The End of Programming

At the bottom of the article is the blurb about the author:<p>&gt; Matt Welsh (mdw@mdw.la) is the CEO and co-founder of Fixie.ai, a recently founded startup developing AI capabilities to support software development teams. He was previously a professor of computer science at Harvard University, a director of engineering at Google, an engineering lead at Apple, and the SVP of Engineering at OctoML. He received his Ph.D. from UC Berkeley back in the days when AI was still not playing chess very well.

I’ve always felt that the actual code writing is the least interesting part of coding. It find it fun, but feel that the real engineering is largely the stuff that comes before (and some after) the code is written.<p>I’m not yet sold on the idea that it will replace coding. It’s like autonomous cars. 99% is far far too low. But it’s also not to be dismissed. A brilliant tool with many use cases.<p>I think we have invented the Enterprise “ship’s computer.” It won’t answer “how do I solve this problem?” But it probably will answer things like, “if X and Y and Z, what might W be?” We get to be the big brains standing around a terminal! I can’t wait to have a holodeck where I can construct a world just by asking for things.

The author seems to forget that while an AI can create text that looks like code, it can never prove the code is working correctly (see halting problem).<p>As with many things, AI will excel at the simple tasks that just require pattern matching and generation to work. Nothing more but also nothing less.<p>People get carried away by how great AIs are at spitting out complex sentences from a huge training set that look like a human would have written them. Winter v2 is coming.<p>Besides, there&#x27;s no training set for the specific domain problem I&#x27;m currently solving. It is - by definition - not generic. The problem domain is not bubble sort or fizzbuzz.

If anything it&#x27;s rather the start of programming in my opinion, or rather the start of a new era.<p>We build endless higher level abstractions ontop of each other in programming, this is just another one.<p>I&#x27;m not bullish on AI actually understanding something in the near future and it&#x27;ll rather continue to be something more akin to mimickery, albeit amazingly expressive and accurate.<p>I think this is rather going to become an amazing tool to help reduce repeating already solved problems. But humans would still be needed to plumb it together and adjust it to meet some final need.<p>If the AI can do the whole thing then whatever you&#x27;re trying to create probably already exists and there&#x27;s unlikely to be a need for it in my opinion.

Can someone that understands ML better than I tell me if there is a point where the AI can indefinitely train on data generated by other AI? If AI is trained on human development work product and then it eliminates human developers, will the capabilities of the AI be stuck indefinitely at the level of the software from which the models were trained? Not sure if I&#x27;m making sense, but the crux of my question is: can AI effectively generate their own training data sets? If not, then I don&#x27;t see how it could replace an industry.

&gt; Fast-forward to today, and I am willing to bet good money that 99% of people who are writing software have almost no clue how a CPU actually works, let alone the physics underlying transistor design.<p>I dont fully agree with this. A lot of folks in systems land have mechanical sympathy and deeply think about memory, IO and processors. Things are mostly built upon underlying abstractions. With AI becoming mainstream, some of the abstractions will be pushed down and some might evolve further.

Nah, these tools will empower programming. Almost no one writes assembly any more. We&#x27;ve got high-powered tools that already abstract away a <i>ton</i> of software engineering complexity so people can do what they want to do quickly and inexpensively. But I have a very hard time believing that &quot;programming&quot; — the act of writing out precise textual instructions in a file for a computer to read and execute — is going anywhere. It&#x27;s a very elegant and powerful means of interacting with a machine. Similar to how the written word remains one of the most powerful mechanisms for interpersonal communication despite the massive and powerful media tools at our disposal.

As it stands I think this will entirely won&#x27;t work. We can&#x27;t still to this day define what we want a program to do. So much in successful software development happens in the journey of making it. The constant shaping and add features and product and engineering finding users do unexpected things so we then try and capitalize that.

&gt;Yet I think it is time to accept that this is a very likely future, and evolve our thinking accordingly, rather than just sit here waiting for the meteor to hit.<p>You know, I would love for all business app development to die in a wretched fire of scum and villainy. Not that it’s bad, but it’s probably some of the most mundane work that programmers could do. The people giving out busy work or bullshit jobs won’t be able to affect actual people. They’ll just have AI do it, which is great!

Plain English is a terrible way to express ourselves clearly and unambiguously. That&#x27;s why programming languages exist.

