In short, the more deterministic, automated, checks the better. AI can deal with a pedantic language. I intend to add statically verified structured concurrency, units of measure, contracts, and eventually more and more formal methods into the language so it can be a familiar TYpeScript-like base with as many static guarantees as we can fit in.
I also think that fine-grained isolation, which Zena gets via Web Assembly, is critical for limiting the capabilities of generated code and the blast radius of bugs, vulnerabilities, and non-aligned behavior.
I do have an optimistic hope that a language also optimized for humans, readability and simple semantics especially, has value in the future, even when most code is generated. We'll see about that.
I love this. I was thinking about a "cleaned up" typescript for a while now, and this seems to be it. I believe this can work better as an "ai-first" language than some other attempts I've seen that try to reinvent the language from scratch.
One thing I would love to have as a feature is native compilation.
From experience with Zena, this is not true at all. Opus, Fable, Gemini Flash and Pro all barely make any syntax mistakes after a little is in context, and those are caught extremely early.
The one thing I do see sometimes is that agents sometimes don't take advantage of added features, but that's partially because the Zena code base doesn't use them as much yet. I'm working on skills and linter-based suggestions to use better patterns.
This is absolutely not the case in my experience. I am building a very large embedded domain specific language for describing distributed systems. It looks like a small subset of Elixir, but with object-oriented syntax in a lot of places. (It’s called a choreography; there exist many other choreographic programming languages.)
Even though this programming language is absolutely nowhere in any large language model’s training set, they have so far done extremely well at extrapolating from the small set of examples I’ve given it when I need an agent to generate some tests or whatever for me.
I think starting with a familar typescript-like base language is a good approach to this. This should be familiar enough for LLMs for the most part as long as additional features can be explained in a succinct system promopt/skill.
> bc agents will naturally be bad at it due to a lack of examples.
Can we please as a community stop parroting these false premises as a basis of every argument against doing anything new? It's plainly obvious to anybody that uses LLMs on a regular basis that it's not true.
I don’t know how stupid of a suggestion this is, but if no one is reading the code anymore (I do, but I hear many in much more elite shops than mine do not), then should we not just be using AI to write binary or machine code?
Machine code isn't especially expressive per line or unit of code. Lower level languages takes up more of an LLM's context than higher level ones.
To be effective in using low level languages, LLMs would have to build higher level constructs like subroutines from scratch every program.
It's not that different from why we almost never use assembler for anything more than code islands: even a modest subroutine can overwhelm our own mental context window.
Even when no(human)body is reading the code, AI is still reading the code in order to "understand" it. Languages that can express high level concepts, use structured programming for recognizable control flow patterns instead of inscrutable jumps, and assign names to things have the same benefits for the AI-coders and AI-reviewers that they always have for humans.
Personal responsibility will always exist at the touchpoints of software and human activity. Concentration and scope of responsibility may vary, and the degree to which that person needs to understand the code will vary as a result, but the need for a human to be able to read and understand code is going to be around for a very long while still.
LLMs are good at optimizing towards local goals. Getting types right at compile time is a local goal. Entry and exit assertions are local goals. Unit tests are local goals. So those constructs all help AI-generated code.
Matching a desired output is a global goal, but even that sometimes works now.
Someone sent me a LLM-generated JPEG 2000 decoder. They got Fable to generate a decoder that uses a GPU to get the same answer as the reference implementation gets on the GPU.
>This creates an interesting tension. Coding agents could dramatically reduce the cost of building an ecosystem while simultaneously weakening one of the forces that causes ecosystems to form in the first place.
This is deeply unintuitive but AI negates language specific ecosystems, while strengthening language agnostic ecosystems.
Pick whatever your favourite programming language is and its ecosystem. With AI someone can take your ecosystem and just port it to their language.
This means the only way you can protect your ecosystem is to play on all language fronts at the same time so porting the software to another language becomes a meaningless exercise.
> With AI someone can take your ecosystem and just port it to their language.
I don't think its that "just". Examples of porting we seen had some prerequisites: being self contained with very strong tests coverage, so AI could iterate N millions times and fix bugs in new implementation. Otherwise such porting could be very buggy and unmaintainable.
Don't all of "serious" programming languages meet that bar? Java, C#, Go, Python, etc all have enormous test suites. Once you get into the third party, things become much more uneven, but if you can restrict yourself to say the top N packages in a language, those are going to have better than average development practices which makes that plausible.
> AI negates language specific ecosystems ... Pick whatever your favourite programming language is and its ecosystem
I think this is only true when it comes to LLM raw output. There's also the concern of checking its work. A compiler that can check many aspects of correctness (static types, null) is a huge boost to AI. It can use the compiler directly to check its own work.
I’m not sure. my read of this was that AI weakens human ecosystems in general because we don’t need to work together as much when we are all just working with AI separately, but maybe you have specific examples of language agnostic ecosystems in mind? I’m struggling to imagine what that would look like
Why even assume that the most optimal programming languages for agentic coding are the ones that humans use? Maybe operate on ASTs directly? Some other form of programming that humans would find hard but that is a good fit for LLMs?
