Who actually asked for AI and do we really need it?
By Simon Vumbaca
SYNOPSIS: AI is everywhere—and we’re told it’s indispensable. But when something is presented as a universal need, it demands deeper questioning. In this thought-provoking reflection, The Contemplationist asks four simple yet disruptive questions: What problem is AI solving? What need does it serve? Who had that need? And—who actually asked for it? Backed by data (over $3 trillion in AI investment since 2020), and contrasted with global spending on medicine and defense, the piece highlights a silent truth: AI may be a solution in search of a problem—engineered more by capital and theory than by human demand. From mass job displacement forecasts to vague promises of “efficiency,” the justification for AI feels more like marketing than meaning. This is not a rejection of technology—it’s a call to pause. To reframe progress. Because if AI is the answer, then we must ask: What exactly was the question?
As the latest storm is battering the south of England coast, a seagull is flying by the seashore, whilst battling strong winds. Whilst gliding in the storm, I am thinking: how much human progress has been seen by seagulls over the centuries. The most important for the seagull is possibly the fish and chips take-away box, now readily available on a bench near you! I also wonder what could be the most important human progress in the next century? Possibly A.I.?
A.I. headlines leave me abashed. Its presence is highlighted so much that we now take our need for it for granted.
The words of Ivan Illich: “Needs are created by the society that claims to satisfy them.” resonate somehow and inexplicably. As the Contemplationist, I believe that the moment something is framed as a need, it ceases to be neutral. It becomes a force that demands, if not blind obedience, certainly attention. I’ve learned that when something is framed as a “need,” it warrants the most fundamental of questions. A.I. should be no exception.
First, what is the problem that AI is resolving?
Second, what is the need that AI is covering? What issues did the existing system fail to handle effectively, that AI is now solving?
Third, who has the need that AI is trying to resolve? And,
Finally, who asked for AI?
The answers to the above must be very important, as in the last 5 years an estimated 3 trillion USD has been invested by venture capital, private equity funding rounds and alike (that is without counting the Government associated or collateral spend it generated), with over a minimum of 300 billions being committed in 2025 alone and a according to the Gartner research, 1.5 trillion USD in 2025 alone. Clearly, this is perceived as a necessity, particularly if compared to other spending areas. Medical spending on medical research in the last 5 years has been estimated to be at 1.5 trillion USD (ifpma.org–the International Federation of Pharmaceutical Manufacturers and Associations (IFPMA) which states to represent the innovative pharmaceutical industry at the international level and in official relations with the United Nations, here taken at face value). The reported Defence spending of the last 5 years in the USA and Europe being a substantially higher number (estimated combined at 7.3 trillion USD according to the SIPRI Military Expenditure Database. That does not carve out what the AI-related part of that budget is).
From the above, we could extrapolate that military expenditure is very important, AI comes close, and medical expenditure is further down the line. However, that could be a not so accurate interpretation, given that medical expenditure has been at this level for more than 10 years and AI is a more recent phenomenon. AI is a most recent need of a sort, that no-one seem to have identified where it comes from. Therefore, what is the need that we are trying to satisfy?
We often read that AI is essential in the workplace. That is being put forward as a reason for its need. According to the World Economic Forum own research, by 2030 an estimated 92 million jobs will be “displaced” because of AI implementation and adoption. Trying to understand if there is a direct correlation between the displacement of jobs and efficiency, profit, or any form of gains is data that, at the time of writing this piece, I have not located from a reliable source. At most, I located generic statements of “should” and often not considering any variations from the current state of the economy or business realities. The World Economic Forum also estimates that 78 million jobs would be “generated” thanks to AI adoption. I suspect that the 92 million employees losing their jobs have not asked for AI.
Maybe the answer to my basic questions is rooted in AI history? I must confess I went straight to the source on that one and asked AI to answer.
