today.

Even non-technical people know of its existence, so it is a good topic for a newspaper article.

Having said all that, the foundation of this argument is true. Industrial enterprises such as AI, cryptocurrencies, large-scale datacenters, or even scientific enterprises such as the Large Hadron Collider need a lot of power, and we should make sure that the environmental impact of such enterprises is as low as possible.

It is in times like these, when the industry turns its attention to a problem for its own needs, that they are fully motivated to find interesting solutions.

In this particular case, I believe that the push for AI will also mean a boost for green energy.

We cannot discover new oil fields whenever and wherever we want, but we can easily take a previously unused industrial estate and plant a solar farm in it and make it produce the energy we need.

In case solar panels are not efficient enough, hey, we have a newfangled productivity enhancer called AI that we could use to make things better.

We can also use it to improve the efficiency of other green energy sources, including nuclear energy.

I believe that even though government policy introduced the migration to a carbon-neutral economy because of climate change, the need for extra energy for AI will accelerate the evolution of green technologies and eventually benefit the whole society.

Ethical Concerns

Oh boy, this is a real stinker.

I am not sure if there has ever been any significant technological advancement with as many ethical concerns as AI.

LLMs started their life by plagiarizing the whole internet, even without consent from the owners and creators of the content.

Major tech companies have taken advantage of terms and conditions agreements designed ages ago to allow people to scan their email and personal data for spam detection and security purposes to train their models.

Artists find their works of art ungracefully scraped by AI companies and then reused without license or permission to create other works of art.

Journalists and authors find their work also scraped without license or permission and used for training these models.

People find their images edited without their consent and then posted again on the internet, with or without clothes on.

Scammers are using all of the AI tools to impersonate people, steal their identity, and money.

Only recently I read about a scam where scammers impersonated people’s voices using AI in order to make money transfers or place orders using phone services that use voice recording as proof that the owner actually requested these transfers or orders.

There are even more concerns when it comes to AI-enabled social media that use psychological manipulation to increase engagement at the detriment of their users’ health, mental or physical.

I could go on and on and on, but the fundamental question is: who is responsible for the use of a tool—the tool itself or the user?

The answer is the user. Always.

So all of the above could have been avoided if the owners of these models took a more responsible approach.

But alas, this was never meant to be.

This is a gold rush, and in a gold rush, you do not have a calm discussion about where to dig. You have a stampede.

It is part of human nature.

But it didn’t have to be this way. The companies that participated in this gold rush are not half-starved diggers. They have balance sheets that are bigger than the national budgets of some countries.

They could behave as responsible adults, but they didn’t.

There is no excuse for that.

Unfortunately, the situation is still evolving, and there is no indication that the protagonists of the story are taking into account the risks of what they do at this breakneck pace.

People will be hurt because of this sloppy and haphazard approach, and the AI companies will have to answer for it. But I suppose they don’t care. They have enough cash to challenge anyone in court, and in the worst case, pay whatever fines they get slapped with.

Everyone in the AI race is busy making money.

Cloud-Based and Local AI

AI and machine learning do not necessarily have to be cloud-based. Nevertheless, in the last few years, they have been dominated by cloud-based operations.

It was simply too expensive to buy and own the necessary hardware to make LLM training possible.

As the technology evolves, however, we see models emerging optimized for local execution. These are often referred to as edge-optimized models.

Where cloud-based AI is often criticized for its high environmental footprint, local AI execution does not have this problem. It can be part of our local computational operations, which means that power consumption and overall environmental impact are considerably lower.

I do not believe that local AI models will replace cloud-based AI compute, because some things are just too large to run locally, even for big companies. These use cases—industrial and environmental simulations, protein/medical research—will still require cloud-based AI.

However, as technology evolves, local models should be able to cover the needs of most people. The ability to use a local model as an expert system exists today.

We can run local AI models that are multimodal, which means that they can understand multiple forms of input. Text, video, or audio inputs are quite powerful and standalone. They do not depend on any external resources.

They can, of course, be extended by the use of external resources, but they can also operate offline.

This last property makes them ideal for isolated/sensitive environments.

When the other major concern of cloud-based AI—apart from environmental impact—is privacy, and the very well-documented past history of not respecting creators’ licenses and just scraping content anyway, many people believe that any information submitted to any online AI system will eventually become part of its training set.