New technology taking my job is the least of my concerns here. In my opinion there is simply a bifurcation of software underway: traditional programming on one side, and training&#x2F;learning-based technologies on the other.<p>Traditional software will continue to be chosen when we want predictable, unbiased, mechanical execution of instructions. There are many areas where this is preferred, and I don&#x27;t see that changing. Mechanical and later silicon calculation devices are invaluable for their speed, but the greatest benefit is that they are predictable and consistent: they do not make errors unless the design is in error.<p>AI, machine learning, and other training&#x2F;learning-based technologies also have many useful and tantalizing applications. For applications such as those that enhance productivity, provide entertainment (e.g. art and music), or autonomously perform tasks where mistakes can be tolerated, these training&#x2F;learning-based technologies will reap great things.<p>However, for many applications we don&#x27;t want a complex device, whose behavior, while it can be ostensibly tested, cannot be completely understood and examined to be provably correct. Or, whose faulty action cannot be definitively reproduced and root-caused after a mishap. Or, whose &#x27;black-box&#x27; can be infected or influenced by bad actors in a manner that is undetectable.<p>I don&#x27;t ever want to see a radiation dosing machine that is clever, an industrial control process that is expected to be trained to infer its own decisions where injury or life is at stake, nor do I wish to argue with a machine to open my pod bay door.<p>Alternatively, perhaps legal precedent will just establish the degree to which machines are allowed to make mistakes, and if they make fewer than a human, we will just accept the cost&#x2F;benefit of injury, loss of life, or evil as &#x27;practical&#x27;, and move on. &#x27;Actuary Shrugged&#x27;?<p>The most ominous prospect is if humanity fails to evolve past war and conflict faster than this technology&#x27;s destructive capability. Maybe Fermi will get his answer.

FWIW, I&#x27;m not convinced that this piece will be right for more than a few years into the future. I think it&#x27;s an interesting discussion piece, though.<p><i>Context</i> matters a ton and a lot of programming is understanding context and requirements and goals and needs and economics and those, while trainable, will suffer from the slowness and lack of richness of the I&#x2F;O between the real world and the model (this interface is not improving nearly as fast as the models themselves).

This article makes me think of two other articles which discuss the matter. One by Bartosz Milewski[1] and one by Eli Bendersky[2]. The main idea of both is that programming can change significantly from what we do today but never will be obsolete. It just will be on an upper step on the meta ladder.<p>[1] <a href="https:&#x2F;&#x2F;bartoszmilewski.com&#x2F;2020&#x2F;02&#x2F;24&#x2F;math-is-your-insurance-policy&#x2F;" rel="nofollow">https:&#x2F;&#x2F;bartoszmilewski.com&#x2F;2020&#x2F;02&#x2F;24&#x2F;math-is-your-insuranc...</a><p>[2] <a href="https:&#x2F;&#x2F;eli.thegreenplace.net&#x2F;2022&#x2F;asimov-programming-and-the-meta-ladder&#x2F;" rel="nofollow">https:&#x2F;&#x2F;eli.thegreenplace.net&#x2F;2022&#x2F;asimov-programming-and-th...</a>

When I started programming as an intern 7 years ago, I asked simple questions around the office and they told me to just google it. My approach was to understand every single bit that goes on, but it seemed like nobody truly knew what they were doing.<p>Fast forward to today, the new programmers I meet don&#x27;t truly understand what the code does. If a problem pops up, the first action is googling it for hours. Very few people have this high level structured thinking ability to filter out the noise to distill a problem.<p>I believe Ai will accelerate this, where programmers will know even less about what goes on in the program and struggle more when stuff doesn&#x27;t go as expected. Over the years, google became less useful as all the SEO spam took over. I noticed how people couldn&#x27;t come up with solutions on their own, as a google search yielded no results.<p>Now I read these articles every other month about another tool, framework or article announcing the end of programming as we know it. Nothing ever happened and truly experienced developers became even more valuable.<p>If we cross this fine line between aiding and replacing developers, we set ourselves and the next generation up for a bad time in my opinion...

