When we talk about AI now, I’m not even sure we’re supposed to call it AI anymore. Depending on who is talking, we may already have moved on to SI, for superintelligence.

I digress.

Before I offer my thoughts on AI security incidents, whether these systems will help find new cures for cancer and other diseases, or whether we are all headed toward some extinction-level event, I think we need to rewind. The current conversation often makes it sound as if artificial intelligence suddenly appeared a few years ago. It did not.

The term artificial intelligence dates to a 1955 proposal for the Dartmouth Summer Research Project on Artificial Intelligence, led by John McCarthy. The workshop took place the following summer. That makes the field about seventy years old, depending on where you choose to start counting.

It was not new when I entered the technology business either. We talked about AI, machine learning, neural networks, and expert systems. The tools were narrower, the hardware was less capable, and the interfaces were not built for everyone, but the ideas were already there.

I first started calling some of my own work robots in 2001. When I told people I built robots, I would usually get the same kind of response: “Like R2-D2?”

We have always attached a body to the word robot. We picture wheels, arms, blinking lights, or something that walks into the room. I used the word more broadly. To me, a robot was a computer program that performed a task on my behalf or helped me complete one.

The robots I was building worked in the ticket business. We built systems that helped purchase and price tickets. We built systems that facilitated orders across multiple parties, handed work to credit card processors and shipping providers, and kept transactions moving when no single company owned the whole process.

Those systems did not look like R2-D2, but they were doing work.

As the years went on, the techniques changed with the problems in front of us. Around 2007, we built technology around shipping and logistics, including systems that gathered shipping behavior and submitted refund requests to small-parcel carriers. As that business became more data-driven, we found uses for genetic algorithms, evolutionary approaches, Bayesian networks, decision trees, random forests, and neural networks.

In another company started in 2011, we worked with social media data. We used natural language processing and natural language understanding to make sense of what people were saying in posts. We assigned sentiment scores and confidence measures. We used keyword matching and image object identification. None of it was perfect, but all of it belonged under the broad AI umbrella.

By 2015, robotic process automation had become a major part of our work in ticketing. The retail side of the industry had consolidated, competition had changed, and the business needed to become more efficient on the back end. By 2019, micro-pricing and dynamic pricing created new demand for pricing intelligence and forecasting. If you could not respond to changing market conditions, you risked missing the profit you expected. Those pressures are still part of that industry today.

I go through that history for a reason. I had been building robots and applying AI techniques for years before large language models entered the public conversation. AI was real. It was useful. It was also mostly hidden inside specific products, research environments, and professional systems.

Then ChatGPT launched publicly on November 30, 2022.

That changed everything, but not because intelligence had suddenly been invented.

I think the closest comparison is Google Search. Google was not the first search engine. Databases were not new. The web was not new. What Google did was put an enormous amount of complexity behind a remarkably simple experience. There was a logo, a text box, and a couple of buttons. You did not need to understand indexing, ranking, or distributed systems. You typed what you wanted, and something useful came back.

ChatGPT did something similar for AI. It took a collection of ideas and capabilities that had largely been available to researchers, technical teams, and specialized businesses and put them behind a text box. You did not need to know how to train a model. You did not need to understand neural-network architecture. You did not need to write code. You could type a question in ordinary language and get a response.

It felt like magic because the complexity disappeared.

The speed of adoption has been remarkable. By July 2025, OpenAI reported more than 700 million weekly active ChatGPT users on its consumer plans, nearly ten percent of the world’s adult population. But even that kind of growth is not without precedent when we look beyond AI.

Television went from an invention and an experiment to a fixture in American homes. By 1970, 95 percent of U.S. homes had one. Household computer ownership was only 8 percent in 1984, then climbed dramatically over the next few decades. In 1998, more than a third of U.S. households already owned a cell phone, years before the iPhone changed what a phone meant.

Once a technology becomes useful, affordable, and easy enough to reach, adoption can move very quickly. The large-language-model revolution is unprecedented inside the history of AI. It is much less surprising inside the history of technology.

You might even call it predictable.

The pattern tends to repeat. A technology begins with researchers and a few professional use cases. It develops alongside the infrastructure that already exists around it. Commercial products appear. A few companies gain momentum. Consolidation follows. The technology finds an early use case that is not necessarily the final one, but it is useful enough that people start paying for it. Eventually, it becomes hard to imagine life without it.

That does not mean today’s winners will be tomorrow’s winners. The early internet was full of companies that once seemed unavoidable. Some disappeared. Some were acquired. Some helped teach the next generation what to build. Pets.com and AltaVista still mean something to people who lived through that era, but neither became the final shape of the internet.

The AI story is not finished either.

In fact, I think we are entering the harder part: product design.

The original ChatGPT experience was powerful because it was so simple. Since then, the major AI companies have been racing to capture more of our work and more of our attention. OpenAI, Microsoft, Anthropic, Cursor, xAI, Google, Meta, and others keep adding models, modes, tools, agents, applications, and new ways to use them. We have moved from websites to command lines, mobile apps, desktop apps, and now back toward cloud systems as personal agents and long-running work take hold.

Every individual step may make sense. Taken together, the story is getting harder for an average person to follow.

What am I supposed to use? Where does it run? What can it see? What can it change? Which model should I choose? Is this a chatbot, an assistant, an agent, or an entire operating environment? When should I trust it, and when does a human need to step in?

The companies building these products often appear to be chasing one another. One launches a feature and the others race to produce their version. AI makes code easier to create, but producing more code is not the same thing as having a clear product strategy. Catching up on a feature list does not guarantee that people will understand what the product is for.

That is one of the questions I keep thinking about as we build CoffeeBreak.

We released Launch Check in September as a focused first product. It is meant to help small teams and vibe coders reduce some of the friction between building a working application and putting it in front of real users. It gives them a second look at what they built and surfaces things they may not have known to ask about or that a model working with limited context may have missed.

Today, those findings can be reviewed in a useful interface and shared as a report with tools such as Claude, Codex, or Lovable. The next step in our product direction is to connect those findings to Mission Control so the work does not stop at a document. The mission can continue, the issues can be addressed, and the path from idea to production can become more coherent.

Over time, our goal is to let people run their own missions with a useful set of tools and integrations, then work with different Personas that have clear roles, guardrails, audit trails, and the right level of intelligence for the task. Those experiences should be able to meet people where they already work, including surfaces such as Slack and Teams, mobile and messaging, vehicles, and eventually robotics.

That is the bet, anyway.

I do not think the future of AI is one giant chatbot that is allowed to do everything. I think it is a set of understandable products that coordinate models, tools, memory, deterministic logic, and human judgment. The intelligence may be impressive, but the product still has to make sense. It has to be clear about what it is doing, what it is allowed to do, and when it needs help.

The first AI revolution put remarkable capability behind a text box. The next one will have to turn that capability into systems people can understand, trust, and use in the middle of real life and real work.

Before we decide whether AI will cure everything or kill us all, it is worth remembering how we arrived here. The technology did not appear in 2022. Access did. The interface changed. The audience changed. Then the rest of the world started building on top of it. What comes next is still being written.