Yes, because that code was never written to be understood by machines, merely to be mechanically translated. Software is a very messy set of layers of leaky abstractions trying to express reasonably well defined ideas. Humans can't write code without mistakes, in spite of all the examples out there. If they could compilers wouldn't have to emit error messages.
LLM's are trained on human code though. Converting the code to its ast tree and training ai on that would be trivial of course, but I imagine there would be information that explains why something exists that would be missed.
I don't care if it's optimal for them. It's clearly good enough. We have, in hand, the ultimate in auditable AI output. We may not be able to audit how it got to the code it delivered, but it is really quite good at delivering code we can read. It would be very silly for us to give it up so that they can be somewhat more efficient or something, if they even would necessarily be that much more efficient.
To the point that I would support banning the creation of an AI-only language that can't be read by humans. Huge, huge, huge step in the wrong direction.
Implement whatever abstractions you think LLMs should work in terms of in whatever language is handy, and have your LLM use those abstractions.
---
- Correct by construction: the language makes invalid states or programs hard or impossible to express.
- Statically established: types, proofs, and static analysis establish properties before execution.
- Runtime-enforced: memory management, isolation, capability boundaries, and other runtime enforced properties.
- Empirically validated: program validation through tests, property-based testing, and fuzzing.
---
Along with being familiar, so it's easy to generate, is a huge part of why I'm building Zena: https://zena-lang.dev/
I don't have the AI-first rationale put into the public docs well just yet, but I mention some of it here: https://zena-lang.dev/guide/why-zena/#familiar-to-humans-and...
along with a doc in the repo on this topic: https://github.com/elematic/zena/blob/main/docs/design/ai-fi...
In short, the more deterministic, automated, checks the better. AI can deal with a pedantic language. I intend to add statically verified structured concurrency, units of measure, contracts, and eventually more and more formal methods into the language so it can be a familiar TYpeScript-like base with as many static guarantees as we can fit in.
I also think that fine-grained isolation, which Zena gets via Web Assembly, is critical for limiting the capabilities of generated code and the blast radius of bugs, vulnerabilities, and non-aligned behavior.
I do have an optimistic hope that a language also optimized for humans, readability and simple semantics especially, has value in the future, even when most code is generated. We'll see about that.
One thing I would love to have as a feature is native compilation.
One reason I haven't explored that is that I want to tailor the language for the more constrained environment of Wasm GC first.
Agents will naturally be bad at it due to a lack of examples.
The one thing I do see sometimes is that agents sometimes don't take advantage of added features, but that's partially because the Zena code base doesn't use them as much yet. I'm working on skills and linter-based suggestions to use better patterns.
Even though this programming language is absolutely nowhere in any large language model’s training set, they have so far done extremely well at extrapolating from the small set of examples I’ve given it when I need an agent to generate some tests or whatever for me.
"Dart-style constructors, Swift-style pattern matching and Strings, Trio-style async cancellation, Scala-style sealed classes"
The future is cooked
Can we please as a community stop parroting these false premises as a basis of every argument against doing anything new? It's plainly obvious to anybody that uses LLMs on a regular basis that it's not true.
To be effective in using low level languages, LLMs would have to build higher level constructs like subroutines from scratch every program.
It's not that different from why we almost never use assembler for anything more than code islands: even a modest subroutine can overwhelm our own mental context window.
Like I remember reading human studies that people make 1.5 - 5 errors per 100 loc.
If AI works in a similar way then we should stick to higher level languages that minimize loc
you can do a hell of a lot with a PERL one liner. What i'd recommend is extremely obvious languages like Golang
Matching a desired output is a global goal, but even that sometimes works now. Someone sent me a LLM-generated JPEG 2000 decoder. They got Fable to generate a decoder that uses a GPU to get the same answer as the reference implementation gets on the GPU.
I'm keen to run it.
This is deeply unintuitive but AI negates language specific ecosystems, while strengthening language agnostic ecosystems.
Pick whatever your favourite programming language is and its ecosystem. With AI someone can take your ecosystem and just port it to their language.
This means the only way you can protect your ecosystem is to play on all language fronts at the same time so porting the software to another language becomes a meaningless exercise.
I don't think its that "just". Examples of porting we seen had some prerequisites: being self contained with very strong tests coverage, so AI could iterate N millions times and fix bugs in new implementation. Otherwise such porting could be very buggy and unmaintainable.
year, that's usually what is referred as ecosystem.
I think this is only true when it comes to LLM raw output. There's also the concern of checking its work. A compiler that can check many aspects of correctness (static types, null) is a huge boost to AI. It can use the compiler directly to check its own work.
https://adk.dev/
https://docs.dagger.io/reference/sdks
both can invoke modules written in other languages from your language of choice
Kubernetes is likely an interesting ecosystem to consider under this lens too
To the point that I would support banning the creation of an AI-only language that can't be read by humans. Huge, huge, huge step in the wrong direction.
...
Naturally, it is probably inevitable.
But it's still a terrible idea.