Somehow this article doesn't even mention the fact that AI makes software rewrites much, much faster than before and with higher confidence of backwards compatibility.
Nowadays, a good AI harness can fairly reliably rewrite a medium complexity piece of software to an appropriate modern tech stack with pretty strong confidence of exactly preserving its behavior. The AI can pick up legacy details and keep them exactly the same as before in ways that a human rewriter would usually not bother with. After rewriting each feature it can then exhaustively smoke test all the happy paths and edge cases and ensure the code behaves exactly the same as before, which is another thing that human rewrites basically never do.
Joel is right, but he's also wrong. I've been on the other side of a timid engineering culture that commerical rides roughshod over and its this depressing immeasurable decline. The company stagnates and slowly tailspins around an unmaintainable product until a competitor steals their lunch in a way that that further obscures cause and effect.
Estimates are considerably longer, QA is much harder, integration is full of buckets and rakes, some "senior" devs are afraid to touch stale core code, innovation is stifled, devs are frustrated, hiring is harder, attrition bites. The most frustrating thing is that its very hard to communicate the issues as everyone experiences a fragment of the pain and none of it lines up in a spreadsheet for anyone to appreciate the whole cost. Everything just sucks.
LLMs changing the economy of this sounds great, especially if removes the essential issue with the ground up rewrite, which is the "ground up" part.
This certainly does. If we think from this angle, it really begs the question of what language/tech stack to use if a company wants to start a new project. On one hand, if company uses a very well tech stack, development and rewrites will be faster due to AI having way more examples to draw from. In certain cases, AI will handle some edge cases which are difficult to come by/replicate under strictest test procedures. Overall, that results in faster workflow. On the other hand, if this company choose a newer stack which may be better better than older popular frameworks, development time will increase (along with rewrite time)but the product might be better. we have to see how companies handle this in the future, given this is also affected by how cheap/expensive token consumption becomes. Using something pretrained vs training and then using an AI has cost implications when done in a large scale. It will be interesting to see what directions companies go to, faster workflows and delivery using AI or potentially a better product using more manually written proprietary code with lesser AI involvement.
This kind of data-free opining reminds me of the Mythical Man-Month. Yeah, in theory adding more people to a project will speed it up. And all people are replaceable so I can hire 100 bodies for cheap and we'll be done with this project ASAP.
Sounds great! Have you tried this? Did you see what went wrong? Otherwise this is just the same nonsense as always.
The amount of armchair quarterback commentary in the software business as concerns people waxing eloquent a out difficult things safe atop a perch of the same easy things achieved multiple times has always been obnoxious, offensive to the thermodynamics of the situation as situated by Landauer.
But this new "you're holding it wrong" series by people whose grasp of the system gets fuzzy somewhere in the v8 headers is a new land speed record for being vacuously correct and still an attractive nuisance for profit.
Yes, the trend towards encoding hard-won domain knowledge as property and fuzz testing and sometimes even proof system was underway before ChatGPT, and yes, the economics of this approach bend sharply under a post terrawright world.
But no, you haven't added anything except tinsel and chaff and some green css on mixpanel.
Just stop with this shit. If you knew shit about AI you'd be too busy printing cash to teach the rest of us about it.
Somehow this article doesn't even mention the fact that AI makes software rewrites much, much faster than before and with higher confidence of backwards compatibility.
Nowadays, a good AI harness can fairly reliably rewrite a medium complexity piece of software to an appropriate modern tech stack with pretty strong confidence of exactly preserving its behavior. The AI can pick up legacy details and keep them exactly the same as before in ways that a human rewriter would usually not bother with. After rewriting each feature it can then exhaustively smoke test all the happy paths and edge cases and ensure the code behaves exactly the same as before, which is another thing that human rewrites basically never do.
Does it really change the whys of rewriting?
https://www.joelonsoftware.com/2000/04/06/things-you-should-...
Maybe the LLM will catch and reproduce all corner cases... maybe not...
Joel is right, but he's also wrong. I've been on the other side of a timid engineering culture that commerical rides roughshod over and its this depressing immeasurable decline. The company stagnates and slowly tailspins around an unmaintainable product until a competitor steals their lunch in a way that that further obscures cause and effect.
Estimates are considerably longer, QA is much harder, integration is full of buckets and rakes, some "senior" devs are afraid to touch stale core code, innovation is stifled, devs are frustrated, hiring is harder, attrition bites. The most frustrating thing is that its very hard to communicate the issues as everyone experiences a fragment of the pain and none of it lines up in a spreadsheet for anyone to appreciate the whole cost. Everything just sucks.
LLMs changing the economy of this sounds great, especially if removes the essential issue with the ground up rewrite, which is the "ground up" part.
again with these linkedin "articles".
every sentence stands on its own because it's the most insightful soundbite of wisdom every constructed.
Aphorisms for the collective upgrade of consciousness.
delivered one tweet at a time.
This certainly does. If we think from this angle, it really begs the question of what language/tech stack to use if a company wants to start a new project. On one hand, if company uses a very well tech stack, development and rewrites will be faster due to AI having way more examples to draw from. In certain cases, AI will handle some edge cases which are difficult to come by/replicate under strictest test procedures. Overall, that results in faster workflow. On the other hand, if this company choose a newer stack which may be better better than older popular frameworks, development time will increase (along with rewrite time)but the product might be better. we have to see how companies handle this in the future, given this is also affected by how cheap/expensive token consumption becomes. Using something pretrained vs training and then using an AI has cost implications when done in a large scale. It will be interesting to see what directions companies go to, faster workflows and delivery using AI or potentially a better product using more manually written proprietary code with lesser AI involvement.
The problem is always maintainability. Who's gonna fix new bugs? Who's gonna add new features?
It also changes the economics of buy vs build.
Much less if you consider buy vs build+maintain.
This kind of data-free opining reminds me of the Mythical Man-Month. Yeah, in theory adding more people to a project will speed it up. And all people are replaceable so I can hire 100 bodies for cheap and we'll be done with this project ASAP.
Sounds great! Have you tried this? Did you see what went wrong? Otherwise this is just the same nonsense as always.
The amount of armchair quarterback commentary in the software business as concerns people waxing eloquent a out difficult things safe atop a perch of the same easy things achieved multiple times has always been obnoxious, offensive to the thermodynamics of the situation as situated by Landauer.
But this new "you're holding it wrong" series by people whose grasp of the system gets fuzzy somewhere in the v8 headers is a new land speed record for being vacuously correct and still an attractive nuisance for profit.
Yes, the trend towards encoding hard-won domain knowledge as property and fuzz testing and sometimes even proof system was underway before ChatGPT, and yes, the economics of this approach bend sharply under a post terrawright world.
But no, you haven't added anything except tinsel and chaff and some green css on mixpanel.
Just stop with this shit. If you knew shit about AI you'd be too busy printing cash to teach the rest of us about it.