Our AI Codecon convention kicked off right this moment with Coding with AI: The Finish of Software program Growth as We Know It. Listed here are my opening remarks introducing the sequence’ themes. You may reserve your seat for upcoming AI Codecon occasions right here.
Thanks a lot for becoming a member of us right this moment. We have now over 20,000 folks signed up for this occasion, each subscribers on the O’Reilly studying platform and those that aren’t but subscribers. I believe you’re right here since you all sense what I do: We’re witnessing not the top of programming however its outstanding enlargement. That is probably the most thrilling second in software program improvement that I’ve seen throughout my greater than 40 years on this trade.
I organized this occasion as a result of I’ve grown more and more pissed off with a persistent narrative: that AI will change programmers. I’ve heard variations of this identical prediction with each technological leap ahead—and it’s at all times been improper. Not simply barely improper, however basically misunderstanding how expertise evolves.
Programming, at its essence, is dialog with computer systems. It’s how we translate human intention into machine motion. All through computing historical past, we’ve constantly constructed higher translation layers between human thought and machine execution—from bodily wiring to meeting language to high-level languages to the World Large Internet, which embedded calls to backend methods right into a frontend made up of human-readable paperwork. LLMs are merely the following evolution on this dialog, making entry to laptop energy extra pure and accessible than ever earlier than.
And right here’s what historical past constantly reveals us: At any time when the barrier to speaking with computer systems lowers, we don’t find yourself with fewer programmers—we uncover totally new territories for computation to remodel.
There’s a form of punctuated equilibrium, during which some breakthrough resets the trade, there’s a interval of livid innovation adopted by market consolidation, and admittedly, a little bit of stasis, till some new expertise upsets the apple cart and units off one other interval of reinvention.
The Historic Sample of Enlargement
Think about how dramatically programming has developed over the a long time. It was once actually onerous to inform computer systems what we needed them to do. The earliest programmers needed to bodily join circuits to execute totally different operations. Then got here the von Neumann saved program structure. That permit programmers present binary directions by way of entrance panel switches. That was adopted by meeting language, then compilers that took high-level, more-human-like descriptions and routinely translated them into the machine code that matched the structure of the underlying system. With the World Large Internet, the interface to computer systems turned human-readable paperwork that had a number of the traits of a program. Hyperlinks didn’t simply summon new pages however ran different applications. Every step made the human-machine dialog extra pure.
With every evolution, skeptics predicted the obsolescence of “actual programming.” Actual programmers debugged with an oscilloscope. But the alternative occurred. The sphere expanded, creating new specialties and bringing extra folks into the dialog.
Take the digital spreadsheet—a revolutionary device that modified enterprise perpetually. Dan Bricklin and Bob Frankston first prototyped VisiCalc in BASIC, the Nineteen Seventies equal of right this moment’s “vibe coding.” To create a viable product, they then rewrote it in meeting language for the 6502 microprocessor, the CPU for the Apple II. They needed to do it this method to optimize efficiency and match this system inside the Apple II’s reminiscence constraints. This sample is instructive: Simplified instruments allow speedy prototyping and experimentation, whereas deeper technical data stays important for manufacturing.
Twenty years later, Tim Berners-Lee created the World Large Internet prototype on a NeXT machine—one other leap ahead in programming accessibility. So many people discovered to construct our first net web page just by flattening a menu, clicking “View Supply,” and modifying the easy HTML code. Most of the individuals who created billion-dollar companies on the early net started as novice programmers. Lots of them advised me that they discovered what they wanted to know from an O’Reilly guide.
AI-Assisted Programming At the moment: Democratization on Steroids
That very same sample is repeating now—however at unprecedented scale and pace.
Lately, a tech government advised me about his high-school-age daughter’s summer time internship with a Stanford biomedical professor. Regardless of having no programming background—her pursuits have been in biology and drugs—she was tasked with an bold problem. The professor identified that pulse oximeters don’t work very effectively; the one method to get a superb blood oxygen studying is with a blood draw. He stated, “I’ve an concept that it could be attainable to get a superb studying out of the capillaries within the retina. Why don’t you look into that?” So she did. She fed ChatGPT a lot of photographs of retinas, bought it to isolate the capillaries, after which requested the way it would possibly detect oxygen saturation. That concerned some coding. Fairly gnarly picture recognition that usually would have taken lots of programming expertise to jot down. However by the top of the summer time, she had a working program that was in a position to do the job.