<a href="https:&#x2F;&#x2F;youtu.be&#x2F;-aUcvswVJ58?t=2962" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;-aUcvswVJ58?t=2962</a><p>&quot;We are no longer particularly in the business of writing software to perform specific tasks. We now teach the software how to learn, and in the primary bonding process it molds itself around the task to be performed. The feedback loop never really ends, so a tenth year polysentience can be a priceless jewel or a psychotic wreck, but it is the primary bonding process--the childhood, if you will--that has the most far-reaching repercussions.&quot;<p><pre><code> Bad&#x27;l Ron, Wakener, Morgan Polysoft Accompanies the Digital Sentience technology </code></pre> Bonus :<p><a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=-aUcvswVJ58&amp;t=3852s">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=-aUcvswVJ58&amp;t=3852s</a><p>&quot;&#x27;Abort, Retry, Fail?&#x27; was the phrase some wormdog scrawled next to the door of the Edit Universe project room. And when the new dataspinners started working, fabricating their worlds on the huge organic comp systems, we&#x27;d remind them: if you see this message, <i>always</i> choose &#x27;Retry.&#x27;&quot;<p><pre><code> Bad&#x27;l Ron, Wakener, Morgan Polysoft Accompanies the Matter Editation technology</code></pre>

It would be nice if the AI was used to build libraries for targeted purposes. That would actually be pretty handy.<p>And then it would also be handy for it to design a programming language for both readability and writability that leveraged those libraries.

I will wax a touch philosophical here, but I believe perfect systems do not exist unless they are purely within thought. Implementations will experience failure due to both its physical and logical components, logical in the sense due to unforeseen n-th degree effects. This is when expert knowledge is needed, unless you have already designed such a generalized model that captures, taxonomizes, reacts, and optimizes for all events until the end of time. From an educational standpoint, who cares if you know how to add a node to a binary tree using C++. It&#x27;s not the technical details but the struggle to understand recursion until it finally clicks. It&#x27;s the sculpting of the computational mind. Until the essence of controlling and directing computation by computational machines themselves is satisfactorily solved (for then you&#x27;ve become god), then no, expert humans will always be needed, just not in abundance.<p>Throwback to Keynes forecasting that we&#x27;d all be working 15 hour work weeks.

I highly doubt programming will ever &quot;end&quot;.<p>In the past &quot;programmers&quot; used to write raw assembly. Now the compilers do this, and programmers write source code for the compiler. In the future AI may write the source code, but we will still need to write higher-level specifications, which will likely be much more detailed than &quot;develop a mail client app&quot; or &quot;develop an FPS&quot;.<p>Even today, business managers who have programmers to do all of the actual coding, need to write detailed specifications (and when they write bad specifications, they get bad products); those specifications are in a sense, &quot;code&quot;. But even those specifications are not detailed enough: when refinement is fast and cheap (with AI doing the coding) you really want to be able to customize the UI, add various features, properly handle various edge cases, etc.

There&#x27;s always going to be a programming layer, at least until AIs can self-improve, and at that point it&#x27;s basically AGI. In other words, programmers will be needed until the &quot;singularity&quot; and then we have no idea what happens.

This paper coomparing code developed without copilot to code developed with it feels relevant: <a href="https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2211.03622" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2211.03622</a>

Unless we get a good brain-computer interface you won&#x27;t be communicating precisely and clearly to AI what program it needs to write. It would be like ordering a SmartPhone online and receiving PhoneSmart Teaching Telephone.

If we do go to more ai contributed code fragments, we still need to test them for correctness. Given that we already see compelling looking but bug ridden solutions from chatgpt as well as from humans, I still see the test as crucial. If an ai writes the test, I&#x27;ll skeptical. I&#x27;m skeptical of tests written by humans too.<p>Maybe the key is testing and coding can be seen as adversarial actions, and there is benefit in separating them. If the ai or code generator or whatever you want to call it writes the code and test, I&#x27;m even less likely to trust it.

My greatest fear that used to be programmers will become babysitters for the nocode people.

&gt; Programming will be obsolete. I believe the conventional idea of &quot;writing a program&quot; is headed for extinction, and indeed, for all but very specialized applications, most software, as we know it, will be replaced by AI systems that are trained rather than programmed. In situations where one needs a &quot;simple&quot; program (after all, not everything should require a model of hundreds of billions of parameters running on a cluster of GPUs), those programs will, themselves, be generated by an AI rather than coded by hand.<p>What is impossible to do then is something like convex optimization models for guidance of rockets. You need to be able to mathematically prove that the output of the program will always converge to the solution. You have to assume that the inputs to the algorithm could get scrambled by ionizing radiation on one cycle and that the next cycle it&#x27;ll recover completely. You want hard mathematical proofs. You don&#x27;t want a black box that has been trained a whole bunch and might have some sharp hidden edge condition that you&#x27;ll never know exists until just the right input hits it.

