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Julia is not a Lisp dialect, but Lispy Arnuld’s 2018 essay “Julia and the Reincarnation of Lisp” argues that it revives some Lisp-like ambitions: expressive metaprogramming, an interactive workflow, and a language designed to combine flexibility with performance. The “reincarnation” is a metaphor for shared ideas, not a claim of direct equivalence. The essay is most useful as a personal account of what one programmer wanted from a language—not as a benchmark or neutral comparison.
What the essay means by “the reincarnation of Lisp”
Lispy Arnuld published the essay on April 4, 2018, after years working with C and experimenting with Common Lisp, Scheme, OCaml, Haskell, Ruby, ATS, and other languages. Its central concern is language design: what tradeoffs make a language pleasant and capable for the work a programmer wants to do?
The author was looking for an open-source, dynamically typed language that reduced the memory-management burden he associated with C while still offering high performance and interactive development. Julia looked promising because, in the author’s view, it brought together qualities he associated with several languages: C-like speed, Ruby-like dynamism, Lisp-like macros and homoiconicity, mathematical notation associated with MATLAB, and broad usefulness for scientific and general programming.
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Why Julia seemed appealing to the author
The essay reprints a Julia design statement that captures the appeal: “We want a language that’s open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that’s homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab.” The statement describes a goal, not proof that Julia matches every quality equally well in every workload.
Performance and compilation
The author valued the prospect of compiling code and approaching C-like performance without giving up a high-level, dynamic style. The essay does not provide benchmarks, workload details, or controlled comparisons, so its claims about speed should be read as impressions rather than measured results.
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Interactive, dynamic programming
Coming from C, the author wanted less friction around memory management and a more exploratory workflow. Julia’s dynamism and interactive development appealed to that preference. The comparison is about the author’s experience and goals; it does not establish that Julia is easier than C—or easier than Lisp—for every programmer.
Macros and mathematical notation
The Lisp connection centers on macros and homoiconicity: the idea that code can be represented in a form a program can inspect and transform. The author saw Julia’s macro system as carrying forward a Lisp-associated style of metaprogramming while using more familiar mathematical notation. Similar aspirations do not make the languages’ syntax or macro models interchangeable.
Julia and Common Lisp: a useful comparison, not a scorecard
The essay motivates comparing Julia with Common Lisp, but it does not assess them systematically. A reader deciding between them should treat the following as questions to investigate for a particular project, not verdicts supplied by the essay.
| Question | What the essay establishes | What it does not establish |
|---|---|---|
| Execution and compilation | The author wanted high performance and saw Julia’s compilation as part of its appeal. | No benchmark, workload, or measured Julia-versus-Common-Lisp result is reported. |
| Dynamic typing and REPL workflow | Dynamic programming and interactive development were part of the author’s desired experience. | No comparative usability test or conclusion about which REPL workflow is better is offered. |
| Macros and metaprogramming | The author identifies Lisp-like macros and homoiconicity as part of Julia’s appeal. | The essay does not show that the macro systems are identical or interchangeable. |
| Scientific and mathematical work | The author was drawn to familiar mathematical notation and scientific-computing associations. | No neutral evaluation of libraries, package coverage, or domain-specific suitability is provided. |
| Syntax and learning | The author compared Julia’s appeal with syntax and dynamism associated with Ruby and MATLAB. | No evidence establishes that Julia is easier to learn than Common Lisp. |
| Ecosystem and adoption | The essay argues that Lisp did not become mainstream and hopes Julia may reach broader adoption. | It presents no adoption data, and its predictions should not be treated as current market facts. |
Is Julia a modern Lisp, and should you learn it instead?
Not in the literal sense. The essay makes a case for family resemblance in ideas and goals—especially macros, expressive programming, and interactive work—but Julia and Common Lisp remain distinct languages. Whether to learn Julia instead depends on what you want to build and which language’s syntax, tools, and ecosystem fit that work. The essay offers motivation for exploring Julia, not enough evidence to make that decision on a reader’s behalf.
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Arnuld’s broader point is that a programming language is shaped by its design choices and the reasons behind them. He writes, “A Programming Language is a lot about its design and whys.” That perspective explains why the article compares Julia with several languages rather than treating a feature checklist as the whole story.
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The essay is autobiographical and dates to 2018. Its judgments about Julia’s speed, friendliness, and prospects for becoming more popular are the author’s opinions, not controlled measurements or current evidence about adoption. Renato Athaydes revisited Common Lisp in a 2021 essay prompted by the title and quoted Arnuld’s observation that “The industry has a lot of code in Java even when it takes much less time to write code in Lisp.” That later response shows the essay’s ability to provoke discussion; it does not validate a general claim about which language is faster to develop in.
Best Value
For readers moving from Lisp, Python, or C, the essay can serve as a starting point for thinking about what they want from Julia. JuliaHub’s 2018 newsletter listed training, consulting, and Julia products as areas of contact, but that historical mention does not establish current availability or any referral arrangement.
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