Now it’s simple to attract the conclusion from a narrative like this that that is the top {of professional} programming, that AI can do all of it. For me, the lesson is the exact opposite. Pre-AI, investigating an thought like this might have meant taking it severely sufficient to jot down a grant software, rent a researcher and a programmer, and provides it a go. Now, it’s tossed off to a highschool intern! What that shouts to me is that the price of making an attempt new issues has gone down by orders of magnitude. And that signifies that the addressable floor space of programming has gone up by orders of magnitude. There’s a lot extra to do and discover.
And do you suppose that that experiment is the top of this undertaking? Is that this prototype the completed product? In fact not. Turning it into one thing strong, dependable, and medically legitimate would require skilled software program engineers who perceive methods design, testing methodologies, regulatory necessities, and deployment at scale.
Proper now, we’re seeing lots of people reengineering previous concepts to do them higher with AI. The following stage goes to be tackling totally new issues, issues that we couldn’t have—or wouldn’t have bothered to strive—with out AI.
The New Spectrum: From Vibe Coding to AI Engineering
What’s rising is a brand new spectrum of software program creation. At one finish is “vibe coding”—speedy, intuitive programming assisted by AI. On the different finish is systematic AI engineering—the disciplined integration of fashions into strong methods.
This mirrors the evolution of the net. What started as easy static HTML pages developed into complicated, interconnected methods with frameworks, APIs, and cloud infrastructure—what I known as in 2005 “software program above the extent of a single system.” The net didn’t get rid of programming jobs; it created totally new classes of improvement work. Frontend engineering, backend engineering, DevOps, data safety. Extra JavaScript frameworks than anybody can hold observe of!
We’re seeing that very same sample with LLMs and brokers. The uncooked mannequin is just the start—like HTML was to the net. The actual magic occurs in how these fashions are built-in, refined, and deployed as elements in bigger methods.
The New Hybrid Computing Paradigm
A device like ChatGPT, Perplexity, or Cursor highlights simply how rather more there’s to an AI software than the mannequin. The bare mannequin is wearing fashions dreamed up by entrepreneurs, formed by product managers, and pieced collectively by AI engineers. Any AI app (together with only a chatbot) is definitely a hybrid of AI and conventional software program engineering.
In a latest dialog in a personal chat group, Eran Sandler used a automotive metaphor: “The mannequin is the engine, however you want an entire lot round it to make it a sports activities automotive—context administration, codified workflows, and extra. These are the ‘actual makes use of’ of AI fashions.”
This jogged my memory of Phillip Carter’s perception that we’re now programming with two basically several types of computer systems: one that may write poetry however struggles with fundamental arithmetic, one other that calculates flawlessly however lacks creativity. The artwork of contemporary improvement is orchestrating these methods to enrich one another.
Sam Schillace added one other dimension: “There’s now a pressure between dependable and versatile—code is dependable however inflexible, inference is versatile however unreliable.” He described how the brand new job of the programmer is to craft fastidiously designed “metacognitive recipes”—code that manages and directs AI inference. Doing this effectively can rework a process from 5%–10% dependable to almost 100% in particular domains.
These conversations reveal the long run panorama. We’re not on the finish of programming—we’re initially of its most profound reinvention but.
A Renaissance of Innovation
It’s a unprecedented time to be in software program improvement. After years of incremental advances that made the sphere really feel considerably predictable, we’re getting into a interval of radical innovation. The basic constructing blocks of how we create software program are altering.
This isn’t nearly utilizing AI instruments to jot down code quicker—although that’s beneficial. It’s about reimagining what software program can do, who can create it, and the way we strategy issues that beforehand appeared intractable.
This convention will discover three crucial dimensions of this new panorama:
- How you can successfully collaborate with AI to reinforce your present improvement workflow
- The rising patterns and antipatterns of constructing dependable, production-grade AI methods
- The increasing alternative panorama as beforehand infeasible initiatives grow to be attainable
The programming world was frankly getting a bit predictable for some time. The enjoyable is again—together with unprecedented alternative. All through this occasion, I hope you’ll not simply take up data however actively contemplate: What downside that appeared not possible yesterday would possibly you now be capable of remedy?
Let’s embrace this second not with worry however with the joy of explorers discovering new territory.
