I believed I’d supply a number of takeaways and reflections primarily based on final week’s first AI Codecon digital convention, Coding with AI: The Finish of Software program Improvement as We Know It. I’m additionally going to incorporate a number of brief video excerpts from the occasion. Should you registered for Coding with AI or in the event you’re an present O’Reilly subscriber, you possibly can watch or rewatch the entire thing on the O’Reilly studying platform. Should you aren’t a subscriber but, it’s simple to begin a free trial. We’ll even be posting further excerpts on the O’Reilly YouTube channel within the subsequent few weeks.
However on to the promised takeaways.
First off, Harper Reed is a mad genius who made everybody’s head explode. (Camille Fournier apparently has joked that Harper has rotted his mind with AI, and Harper really agreed.) Harper mentioned his design course of in a chat that you simply may wish to run at half pace. His greenfield workflow is to start out with an thought. Give your thought to a chat mannequin and have it ask you questions with sure/no solutions. Have it extract all of the concepts. That turns into your spec or PRD. Use the spec as enter to a reasoning mannequin and have it generate a plan; then feed that plan into a unique reasoning mannequin and have it generate prompts for code era for each the applying and checks. He’s having a wild time.
Agile Manifesto coauthor Kent Beck was additionally on Workforce Enthusiasm. He instructed us that augmented coding with AI was “probably the most enjoyable I’ve ever had,” and stated that it “reawakened the enjoyment of programming.” Nikola Balic agreed: “As Kent stated, it simply introduced the enjoyment of writing code, the enjoyment of programming, it introduced it again. So I’m now producing extra code than ever. I’ve, like, one million strains of code within the final month. I’m enjoying with stuff that I by no means performed with earlier than. And I’m simply spending an obscene quantity of tokens.” However sooner or later, “I feel that we received’t write code anymore. We are going to nurture it. It is a imaginative and prescient. I’m certain that a lot of you’ll disagree however let’s look years sooner or later and the way the whole lot will change. I feel that we’re extra going towards intention-driven programming.”
Others, like Chelsea Troy, Chip Huyen, swyx, Birgitta Böckeler, and Gergely Orosz weren’t so certain. Don’t get me incorrect. They assume that there’s a ton of fantastic stuff to do and be taught. However there’s additionally plenty of hype and unfastened considering. And whereas there might be plenty of change, plenty of present expertise will stay necessary.
Right here’s Chelsea’s critique of the current paper that claimed a 26% productiveness enhance for builders utilizing generative AI.
If Chelsea will do a sermon each week within the Church of Don’t Imagine All the pieces You Learn that consists of her exhibiting off varied papers and giving her dry and insightful perspective on how to consider them extra clearly, I’m so there.
I used to be a bit stunned by how skeptical Chip Huyen and swyx had been about A2A. They actually schooled me on the notion that the way forward for brokers is in direct AI-to-AI interactions. I’ve been of the opinion that having an AI agent work the user-facing interface of a distant web site is a throwback to display screen scraping—absolutely a transitional stage—and whereas calling an API might be one of the best ways to deal with a deterministic course of like fee, there might be a complete lot of different actions, like style matching, that are perfect for LLM to LLM. Once I take into consideration AI searching for instance, I think about an agent that has realized and remembered my tastes and preferences and particular targets speaking with an agent that is aware of and understands the stock of a service provider. However swyx and Chip weren’t shopping for it, a minimum of not now. They assume that’s a great distance off, given the present state of AI engineering. I used to be glad to have them convey me again to earth.
(For what it’s price, Gabriela de Queiroz, director of AI at Microsoft, agrees. On her episode of O’Reilly’s Generative AI within the Actual World podcast, she stated, “Should you assume we’re near AGI, attempt constructing an agent, and also you’ll see how far we’re from AGI.”)
Angie Jones, then again, was fairly enthusiastic about brokers in her lightning speak about how MCP is bringing the “mashup” period again to life. I used to be struck particularly by Angie’s feedback about MCP as a type of common adapter, which abstracts away the underlying particulars of APIs, instruments, and information sources. That was a strong echo of Microsoft’s platform dominance within the Home windows period, which in some ways started with the Win32 API, which abstracted away all of the underlying {hardware} such that software writers not needed to write drivers for disk drives, printers, screens, or communications ports. I’d name {that a} energy transfer by Anthropic, aside from the blessing that they launched MCP as an open normal. Good for them!
