AI and the Myth of Creativity 1/4: Ada Lovelace's Objection
Can an AI be creative? This question is not new. It was posed at the very birth of computing, by two visionary minds whose intellectual duel still shapes our greatest fears and hopes today.
The Keeper of the Fortress: Ada Lovelace's Objection (1843)
Picture Victorian England, at the dawn of the Industrial Revolution. Amidst steam engines and weaving looms, a woman — Ada Lovelace, daughter of the poet Lord Byron and a mathematical genius — collaborates with Charles Babbage on a wild project: the Analytical Engine, a mechanical ancestor of our computers. “The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform,”(1) she wrote in her famous notes.
The key word is originate. For Lovelace, creativity is an act of pure invention. The machine, on the other hand, is a perfect executor, but only an executor. It can only follow the orders inscribed on its punched cards. For Lovelace, the Analytical Engine is like a mechanical piano. It can play a complex score to perfection, but it will never compose the Tenth Symphony. It plays the score; it doesn't invent it.
This “Lovelace Objection” establishes a fundamental difference between the human, source of intention, and the machine, mere tool of execution. A century and a half later, this distinction resonates strangely with the concerns of many designers facing generative AI: these tools execute brilliantly, but do they truly invent?
The Quiet Revolutionary: Alan Turing's Response (1950)
A century later, Alan Turing poses the question: “Can machines think?” He takes Lovelace's objection very seriously and responds with a subtle counter-attack that seeks to shift the debate. His response rests on three arguments that, even today, challenge our conception of creativity.
The Criterion of Surprise
Turing observes that even a simple program can have completely unexpected consequences. If a machine can produce a result that its own creator had not anticipated, isn't that already a form of creativity? Turing's "surprise" wasn't a mere calculation error. It came from his groundbreaking work on morphogenesis, the study of pattern formation in nature. He discovered that by simulating very simple mathematical rules (describing the interaction of two chemical substances) on a computer, he saw spontaneously emerge patterns of spectacular complexity and beauty, identical to a zebra's stripes or a leopard's spots. The machine was merely following the rules, but the result was an unpredictable and magnificent creation. It was this experience – seeing a simple calculation reveal a universe of hidden complexity, much like fractals – that forged his conviction. For him, computation is not mere execution but a genuine engine of discovery and creation.
For a designer, this idea is fundamental: how many times has a technical constraint, a chance occurrence, or a software "error" opened up an unexpected creative direction? Might creation lie as much in the ability to recognize and exploit surprise as in the initial intention?
The Myth of Human Originality
Here, Turing deploys his most subversive argument. Rather than defending the machine, he attacks the pedestal on which we have placed the human mind. Lovelace's objection rests on a clear distinction: humans originate, machines execute. Turing forces us to look this distinction in the eye and ask ourselves if it is as solid as it appears. His reasoning is an invitation to introspection: where do our most "original" ideas come from? Are they truly creations ex nihilo, born from nothing? Turing, with provocative humility, suggests not. He argues that what we call "originality" is often the complex and unpredictable result of a multitude of influences. "Who can be certain that 'original work' that he has done was not simply the growth of the seed planted in him by teaching, or the effect of following well-known general principles?"(2) he asks.
Every stroke of genius, every revolutionary work of art, is part of a history. It is a response to a conversation already in progress, a recombination of existing concepts, a variation on learned themes. A painter "learns" colors and forms before reinventing them. A scientist "learns" existing theories before surpassing them. A designer "learns" visual codes, user needs, and technical constraints before proposing a new solution. In short, the human mind is itself constantly "programmed": by our education, our language, our culture, our experiences. If we accept that a "programmed" being (a human) can be creative, on what grounds would we refuse this possibility a priori to a machine that has also been "programmed"? Turing isn't saying that humans and machines are identical. He's saying that the line of demarcation we thought so clear – that of pure originality – is in reality a mirage.
The Learning Machine
Ada Lovelace's objection was perfectly valid for the machine she had before her eyes: a mechanical automaton designed to execute a fixed sequence of instructions. But Turing, a century later, no longer thinks in terms of "calculating machines." He thinks in terms of "learning machines." This is where his vision becomes prophetic. He proposes to shift the problem. Instead of trying to program an adult intelligence with all its complex knowledge, why not try to simulate the process by which this intelligence is formed? Why not build a child-machine?
For Turing, the path to artificial intelligence is not to create a program that contains all the knowledge of the world, but to create a simpler initial program – the mind of a child – and subject it to an education process. This "child-machine" would be capable of receiving information (its "lessons"), modifying its own programming based on its "experiences," and evolving over time. This vision changes everything. The machine is no longer a mere executor passively following its initial code. It becomes a dynamic system, capable of reorganizing itself, adapting, and thus developing behaviors that its creators neither explicitly coded nor even anticipated. By imagining the learning machine, Turing anticipates, as early as 1950, the fundamental principles underlying today's Deep Learning. He tells us: don't judge the machine on what it is at time T, but on what it has the potential to become.
A Still-Living Debate
This intellectual duel is more relevant than ever. Every assertion about AI creativity today is an echo of this foundational conversation. For designers, this debate is not just a historical curiosity: it directly questions the nature of their expertise and its value in the face of tools that "create" automatically.
But what if this debate, however stimulating, rested on a misunderstanding of what creativity itself is? What if, to break the deadlock, we needed to stop looking at the machine and finally dare to look at our own mind? That is precisely what we will explore in article AI and the Myth of Creativity 2/4: Alan Turing’s Response.
Article by Matthieu Ferry, clinical psychologist, published in French in Intelligences Plurielles on 20 July 2025, adapted in English for Copryce Lab in December 2025.
Ada Lovelace, Note G, 1843.
Alan Turing, Computing Machinery and Intelligence, 1950.
Copryce Perspective
Beyond the Debate, the Question of Value
When a designer wonders how to price their expertise in the face of clients who might be tempted by “free” AI tools, they confront exactly the same question: what is the nature of my creative contribution?
Turing’s argument about surprise reminds us that a designer’s value doesn’t reside solely in the predictable execution of a brief. It resides in their ability to reveal unexpected solutions, to make complexity and beauty emerge from apparently simple constraints. It's this capacity for emergence — seeing what the client didn't see, proposing what they hadn't imagined — that justifies remuneration beyond simple time spent. Copryce helps designers structure this value in their quotes, by clearly distinguishing technical execution from the strategic and creative dimension.
Turing’s argument about the myth of pure originality may seem destabilizing: if we only recombine what we’ve learned, where is our added value? The answer lies in the quality and relevance of this recombination. An experienced designer doesn’t merely apply recipes: they mobilize a corpus of references, technical constraints, understanding of user needs, visual culture, and market knowledge. This capacity for situated and relevant synthesis is what differentiates the human from the automatic generator. The market data and pricing benchmarks offered by Copryce allow designers to value precisely this accumulated expertise.
Turing's vision of a “child-machine” that learns reminds us that expertise is built over time. This temporal dimension of expertise is at the heart of design's economic model. The progressive accumulation of experience, use cases, and adaptability shapes a designer's value. Copryce helps designers translate this skill development into coherent pricing evolution.
The question is perhaps not “can AI create?” but rather “what makes creation valuable?” This second question, more concrete, is also the one that allows designers to defend their fair compensation, not on principle but through clarity about what they truly bring to the table.
