Our AI Codecon convention kicked off as we speak with Coding with AI: The Finish of Software program Improvement as We Know It. Listed below are my opening remarks introducing the sequence’ themes. You possibly can reserve your seat for upcoming AI Codecon occasions right here.
Thanks a lot for becoming a member of us as we speak. We’ve got over 20,000 individuals 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 tip of programming however its exceptional growth. That is probably the most thrilling second in software program improvement that I’ve seen throughout my greater than 40 years on this business.
I organized this occasion as a result of I’ve grown more and more pissed off with a persistent narrative: that AI will substitute programmers. I’ve heard variations of this identical prediction with each technological leap ahead—and it’s at all times been fallacious. Not simply barely fallacious, 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 repeatedly constructed higher translation layers between human thought and machine execution—from bodily wiring to meeting language to high-level languages to the World Broad Internet, which embedded calls to backend techniques 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 type of punctuated equilibrium, during which some breakthrough resets the business, 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
Take into account how dramatically programming has developed over the a long time. It was actually exhausting 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 via 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 Broad Internet, the interface to computer systems turned human-readable paperwork that had a few 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 other occurred. The sector expanded, creating new specialties and bringing extra individuals into the dialog.
Take the digital spreadsheet—a revolutionary device that modified enterprise ceaselessly. Dan Bricklin and Bob Frankston first prototyped VisiCalc in BASIC, the Nineteen Seventies equal of as we speak’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 solution 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 information stays important for manufacturing.
Twenty years later, Tim Berners-Lee created the World Broad 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 straightforward HTML code. Lots of the individuals who created billion-dollar companies on the early net started as newbie programmers. A lot of them informed me that they discovered what they wanted to know from an O’Reilly ebook.
AI-Assisted Programming Right this moment: Democratization on Steroids
That very same sample is repeating now—however at unprecedented scale and velocity.
Lately, a tech govt informed me about his high-school-age daughter’s summer season 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 formidable problem. The professor identified that pulse oximeters don’t work very effectively; the one solution to get an excellent blood oxygen studying is with a blood draw. He stated, “I’ve an concept that it is perhaps potential to get an excellent studying out of the capillaries within the retina. Why don’t you look into that?” So she did. She fed ChatGPT a number of pictures of retinas, obtained it to isolate the capillaries, after which requested the way it may detect oxygen saturation. That concerned some coding. Fairly gnarly picture recognition that usually would have taken lots of programming expertise to write down. However by the tip of the summer season, she had a working program that was capable of do the job.
Now it’s simple to attract the conclusion from a narrative like this that that is the tip {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 could have meant taking it critically sufficient to write 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 attempting new issues has gone down by orders of magnitude. And that implies 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 tip of this undertaking? Is that this prototype the completed product? After all not. Turning it into one thing strong, dependable, and medically legitimate would require skilled software program engineers who perceive techniques design, testing methodologies, regulatory necessities, and deployment at scale.
Proper now, we’re seeing lots of people reengineering outdated concepts to do them higher with AI. The subsequent 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 techniques.
This mirrors the evolution of the online. What started as easy static HTML pages developed into advanced, interconnected techniques with frameworks, APIs, and cloud infrastructure—what I known as in 2005 “software program above the extent of a single system.” The online didn’t remove 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 only the start—like HTML was to the online. The true magic occurs in how these fashions are built-in, refined, and deployed as parts in bigger techniques.
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 current dialog in a personal chat group, Eran Sandler used a automotive metaphor: “The mannequin is the engine, however you want a complete 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 various kinds of computer systems: one that may write poetry however struggles with primary arithmetic, one other that calculates flawlessly however lacks creativity. The artwork of recent improvement is orchestrating these techniques 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 rigorously designed “metacognitive recipes”—code that manages and directs AI inference. Doing this effectively can remodel a job 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 at first of its most profound reinvention but.
A Renaissance of Innovation
It’s a rare time to be in software program improvement. After years of incremental advances that made the sector 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 write down code sooner—although that’s useful. It’s about reimagining what software program can do, who can create it, and the way we method issues that beforehand appeared intractable.
This convention will discover three important dimensions of this new panorama:
- The right way to successfully collaborate with AI to reinforce your present improvement workflow
- The rising patterns and antipatterns of constructing dependable, production-grade AI techniques
- The increasing alternative panorama as beforehand infeasible tasks change into potential
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 think about: What downside that appeared inconceivable yesterday may you now be capable of remedy?
Let’s embrace this second not with worry however with the joy of explorers discovering new territory.