Birgitta Böckeler talked frankly about how LLMs helped scale back cognitive load and helped assume by way of a design. However a lot of our every day work is a poor match for AI: giant legacy codebases the place we modify extra code than we create, antiquated expertise stacks, poor suggestions loops. We nonetheless want code that’s easy and modular—that’s simpler for LLMs to grasp, in addition to people. We nonetheless want good suggestions loops that present us whether or not code is working (echoing Harper). We nonetheless want logical, analytical, essential excited about drawback fixing. On the finish, she summarized each poles of the convention, saying we want cultures that reward each experimentation and skepticism.
Gergely Orosz weighed in on the continued significance of software program engineering. He talked briefly about books he was studying, beginning with Chip Huyen’s AI Engineering, however maybe the extra necessary level got here a bit later: He held up a number of software program engineering classics, together with The Legendary Man-Month and Code Full. These books are many years outdated, Gergely famous, however even with 50 years of instrument growth, the issues they describe are nonetheless with us. AI isn’t prone to change that.
On this regard, I used to be struck by Camille Fournier’s assertion that managers like to see their senior builders utilizing AI instruments, as a result of they’ve the abilities and judgment to get probably the most out of it, however usually wish to take it away from junior builders who can use it too uncritically. Addy Osmani expressed the priority that fundamental expertise (“muscle reminiscence”) would degrade, each for junior and senior software program builders. (Juniors could by no means develop these expertise within the first place.) Addy’s remark was echoed by many others. No matter the way forward for computing holds, we nonetheless have to know easy methods to analyze an issue, how to consider information and information constructions, easy methods to design, and easy methods to debug.
In that very same dialogue, Maxi Ferreira and Avi Flombaum introduced up the critique that LLMs will have a tendency to decide on the commonest languages and frameworks when attempting to unravel an issue, even when there are higher instruments obtainable. It is a variation of the statement that LLMs by default have a tendency to provide a consensus answer. However the dialogue highlighted for me that this represents a danger to talent acquisition and studying of up-and-coming builders too. It additionally made me marvel about the way forward for programming languages. Why develop new languages if there’s by no means going to be sufficient coaching information for LLMs to make use of them?
Virtually all the audio system talked concerning the significance of up-front design when programming with AI. Harper Reed stated that this feels like a return to waterfall, besides that the cycle is so quick. Clay Shirky as soon as noticed that waterfall growth “quantities to a pledge by all events to not be taught something whereas doing the precise work,” and that failure to be taught whereas doing has hampered numerous tasks. But when AI codegen is waterfall with a quick studying cycle, that’s a really completely different mannequin. So this is a crucial thread to drag on.
Lili Jiang’s closing emphasis that evals are far more complicated with LLMs actually resonated for me, and was according to most of the audio system’ takes about how a lot additional we now have to go. Lili in contrast a knowledge science venture she had executed at Quora, the place they began with a fastidiously curated dataset (which made eval comparatively simple), with attempting to take care of self-driving algorithms at Waymo, the place you don’t begin out with “floor fact” and the precise reply is very context dependent. She requested, “How do you consider an LLM given such a excessive diploma of freedom by way of its output?” and identified that the code to do evals correctly will be as giant or bigger than the code used to form the precise performance.
This completely suits with my sense of why anybody imagining a programmer-free future is out of contact. AI makes some issues that was once arduous trivially simple and a few issues that was once simple a lot, a lot tougher. Even in the event you had an LLM as decide doing the evals, there’s an terrible lot to be discovered.
I wish to end with Kent Beck’s considerate perspective on how completely different mindsets are wanted at completely different levels within the evolution of a brand new market.
Lastly, an enormous THANK YOU to everybody who gave their time to be a part of our first AI Codecon occasion. Addy Osmani, you had been the right cohost. You’re educated, an amazing interviewer, charming, and plenty of enjoyable to work with. Gergely Orosz, Kent Beck, Camille Fournier, Avi Flombaum, Maxi Ferreira, Harper Reed, Jay Parikh, Birgitta Böckeler, Angie Jones, Craig McLuckie, Patty O’Callaghan, Chip Huyen, swyx Wang, Andrew Stellman, Iyanuoluwa Ajao, Nikola Balic, Brett Smith, Chelsea Troy, Lili Jiang—you all rocked. Thanks a lot for sharing your experience. Melissa Duffield, Julie Baron, Lisa LaRew, Keith Thompson, Yasmina Greco, Derek Hakim, Sasha Divitkina, and everybody else at O’Reilly who helped convey AI Codecon to life, thanks for all of the work you set in to make the occasion successful. And due to the virtually 9,000 attendees who gave your time, your consideration, and your provocative questions within the chat.
Subscribe to our YouTube channel to observe highlights from the occasion or turn into an O’Reilly member to observe all the convention earlier than the subsequent one September 9. We’d love to listen to what landed for you—tell us within the feedback.
