I run CI Web Group and JustStartAI.io, so I live at the intersection of "automation as a machine" and "automation as a decision system" for real-world ops like HVAC/plumbing scheduling, lead triage, and after-hours customer support. What I've learned is that today's AI feels new, but the winning pattern is old: sense - decide - act, then repeat fast. A clean 20th-century example is Shakey the Robot (late 1960s/early 1970s at SRI). Shakey wasn't impressive because it moved well--it was impressive because it combined perception (cameras/sensors) with symbolic planning to choose actions in a messy environment, which is the conceptual ancestor of modern AI agents and "automation with judgment." That's the same architecture I now apply with AI chatbots for home service companies: capture intent (what the homeowner needs), qualify it (urgency, budget, service type), then execute (book, route, or escalate). In practice, it's how you go from a "contact form" to smart scheduling that accounts for technician availability and route efficiency, and 24/7 lead capture that doesn't drop opportunities after hours. The Reddit takeaway: early robotics proved the hard part wasn't the arm or wheels--it was the feedback loop that turns uncertain inputs into reliable next steps. If your automation can't sense and adapt, it's just a fancy script; if it can, it becomes a scalable ops advantage.
Early robotics experiments shaped today's AI powered automation by forcing engineers to confront the gap between mechanical movement and adaptive decision making. In the early twentieth century, many machines could repeat motions with precision, but they could not sense, adjust, or respond to variation. That limitation became obvious in manufacturing environments. The push to make machines not just move, but respond, laid the groundwork for sensor integration, feedback loops, and eventually machine intelligence. One defining 20th century example is the installation of the first industrial robot, Unimate, at a General Motors plant in 1961. Unimate was developed by George Devol and commercialized with Joseph Engelberger. It was programmed to perform repetitive die casting and welding tasks that were dangerous for human workers. While Unimate did not use AI in the modern sense, it introduced programmable automation into real production lines. The significance was not just mechanical. It demonstrated that tasks could be abstracted into programmable instructions. That abstraction is central to AI driven automation today. Modern robotic systems now combine machine vision, reinforcement learning, and real time data analysis to adapt to changing conditions. But the conceptual leap began when engineers realized machines could be reprogrammed for different tasks rather than built for a single fixed motion. Early robotics also encouraged the integration of sensors and control theory. Feedback mechanisms became essential for precision. That same feedback logic underpins today's AI systems, where models continuously adjust based on data input. In short, early robotics shifted automation from static machinery to programmable systems. That transition created the structural and conceptual foundation on which AI powered automation now operates.
Early attempts to develop robots emphasized logical decision-making instead of mechanical repetition: the real precursor to AI-driven business workflows today. While industrial robotic arms demonstrated that we could automate physically repetitive tasks, robot-building experiments like Shakey the Robot at SRI International in the 1960s confirmed that we could also automate reasoning and reasoning about one's environment. As a generally mobile robot capable of detecting and reasoning about its environment through its vision sensor, Shakey needed to break down complex commands into multiple simple, concrete, and individual processes in order to perform all of its required operational functions. The process that Shakey used to decompose complex commands is precisely the same as how most business process-driven/ERP-like systems leverage AI-assisted exception management and predictive routing (route/predict where your item will be at all times via an algorithm). Although the A search algorithm, used for Shakey's navigation, primarily focused on moving Shakey from one point to another, it also provided Shakey with the ability to identify the most efficient path through all defined constraints. Similarly, the same logic is employed by the AI Agent when determining the best procurement path or predicting where a supply chain bottleneck will likely occur. The key takeaway from the technological advancements in the business world for the last 100 years is that once we start automating "if-then" scenarios autonomously, the ability for an organization to scale its business through automation will become significantly easier. Robotic automation experiments from the 20th century tell us that hardware is less important than the decision-making model - AI-Powered Automation leverages an equivalent level of autonomous reasoning as human beings; therefore, when we are designing AI-Powered Automation systems, we want both speed and the same level of autonomous reasoning that allows the system to self-correct itself when real-world outcomes deviate from expected delivery dates. Although transitioning from a traditional rigidly automated business process to an intelligent business system is often more of a cultural obstacle than a technical one, business executives who recognize the value of AI as the tool to delegate decision-making (not just tasks) will be the most successful at overcoming the barriers between legacy and modern efficiency.