AI Development Phases and Global Expenditure Overview
| Era / Phase | Core Paradigm | Approx. Global Investment (USD, 2025-adjusted) | Capital Drivers | Key Notes |
| 1. Conceptual Foundations (Pre-1940s) | Logic, computation theory | <$1 billion | Academic / government research | Philosophical & mathematical groundwork; little organized funding. |
| 2. Birth of AI (1940s–1956) | Early computing, symbolic reasoning | ~$100 million | U.S. & UK defence research (RAND, DARPA precursors) | WWII cryptography & early computer science (Turing, Shannon). |
| 3. Symbolic AI Era (1956–1974) | Rule-based logic, early neural nets | ~$3–5 billion | U.S. DoD (DARPA), MIT, IBM, RAND | “Golden age” of academic AI; peak optimism; first AI Winter followed. |
| 4. Expert Systems Boom (1974–1990) | Knowledge engineering, logic programming | ~$20–30 billion | Corporate R&D (DEC, Xerox, IBM), Japan’s 5th Generation Project | Governments (U.S., UK, Japan, EU) fund AI industrialization; ends in over-expectation. |
| 5. Machine Learning Emergence (1990–2010) | Statistical learning, SVMs, early neural nets | ~$150–200 billion | Tech corporates (Google, Microsoft), universities, early VC | AI enters products—speech, recommendation, finance. Low visibility, steady growth. |
| 6. Deep Learning & Data Era (2010–2020) | Neural networks, big data, GPUs | ~$500–700 billion | Global tech giants (Google, Meta, Amazon, Tencent, Baidu) + VCs | Massive capital inflow: infrastructure, compute, and AI-as-a-service models. |
| 7. Generative AI Revolution (2020–2025) | LLMs, multimodal AI, foundation models | ~$2.0–2.5 trillion | VC + corporate investment + sovereign wealth funds | Explosion of private investment (OpenAI, Anthropic, xAI, Nvidia, Microsoft, Amazon, SoftBank). |
| 8. Strategic / Sovereign AI Era (2025–Present) | AI governance, autonomous systems, AI as policy infrastructure | Projected $3–5 trillion (2025–2030) | Nation-state industrial policy, defence, infrastructure funds | Governments treat AI as critical infrastructure: defence, education, productivity, and sovereignty. |
Key Sources and Benchmarks: Stanford AI Index 2025 Report – Global AI investment overview/ OECD AI Policy Observatory – National AI expenditure data /SIPRI & NATO – Defence AI funding trends/ McKinsey Global Institute (2024) – Corporate AI adoption and capital expenditure forecasts/ Crunchbase & PitchBook (2023–2025) – AI startup and venture capital volumes/ World Economic Forum (2025) – AI as critical infrastructure analysis
Progress is a great thing. It certainly served humans well to date. There is no evidence yet to prove that AI is progress, though.
Progress means “process of a series of actions, events, etc., through time”. As such, I take AI to be considered progress, as a series of actions have been taken through time, according to the literal definition in the Oxford Dictionary. The second definition of the Oxford Dictionary state that progress is : an “advancement to a further or higher stage, or to further or higher stages successively; growth; development, usually to a better state or condition; improvement; an instance of this.”
To me, it is not yet clear whether AI is driving a better state or condition. Certainly, it is to some and not too many. Not yet anyway.
So what is the problem AI resolves? Apparently, it can resolve them all. As soon as one tries to dig into one specific area, we read that it will be capable, later on and further down the line, to resolve that specific problem, but for now not yet. So no specific problem is being resolved. Gain of time seems to be referred to as the main resolve. Going fast seems to some to equate progress.
What its need? As all newly packaged dreams, we do not know exactly and we attribute to it all the needs one wants. So no specific need. A want, but not a need.
Who had that need? If there is no such need, who had that want? A philosophical intellectual debate of mimicking human brain response, and that could bring a consistent advantage, seems to be the angle expressed. So that want belongs to whomever wants the advantage. In other words, if a fragment of society wants more of one thing, the use of AI may help that fragment of society to gain a provisional advantage. There is likely no long-standing advantage, nor a widespread advantage outside of the specific fragment of society that wanted it.
Who, then, truly had that need? And if no such need ever existed, then we must ask the more unsettling question: who had the want—and to what end? What presents itself as necessity reveals a far more strategic impulse: the intellectual ambition to replicate the human mind in order to (likely) secure some form of advantage. Not an advantage for humanity, but an advantage for those who first imagined themselves entitled to it for whatever specific goal they had set themselves. In this light, the “need” is explained for what it is—a desire dressed in inevitability. An experiment elevated to systemic doctrine. A private aspiration recast as a collective destiny.
When a fragment of society seeks more power—more speed, more reach, more control—today they turn to AI as its instrument. It may grant that fragment a momentary superiority, a provisional dominance in the race it has itself defined. But let us not confuse that with progress. Such an advantage is neither enduring nor universal. It does not belong to society; it belongs to the few who demanded it first. And what is born from want, not necessity, may shape the present—but should it govern the future of all?
Who asked for it? It was an intellectual construct. No one seems to have asked for it. Though now it looks that we all want it. Save for that blissful seagull that keeps gliding above all of us waiting to bless our next big progress.
PS: this article has been written without AI save for table 1
Simon Vumbaca is the Contemplationist – This article has sparked or provoked some reflections? Great! Share them with us. Access this and other Contemplationist Insights at www.simonvumbaca.com