I can definitely see this being useful in some contexts i.e. where the problem can be easily defined. A good example is a bit of glue or plumbing code where one system with a defined interface has to talk to another one.<p>I also see it failing a lot. There were lots of promises made about 4GL languages that didn&#x27;t turn out to be true. They made for great demonstrations but as soon as you push past the boundaries a bit things get tough and you need an actual programmer to make things work.<p>The guys that came and demonstrated PowerBuilder[0] back in the nineties had an absolutely stellar demo, but the devs who were given PowerBuilder to work on at my workplace quickly got mired in details that brought them undone. I feel like the same thing will happen with anything non-trivial that AI generates. The re-work will be more effort than just building whatever it was from scratch.<p>[0]<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;PowerBuilder" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;PowerBuilder</a>

An AI that is intelligent enough to create a full program without supervision could rather just be asked to do the task instead.

After using CoPilot for about 8 months, I still find it useful. I still think it is worth $100&#x2F;year.<p>I have experimented a little with coding and ChatGPT and it seems impressive at first looks.<p>I had a funny social interaction while on a hike this morning with three friends who are all semi-retired videographers. They seemed enthusiastic enough to hear about automation for software development but when I brought up my continued joy at automatic photo&#x2F;video&#x2F;added-music mixups in Apple Photos (‘memories’) they didn’t like that. One friend categorically stated that AIs will never be able to adequately edit video, etc. in postproduction. I think he is wrong but I let it slide. I travel a lot and have a ton of digital media assets and I so very much appreciate the automatically created mixups. I watch every mixup and flag about half to keep forever.

I am currently building tools for programming and modifying programs with natural language using the OpenAI API and their relevant models (similar to ChatGPT).<p>Especially if you combine the text&#x2F;instruct model with the coding model and then give specific instructions, it is able to complete many simple coding tasks without me opening an editor.<p>Right now I am focused on something like Codepen but with English specifications only.<p>I believe that I should immediately start leveraging this type of tool in my other projects. Similar to the way you would use a calculator or Google Translate.<p>I believe over the next few years the models will continue to get better and also start to incorporate visual understanding and better reasoning. The point at which it I would consider it poor software engineering to write programs manually is rapidly approaching for many domains.

That article portrays a very bleak future. I can’t imagine programming will be generally useful if it needs a massive AI model that can only run on machines that cost an enormous amount of money, or cost you some cents for every word it generates using an API.

I use copilot pretty much religiously at this point and would be shocked if what the author is claiming happens in my professional lifetime. It just seems so far away. But I wouldn&#x27;t mind if it did happen, I am much better at my job because of AI.

Ai generated programs are not cool. You know what would be cool? Ai generated unit tests.

In the short term(2-3 years), I think, the hybrid(programmers give high level designs and instructions + AI models implements them) way of programming will become increaseingly popular. Nowaday&#x27;s LLMs have shown some suprisingly abilities of reasoning, remembering, and imitating, but, for now, they are still not good enough to write competent code alone, especially when it comes to complex fields like system design. Things like system design need a lot more sophisicated thinking process than implementing some specific functions.

Daniel Kahneman Thinking Fast And Slow describes some cognitive biases... these AI tools churn out text (ChatGPT) or code which look convincing so we think highly of them but in reality, there&#x27;s nothing. No, AI tools won&#x27;t take away our jobs any time soon, because we are paid to <i>think</i> even if the end result happens to be code, that&#x27;s not the point. Especially in debugging, AI is hopeless.<p>Consider this toot as well <a href="https:&#x2F;&#x2F;phpc.social&#x2F;@andrewfeeney&#x2F;109466122845775778" rel="nofollow">https:&#x2F;&#x2F;phpc.social&#x2F;@andrewfeeney&#x2F;109466122845775778</a>

I remember what my boss told me a few days after I started working, just out of school.<p>&quot;If you want a career you should move away from programming as soon as possible, those days are over, we&#x27;re already testing automatic programming systems, it&#x27;s a matter of months before we use them in production.&quot;<p>It was 23 years ago.<p>I think that the current state of AI will be sufficient to build excellent assistant tools, and I can see some productivity increase in some areas.<p>But for anything more advanced, let&#x27;s say I am not worried.