This is not the case for local AI models. These models are isolated, and as the phrasing goes, “what happens in your local AI stays in your local AI.”

So these models are ideal for environments that require discretion and privacy.

Another potential use case for these local models is their use in embedded devices. I fully expect, in a few years, to see AI-enabled embedded systems, either in the form of household/industrial devices or in the form of personal computing/communication devices such as phones, tablets, etc.

I expect a lot of the AI work to happen on-device for most major phones, thus preserving the privacy of their owners. The same thing will apply to smart home devices and to smart computing devices such as routers, firewalls, etc.

But we will also have AI-enabled field devices, like the robot weeder I mentioned previously, but also AI-enabled drones, AI-enabled irrigation systems, and so on and so forth.

Eventually, we will see specialized AI models in embedded devices that will partially replace the hardcoded logic that we see today and allow for more flexible operation in the field.

A good example of this is firewall rules in embedded firewall and router devices. Using an AI model that can do attack pattern recognition could potentially provide us with more flexibility than the hard-coded block lists we use today.

Open Source AI

At the periphery of the AI giants, we gradually see an AI open source community emerging.

This is not a new thing. Even OpenAI was initially meant to be a non-profit organization. There are several major companies that have released versions of their models as open source, even though we often do not have full visibility of their training set and what has gone into that training.

Despite all that, the existence of these open source models is crucial.

In the same way that the open source movement allowed people to learn, experiment, innovate that eventually created the amazing technological wealth we see today, we need a robust open source community for AI as well.

It is the only way to heal the wounds of the past where AI evolution hurt people in the process. We need ethical AI and open AI.

It is also the only way that niche groups could support their use cases. The major AI players will always focus on where the money is, but that does not mean that fringe ideas are not important.

Most of today’s scientific breakthroughs started their life as fringe ideas, and quite often it took a long time for the original scholar or entrepreneur to make the world understand the value of their innovation.

One of the most recent examples of this is mRNA vaccines.

So we see various communities and tools emerging that support and encourage the development of open source AI models, tooling, tests, and data sets.

We can name a few here.

Hugging Face is an online community where people can collaborate on building AI applications. From here, you can collaborate with people and download, upload, design, and host AI models, applications, tests, and data sets.

It reminds me of how GitHub has helped communities form around specific projects.

LM Studio is a free tool that allows you to download and run open source models. It supports the Hugging Face format, several models, offers a graphical interface for interacting with the model, supports MCP integrations as well as RAG, but it also allows you to use it as a server offering both a CLI mode as well as OpenAI API compatibility.

You can run it in headless operation, and it can work with both GPUs or CPUs.

You can also use it as an interface to online AI models if you have an API key for them and a subscription.

AnythingLLM is another tool that can be used as an interface to either local or online models. It can run models on its own, or it can be combined with Ollama as its backend for local execution of models.

Ollama’s purpose is to allow us to run automations using local AI models, so it can be used as the backend for various automation tasks.

Another tool focusing on automation and potentially local execution is OpenClaw. OpenClaw is a personal AI assistant that you can run on your own devices, and you can use it to create integrations across a number of services.

It is primarily designed to work with Anthropic and OpenAI models, but the important thing to remember here is that most of the tools we mentioned previously support the OpenAI API. So we could use OpenClaw with any one of those local models instead of the cloud based ones.

With OpenClaw, someone could use an AI model to drive automations—for home or otherwise—using messaging systems as control planes, in the same way we can use Slack bots to trigger remote actions.

Of course, it can also be used to receive notification events from your various automated systems.

Conclusion

I understand that some people—quite a lot of people—are hurt by the use of AI, either directly or indirectly.

AI is a technology that can be used for good or bad. At the very minimum, some people will lose their jobs; it will be used extensively by scammers and crooks to impersonate people, steal their money, and steal their identities.

It will be used by bad actors to create fake videos and misinformation.

Not using AI for good purposes does not mean that these bad actors won’t use it for bad purposes.

My suggestion is that we should not let AI be used only by these bad actors. We can use it for good.

We can use it to improve our quality of life and create new solutions for existing hard problems.

We can also use it to detect and protect ourselves from any negative uses of AI.

The one thing we cannot do is ignore it. We cannot pretend it does not exist and go back to a pre-AI world.

The genie is out of the bottle.