Guy Reams (00:01.186)
This is day 60, why Turing's vision feels so modern. I'm in the process of launching a company named after Alan Turing. Since this week marks the first production ready application we have brought to market, I thought I would use my writing time to reflect on the inspiration that Alan Turing provided us. There are moments in history when someone sees the world not only as it is, but as it could become. Alan Turing was one of those rare individuals.
He lived in a time when computers filled rooms, consumed enormous amounts of power, and performed only narrow calculations. Yet he somehow saw beyond the machinery of his era. He looked into a future shaped by learning systems, intelligent behavior, and machines that could collaborate with human beings to solve complex problems. When we read his writing today, it does not feel old. It does not feel speculative or distant. It feels startlingly familiar.
His vision belongs to the world we are stepping into right now. It belongs to the area of large language models, AI co-pilots, agentic workflows, and the increasingly presence of intelligent systems in everyday work. Turing understood something deeper than algorithms. He understood the long arc of discovery. He understood the discomfort that precedes transformation. He understood the patience required to build something that grows from weakness to strength.
These insights feel remarkably aligned with the world we now inhabit. His clarity speaks to us. The beautiful simplicity of the child machine. One of Turing's most profound insights was his belief that a truly intelligent system should not begin as a fully realized adult. It should begin as a child. It should experience the world, make mistakes, form patterns, and grow through repeated correction. There's a quiet wisdom in that idea.
We often look for shortcuts. We hope to skip the early stages of development and leap directly into mastery. Yet every meaningful accomplishment begins with a child phase. Habits begin this way. Growth begins this way. The formation of a new idea begins this way. Even our convictions begin in fragile and uncertain forms and strengthen only through repeated practice. Turing believed the same would be true for machines. He imagined a system with the capacity to learn.
Guy Reams (02:26.93)
not a system with every rule preloaded. That insight became the foundation of modern AI. Today's language models, vision systems, and robotics platforms are not programmed to know everything. They are trained, shaped, and fine-tuned. They grow by exposure to data and by interacting with the world. Touring grasped us long before learning systems were even possible. The humility embedded into this concept of a child machine continues to guide the field.
He anticipated the human experience of uncertainty. Turing understood that a world with intelligent machines would not feel simple or smooth. He knew that uncertainty would come first. People would question whether a machine was truly thinking. They would question its motives and its limits. They would compare it to themselves. They would feel both fascination and fear. We see this unfolding everywhere today. Teams adopt AI tools with excitement but also hesitation.
Leaders feel pressured to innovate, but also a sense of responsibility to proceed carefully. People interact with systems that speak fluently, reason convincingly, and analyze data at tremendous scale, yet they wonder how much to trust a place in these capabilities. Turing first saw this long before anyone could experience it. He understood that intelligence is not just a technical achievement. It is an emotional event for the societies that encounter it. It defines what it means to contribute.
It reshapes how we think about effort, creativity, and value. His vision accounts for the human side of progress, which is often the most difficult side. He recognized the power of scale. Turing believed that once machines became faster and had more memory, they would begin to exhibit unexpected forms of intelligence. He understood that scaling up computation could lead to new behavior that was not present in smaller systems. That insight sits at the center of modern breakthroughs.
The large language models that dominate today were not created by inventing a single clever trick. They emerged from scale. More data, more training, more refinement. Eventually a tipping point appeared and the systems began to show reasoning, creativity, and adaptability. Turing recognized this possibility long before computational scale even existed. He understood that intelligence is often an emergent property. He understood that new capabilities often reveal themselves only after significant growth.
Guy Reams (04:56.411)
This idea feels incredibly modern because we are living through its very proof. He saw that humans would need time to adapt. Perhaps the most forward-looking aspect of Turing's thinking was his understanding that society would need time to adjust. We would not simply adopt intelligent machines in a single sweep. We would integrate them slowly. We would learn how to work alongside them.
we would gradually discover which tasks we should automate and which tasks we should keep for ourselves. This patient view of adoption mirrors our current reality. The challenges facing AI today do not revolve around technology, they revolve around people. They revolve around culture, trust, policy, workflow design, and the readiness of teams to embrace a new way of working. We are learning how to ask better questions. We are learning how to evaluate output.
We are learning how to build processes that take advantage of intelligence without abandoning human judgment. This is a slow, disciplined journey. Exactly the type of journey and touring would have imagined. His thinking aligns closely with our present challenge. The frontier is not capability. The frontier is adoption and diffusion. He imagined machines that could improve themselves.
Turing believed it was possible for machines to modify their own instructions and participate in their own development. At the time, this idea seemed more philosophical than practical. Today, it feels familiar. AI systems help write code. They optimize model. They explore new architectures. They analyze their own errors. We're beginning to see the earliest signs of systems that can strengthen themselves. Turing recognized this trajectory long before such work was even feasible.
Why Turing still feels like a guide. Turing's writing resonates today because he understood the human condition and the presence of new possibility. He saw that intelligence, whether biological or mechanical, grows through struggle and refinement. He saw that people wrestle with uncertainty before they embrace transformation. He saw that breakthroughs begin small, often in forms that look unimpressive at first. He saw that progress requires patience and hope.
Guy Reams (07:11.928)
His vision feels modern because it's not anchored in the technology of his era. It is anchored in the patterns of growth that shape every meaningful journey. The way individuals grow, the way organizations grow, the way ideas grow. When we read Turing now, we encounter someone who understood the future not as a prediction, but as a process. He understood that great change arrives slowly, then suddenly, and only after moments of doubt.
He understood the unfolding of human courage in the presence of the unknown. We are living inside the story that he sketched. We are watching the world move through the same questions that he posed. We are discovering the same truths he pointed toward. His vision feels modern because we are finally walking in the world that he had imagined.