Artificial Intelligence - or AI as it’s commonly known - is becoming increasingly integrated into the daily lives of ordinary people. From accessible tools like ChatGPT to algorithm-driven social feeds (particularly well executed on Instagram & TikTok) and predictive Google searches, it’s almost impossible to go a day without encountering some form of AI.
The etymology of AI stems from the concept of automation, derived from the Ancient Greek "automatos", meaning “self-acting” or “acting of one’s own will”.
Perhaps the earliest record of this principle comes from Ancient Greece itself, with one of the first references to “a mechanical pigeon created by a friend of the philosopher Plato” [Tableau, What is the history of artificial intelligence?], dating to around 400 BCE.
A whimsical idea perhaps, but one that shows how humans, even in ancient times, dreamed of machines that could move - and think - for themselves.
Fast forward to 1726, and we find one of the earliest literary nods to AI in Jonathan Swift’s Gulliver’s Travels. In it, Swift describes “The Engine” - a mechanical device designed to help scholars generate new ideas, sentences, and even books [Tim Mucci, The History of Artificial Intelligence].
The idea of a “thinking machine” has existed for centuries. But when did those ideas start to take shape in reality?
It wasn’t until the 20th century that AI became more than a theory.
In 1914, Spanish engineer Leonardo Torres y Quevedo created a chess-playing machine - one of the earliest functioning examples of AI in practice.
The term “robot” then appeared in English for the first time in 1922, in the Czech play Rossum’s Universal Robots.
By 1966, the first “chatterbot” (or chatbot) was built, followed soon after by Shakey the Robot at Stanford Research Institute.
These milestones paved the way for the AI boom of the 1980s - a decade that saw significant funding, conferences, and breakthroughs including XCON, the first commercial AI system, and the first driverless car by Ernst Dickmann in 1986. But the true birth of AI - as an official field - came slightly earlier...
In 1950, Alan Turing published Computer Machinery and Intelligence, proposing what became known as 'The Imitation Game' - an experiment to test a machine’s ability to exhibit human-like intelligence.
That paper didn’t just inspire a Hollywood film; it inspired generations of scientists to question what intelligence really is - and whether it can be replicated.
AI has come a long way since the musings of Ancient Greek philosophers (some 2,500 years ago).
Since 2012, the rise of machine learning and deep learning has led to a surge in accessible AI tools - from voice assistants to recommendation engines embedded in nearly every website.
Today, Google Gemini, Microsoft Copilot, and ChatGPT dominate headlines - each promising to simplify, automate, and “humanise” our digital experience. And it’s not just the consumer world evolving - major financial institutions are rewriting their playbooks around AI.
Goldman Sachs recently launched its OneGS 3.0 strategy, a sweeping internal overhaul designed to embed AI across its global operations - streamlining workflows, automating reporting, and reshaping how the firm manages client interactions, risk, and data.
Executives have stated that they are looking at every single process in the firm and thinking about how AI could interact with this process.
For us - as a tech recruitment firm deeply connected to the trading ecosystem - these aren’t just statistics. They represent real shifts in how technology, finance, and data intersect. Our clients are hiring AI researchers, data scientists, and infrastructure engineers to build the next generation of predictive models and trading systems.
AI isn’t abstract theory anymore. It’s infrastructure - embedded in how markets move, firms compete, and people work.
As the arms race in AI accelerates, the questions grow louder:
What’s the true purpose of AI? When does innovation turn into dependency? And when the financial incentives peak - as they surely will - what comes next?
One thing is certain: AI is not slowing down. It’s refining, learning, and evolving - much like the humans who created it.
Whether you’re an investor, engineer, or end user, this is the golden age of AI. And if history has taught us anything - from Plato’s pigeon to Turing’s test - the story is only just beginning!
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