Row 2024

Row ID: 2024 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 2024 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Predicting the programming language landscape in the next 5 years is nigh impossible as things evolve very fast and may get even faster. With the development of algorithms-who-code (based on LLM) we may get accelerating feedback loop effects. Ecosystems that have a lot of public code on which to train LLM's may benefit more from that dynamic and will become even more entrenched.

Having said that, there are few factors that may be resilient drivers:

* It will *probably* be the case that open source platforms (like Python, Julia, R) will be even more dominant but the allocation of mind share between them is unclear. Python is the current darling and is likely to coast on that popularity for a while. The driving domain is obviously Machine Learning and Deep Learning but it is close enough for related fields to piggy-bag. But currently there are not an awful lot of economics related projects in Python.

* The tension between the end of Moore's law and the need to process ever larger datasets will put a premium on performant platforms that can easily leverage heterogeneous GPU / multi-core CPU hardware. With sufficient effort any language can be used in a performant way (e.g. using lower-level libraries) but the researcher's time is typically best spend on science not HPC. Pure Python is notoriously slow but its popularity creates enormous demand for performant re-implementations. There are new initiatives developing all the time, for example the Mojo project that aims to provide a performant superset of Python. Languages with more native concurrency (Go, Elixir) may become more important (but may still lack domain-specific libraries)

* For empirical work sourcing data is important and (depending on the domain) may require significant pre-processing work. Famously 80% of data "science" is data cleaning. One could always use a toolkit approach (multiple languages), but as a general purpose language Python offers an advantage here.

All-in-all you need to continuously monitor the landscape.

FieldValue
text Predicting the programming language landscape in the next 5 years is nigh impossible as things evolve very fast and may get even faster. With the development of algorithms-who-code (based on LLM) we may get accelerating feedback loop effects. Ecosystems that have a lot of public code on which to train LLM's may benefit more from that dynamic and will become even more entrenched. Having said that, there are few factors that may be resilient drivers: * It will *probably* be the case that open s…
label r/econpapers
dataType comment
communityName r/EconPapers
datetime 2023-05-25
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Raw Record

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  "text": "Predicting the programming language landscape in the next 5 years is nigh impossible as things evolve very fast and may get even faster. With the development of algorithms-who-code (based on LLM) we may get accelerating feedback loop effects. Ecosystems that have a lot of public code on which to train LLM's may benefit more from that dynamic and will become even more entrenched. \n\nHaving said that, there are few factors that may be resilient drivers:\n\n* It will *probably* be the case that open source platforms (like Python, Julia, R) will be even more dominant but the allocation of mind share between them is unclear. Python is the current darling and is likely to coast on that popularity for a while. The driving domain is obviously Machine Learning and Deep Learning but it is close enough for related fields to piggy-bag. But currently there are not an awful lot of economics related projects in Python.\n\n* The tension between the end of Moore's law and the need to process ever larger datasets will put a premium on performant platforms that can easily leverage heterogeneous GPU / multi-core CPU hardware. With sufficient effort any language can be used in a performant way (e.g. using lower-level libraries) but the researcher's time is typically best spend on science not HPC. Pure Python is notoriously slow but its popularity creates enormous demand for performant re-implementations. There are new initiatives developing all the time, for example the Mojo project that aims to provide a performant superset of Python. Languages with more native concurrency (Go, Elixir) may become more important (but may still lack domain-specific libraries) \n\n* For empirical work sourcing data is important and (depending on the domain) may require significant pre-processing work. Famously 80% of data \"science\" is data cleaning. One could always use a toolkit approach (multiple languages), but as a general purpose language Python offers an advantage here.\n\nAll-in-all you need to continuously monitor the landscape.",
  "label": "r/econpapers",
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  "datetime": "2023-05-25",
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