I've spent 20+ years building automated systems--most recently GermPass, which uses UVC chambers that automatically sanitize door handles and touchpoints within seconds of every touch. What I've learned is that early robotics taught us one critical lesson: remove human inconsistency from repetitive tasks. The Stanford Cart from the 1960s-70s is a perfect example most people don't mention. It was one of the first robots to steer obstacles using cameras and computer vision--crude as hell, taking 10-15 minutes to move one meter. But that "sense environment, make decision, execute action" loop is exactly what powers our GermPass sensors today. When someone touches a door handle, our system detects it, seals the chamber, fires UVC light, and reopens--all without a person needed. We literally started tinkering in our garage in 2019, not as engineers but as problem-solvers. That Stanford Cart mentality of "automate the decision-making, not just the movement" is why GermPass can kill 99.999% of pathogens automatically after every single touch. Before automation, hospitals relied on manual cleaning schedules with gaps of hours or days between wipes--our tech closes that deadly window to 5 seconds. The manufacturing floor taught us precision through repetition, but those early vision-based robots taught us *smart* automation. That's the difference between a system that just moves and one that actually prevents the 54,000 daily deaths from infectious disease that the CDC reports.
As President of Alliance InfoSystems, I lead a team that manages complex, interconnected networks where we use "sense-and-respond" logic to ensure 24/7 security and uptime. My background in technical leadership gives me a front-row seat to how early logical systems evolved into the automated, cloud-based IT management we provide today. A defining 20th-century instance is **Shakey the Robot**, developed by SRI International in the late 1960s. Shakey was the first mobile machine to integrate perception with logical reasoning, a breakthrough that directly mirrors how we now use smart software to monitor network functions--similar to how a modern car monitors its own engine temperature or fuel consumption. Shakey's ability to break down complex tasks into steps shaped the productivity automation we implement for clients, like automating data entry so a single address change ripples through billing and appointment systems. This "work smarter" approach reduces the 10-minute delays of manual maintenance and eliminates the productivity drains that stifle growing businesses. As we move toward a world of 50 billion connected IoT devices, the focus has shifted from Shakey's basic navigation to securing the entire "inter-connectivity web." We now use that foundational automation to build disaster-resistant cloud solutions that allow businesses to scale quickly while maintaining a safe, encrypted environment for their data.
When building digital toolchains for modern-day use, I look back to Shakey the Robot at SRI International from 1966 to 1972. Shakey was the first autonomous robot to reason about its actions—it can be seen as the "granddaddy" of autonomous navigation systems we have today. My work with cloud-based automated systems employs the same A* search algorithm and logical "if/then" programming that were exemplified by Shakey. Shakey demonstrated that an autonomous robot must be built on a solid digital architecture capable of interpreting and existing within a constantly changing physical environment to be truly useful. We can observe Shakey's influence on AI today in the technical debt management of autonomous systems. By 2026, sensors used for real-time processing are merely the newest evolution of Shakey's original visual and bump sensors. This instance in the 20th century taught us that technical agility is not just speed; it's also the machine's ability to interpret and respond to its environment. The code that powers every self-driving delivery bot today originated with the early experiments conducted at Stanford and SRI.
Being Partner at spectup, I often think about how early robotics experiments were less about building useful machines and more about learning how intelligence could be translated into mechanical behavior. One of the most influential 20th century examples was Shakey the Robot, developed at the Stanford Research Institute during the late 1960s and early 1970s. Shakey was slow, fragile, and honestly not very practical for real world deployment, but its value was conceptual rather than operational. What made Shakey important was its integration of perception, reasoning, and movement planning inside a single system. Before that, robotics research treated movement and intelligence as separate engineering problems. Shakey introduced early forms of environment mapping and goal based navigation, which later became foundational ideas in autonomous systems. When I look at modern AI automation, I see echoes of that architecture inside today's workflow intelligence platforms. The experiment showed that machines needed internal representations of space before they could act reliably. That idea directly influenced modern path planning algorithms used in autonomous logistics, manufacturing robots, and even some digital automation systems. I remember reading early project notes where researchers struggled with uncertainty in sensor feedback, something still relevant in AI deployment today. Another lasting contribution was the shift from hard coded movement rules to search based problem solving. Instead of programming every possible action, researchers allowed the machine to explore solution paths. That philosophy now lives inside reinforcement learning and adaptive automation frameworks used in modern industry. Shakey also taught engineers that robotics progress would be slow and iterative rather than revolutionary in a single leap. The machine moved cautiously, sometimes stopping for long computation cycles, which surprisingly resembles how some AI decision systems operate today when balancing risk and optimization. The biggest legacy of early robotics experiments is not hardware design but cognitive architecture thinking. Modern. Looking back, 20th century robotics research shaped today's AI economy by proving that intelligence is a process, not just a product. The experiments were imperfect, but they established the philosophical and technical foundation for modern autonomous technology.
I can trace almost all of today's AI success back to one machine: Unimate. Unimate was the first industrial robot ever used at General Motors. It didn't have "brains" like modern AI, but it was a giant hydraulic arm that did one thing. It was used to move hot pieces of metal. It slashed factory injury rates by 90%. It proved that machines should handle the "dangerous and dull" tasks, a principle that still drives automation today. Unimate used hard codes for repetition. Today, we've replaced those rigid commands with AI "neural networks." However, the goal remains the same, which is mastering the chaos of a busy floor. Early struggles with Unimate's precision forced engineers to develop "feedback loops." This evolved into the AI Vision Systems that we use in 2026 to help robots "see" and adapt to their surroundings. In my work with legacy clients, we still use Unimate's original "task sequencing" logic. The Unimate robot started our moving, but the AI of today is the thing that is teaching us how the thinking works.