These computational advances in AI are good. However, I am still waiting for AI to &quot;synthesize&quot; knowledge that is as yet undiscovered through a combination of theorem proving, reasoning, AI search techniques and so on.<p>It is not just AI &quot;explainability&quot; but techniques for exploration and fact-finding. For eg: could an AI system prove or disprove P=NP.<p>It is a little premature to dismiss human endeavor while such large gaps in our epistemic knowledge exist.

So far I&#x27;m not impressed. If you ask these chatbots for something simple it usually gives you wikipedia&#x2F;stackoverflow levels of answers with lots of edge conditions because they&#x27;ve been trained on so much garbage. And if you try to ask it for something difficult then it doesn&#x27;t know how to produce it. And I&#x27;ve tried to make ChatGPT do physics and it keeps on failing on basic dimensional analysis.

Programming is one of the more complex and exacting mental activities, so if this is true, it is not only the end of programing.<p>AI progress is astounding and the results impressive, but they still seem to be &#x27;fuzzy&#x27;, almost like an instantiated dream generated by the input. Sure, as an assistant it, is powerful, but it still seems to be missing something to be able to provide a complete abstraction between people and code.

This will be the case eventually, but not for a while yet. There are still so many useful and interesting applications that can be built until then.

The moment he clarifies about his position on the AI market makes him another seller deeply trying to sell his product. It’s like those articles from 90’s and 00’s speculating the end of the C language with titles like “C is dead” or “C is going to be replaced in the near Future”. The year will be 2060 and C will still dominate the market.

I bet we will see lawyers losing their jobs first.

Writing code for what? Instead of writing code for e-commerce or whatever sites, why not just have AI tickle our pleasure centers directly and do just enough to keep us alive. Seems easier, and so much more efficient than writing code to make things and then ship those things out to us for a few hours of mediocre enjoyment.

Is AI doomed to always be a version behind the target framework&#x2F;language? If a framework API is updated or if a new hardware feature is launched, it will take a little time for the AI to catch-up right?<p>I imagine it’s not just about having API examples, you would want real codebases and use cases for training.

So let say that the AI will generate (not sure how) 100K app. Now there is req change (new feature), who will add it ? the AI?<p>I would extrapolate from copilot, since software does not scale linearly. 100LOC &lt;&lt; 1000LOC &lt;&lt; 100000 LOC.<p>The AI would probably stop at the range of 100-500 LOC.

If this prediction is true (I’m skeptical), it could be a boost for personal computing, assuming everyone could run (or rent) such a model, because it then would become easy to design your own software or user interface by merely explaining what you want to the AI.

When electronic calculators replaced slide rules it wasn&#x27;t the end of math.

People have a been trying to do this for CRUD apps for years. Now they want to use AI to do the same thing. Somehow, I am not convinced developing an AI to do this is as simple as it sounds.

Programming as we know it. I believe the future will be to write or design program specifications and the AI will write the code. We will be more like program architects.

I wish AI would just fix all bugs in all FOSS software, and implement all feature requests. I don&#x27;t know is that possible at all.

To quote the great Donald Knuth, when asked about AI code... &quot;but how do we know if it&#x27;s right?&quot;

Even the people who created AIs could be made redundant as well by their own creation.

Well, where is that future? Are people still going to be writing programs in 2023?

I&#x27;m not saying this vision won&#x27;t eventually happen, but I see some barriers here:<p>- We&#x27;ll want human engineers reviewing and testing the code generated by the AI.<p>- We&#x27;ll want human engineers building a better, higher quality training sets<p>- I suspect AI will run into the same issues that humans do when making changes to complex existing code bases.<p>- We&#x27;ll want better languages and libraries for the AI to program in<p>- This may all be well and good for standard types of applications, but I wonder how well it will work for novel types of computing<p>- None of this will solve the problem of needing to clarify our ideas (<a href="https:&#x2F;&#x2F;xkcd.com&#x2F;568&#x2F;" rel="nofollow">https:&#x2F;&#x2F;xkcd.com&#x2F;568&#x2F;</a>)

Nah. The author is living in a fantasy world.

Clickbait again