Early robotics experiments proved that machines could repeat precise tasks with consistency. One clear example is the first industrial robot, Unimate, installed at General Motors in 1961. It handled dangerous die casting tasks and reduced workplace injuries while improving output. That breakthrough showed businesses that automation could boost safety and efficiency at scale. At PuroClean, I see the same principle when we use moisture mapping tools that collect data faster than manual checks. The core idea never changed, remove risk and increase accuracy. Early robotics laid the groundwork for today's AI systems that now learn and adapt instead of just repeat.
As founder of Yacht Logic Pro, I've built AI tools automating marine ops, tracing roots to 20th-century robotics that replaced manual drudgery with precise sequencing. One instance: Unimate, George Devol's 1961 industrial robot at GM, autonomously transferred 200-pound die castings hourly, cutting human error in hazardous tasks. It shaped AI automation by validating programmable repeatability, evolving into predictive systems like ours that trigger maintenance from engine-hour data. Yacht Logic Pro's AI now auto-generates jobs and tracks parts in real-time, saving marine shops hours weekly--mirroring Unimate's factory gains for dockside efficiency.
Early robotics projects provided the foundation for AI-driven automation by proving the capability of machines to execute repetitive tasks with accuracy and learn from structured data. A classic example of robotics at work is the Unimate robot installed at General Motors in 1961, which automated the welding of car parts. This project proved the capability of mechanical systems to execute pre-programmed tasks with accuracy, which later helped develop AI algorithms that enable machines to not only follow instructions but also optimize tasks based on real-time data.
As Founder and CEO of Connectbase, I've scaled AI-driven platforms automating fragmented telecom supply chains, directly inspired by early automation breakthroughs. A key 20th-century instance was George Devol's Unimate robot, patented in 1959 and deployed at GM in 1961--the world's first programmable industrial arm. It autonomously handled 500 hot die castings per hour with programmed precision, replacing manual labor and pioneering sequenced task automation. This foundation powers today's AI in connectivity: our platform automates quote-to-cash across 300+ providers, slashing order fallout 35% via API-orchestrated workflows that open up $2B+ in annual transactions.
In 1961, General Motors installed Unimate, the first industrial robot, on its production line. It handled die casting tasks that were dangerous and physically demanding for workers. What made it significant was not just the machine itself, but the idea that tasks could be executed through programmed instructions instead of human motion. That shift laid groundwork for today's AI automation. Modern systems still follow the same premise. Define rules, execute consistently, and reduce manual intervention. The difference now is that software adapts based on data, but the operational thinking traces back to those early robotics experiments.
In the past few decades, advances in robotics have changed machines from rigid, pre-programmed devices into systems that can perceive, reason, and operate autonomously. This shift serves as a foundation for many of today's AI-driven forms of automation, such as autonomous vehicles, warehouse robots, and smart industrial systems. One major example of this development occurred in the 20th century. Shakey the Robot was the first mobile robot that could think about what to do before acting, marking an early step toward robots that can think independently. Shakey was developed by SRI International and created many innovations over its six years of operation. These advancements included: The creation of the STRIPS planner for logical task execution. The implementation of the A* algorithm for navigation. Early Development of Computer Vision. Early Development of Natural Language Processing. The Long Lasting Effects of Shakey on the Future of Automation. Inspired the development of autonomous navigation systems. Fostered the development of AI-powered mining and robotic systems. Provided the basis for predictive maintenance through smart decision makers. Shakey was a fundamental part of the birth of intelligent automation in our society because it could think before acting.
Through my role as a founder and chief technology officer, I've spent over 15 years creating AI automation startups, and I have seen how the tinkering of yesterday's inventors has created today's intelligent factories. In the 1940s there was a "rat brain" machine - known as the Stochastic Neural Analog Reinforcement Calculator (SNARC) developed by Marvin Minsky and Dean Edmonds at Harvard in 1951 - that learned using trial-and-error. The challenge was that robotics were not adaptable in their early stage. Rigid machinery could not develop without an AI feedback mechanism, which kept manufacturing from scaling their automation systems. Three ways to progress: Mimic nature: SNARC used 40 vacuum tubes connected to "neurons" who received rewards for adjusting their circuits. Today's deep neural networks are built on this foundation. Scale feedback: The Shakey robot was developed in the 1960s and integrated sensing with planning, enabling the robot to move autonomously. Build with AI from the start: According to Forbes, current AI robots can reduce manufacturing errors by 30% because they were designed with AI capability from the outset. The results will be 40% increase in production line speed and over $1.5 trillion in savings worldwide by 2030. Manufacturing will be transformed by AI automation - game on!