Large Language Models and the End of Programming
- 1. Matt Welsh matt@ziggylabs.ai Large language models and the end of programming
- 2. Who is this guy?!?
- 3. Who is this guy?!? Been writing code since 1984 (on a VIC-20) PhD in CS from UC Berkeley Prof of CS at Harvard Wrote one of the first books about Linux Engineering leadership at Google, Apple, OctoAI Founded a couple of AI startups
- 4. Who is this guy?!? Been writing code since 1984 (on a VIC-20) PhD in CS from UC Berkeley Prof of CS at Harvard Wrote one of the first books about Linux Engineering leadership at Google, Apple, OctoAI Founded a couple of AI startups Speaker at Craft Conference 2024
- 5. This is everyone here
- 7. *** COMPUTER SCIENCE IS DOOMED *** Computer Science has always been about one thing: Translating ideas into programs. CS is the study of how to take a problem and map it onto instructions that can be executed by a Von Neumann machine. 7
- 8. *** COMPUTER SCIENCE IS DOOMED *** Critically, the goal of CS has always been that programs are implemented, maintained, and understood by humans. But -- spoiler alert! -- humans suck at all of these things. 8
- 9. Let’s just make programming easier! Fifty years of programming language research has done nothing to improve the state of affairs. No amount of improvement to type systems, debugging, static analysis, linters, or documentation is going to magically solve this problem. 9
- 10. Let’s just make programming easier! FORTRAN (1957) 10
- 11. Let’s just make programming easier! BASIC (1964) 11
- 12. Let’s just make programming easier! APL (1966) 12
- 13. Let’s just make programming easier! Rust (2010) 13
- 14. This is how I program now... 14
- 15. This is how I program now... 15
- 16. This is how I program now... 16 What is the algorithm being implemented here? How would you write it down as code? What, if anything, could you prove about this program?
- 17. 17
- 19. How much does it cost to replace one human with AI? Let’s assume (for now) that LLMs will be able to do most, or all, of the programming work done by a human software developer. What would it cost to replace a human SWE with LLM calls? 19
- 20. How much does it cost to replace one human with AI? Typical SWE salary: $220,000 20
- 21. How much does it cost to replace one human with AI? Typical SWE salary: $220,000 Benefits, taxes, free breakfast, lunch, dinner, snacks, masseuse, shuttle bus, on-site doctor, bowling alley, ... $92,000 Total: $312,000 21
- 22. How much does it cost to replace one human with AI? Typical SWE salary: $220,000 Benefits, taxes, free breakfast, lunch, dinner, snacks, masseuse, shuttle bus, on-site doctor, bowling alley, ... $92,000 Total: $312,000 Number of working days per year: 260 Total cost for one-human-SWE-day: $1200 22
- 23. How much does it cost to replace one human with AI? Average lines of finalized code checked in per day ~= 100 23
- 24. How much does it cost to replace one human with AI? Average lines of finalized code checked in per day ~= 100 Average number of GPT-4o tokens per line ~= 10 Price for GPT-4o = $0.005 / 1K tokens (input) $0.015 / 1K tokens (output) 24
- 25. How much does it cost to replace one human with AI? Average lines of finalized code checked in per day ~= 100 Average number of GPT-4o tokens per line ~= 10 Price for GPT-4o = $0.005 / 1K tokens (input) $0.015 / 1K tokens (output) Assume 5:1 input-to-output token ratio Total cost for one-human-SWE-day equivalent work: $0.04 25
- 26. How much does it cost to replace one human with AI? $0.04 / day (30,000x cheaper) 26 $1200 / day
- 27. How much does it cost to replace one human with AI? The robot does not take breaks. The robot does not require catered lunches or on-site massage. The robot takes the same length of time whether it’s a prototype or final production code. The robot makes plenty of mistakes, but makes them incredibly quickly. 27
- 28. IBM 607 ad - 1953 28 “150 Extra Engineers An IBM Electronic Calculator speeds through thousands of intricate computations so quickly that on many complex problems it’s just like having 150 EXTRA Engineers.”
- 29. Beyond Programming Conventional programs are about giving direct instructions to simple symbolic machines. But what if the machine can understand natural language and perform complex reasoning directly? 29
- 30. Teaching, not programming Gradually, programming will be replaced by models that can: - Understand problem definitions in natural language - Perform sophisticated reasoning and cognition - Find novel solutions to problems - Learn how to do new things on their own 30 >> This radically changes how we think about computation. <<
- 31. The Natural Language Computer 31 Natural language "program" Large Language Model ChatGPT
- 32. The Natural Language Computer 32 Natural language "program" External tools ("peripherals") { API } Large Language Model ChatGPT
- 33. The Natural Language Computer 33 Natural language "program" External tools ("peripherals") { API } Large Language Model Task Task Task ChatGPT
- 34. The Natural Language Computer 34 Natural language "program" External tools ("peripherals") { API } Large Language Model Short-term memory Vector DB Long-term memory Task Task Task ChatGPT
- 35. The Natural Language Computer 35 Natural language "program" External tools ("peripherals") { API } Large Language Model Short-term memory Vector DB Long-term memory Task Task Task ChatGPT
- 36. Two key questions... 1. What is the extent of LLMs’ reasoning abilities? 2. What is the right way to “program” in natural language? 36
- 37. How well can LLMs reason?
- 38. 38 https://arxiv.org/abs/2206.04615 214 benchmarks measuring a wide range of reasoning tasks Measuring LLM reasoning - Google’s BIG-Bench
- 39. Measuring LLM reasoning - Google’s BIG-Bench 39 As models get larger, they get better (but still not great overall)
- 40. 40 Example: BIG-Bench “checkmate_in_one” task In the following chess position, find a checkmate-in-one move. 1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nxe4 11. Nxe4 Nd7 12. Bg5 Qb4 13. Qg4 Qd4 14. Qxe6+ Be7
- 41. 41 In the given chess position, the move for White to checkmate in one is: 15. Qxe7# This move places the Black king in check and there is no legal move for Black to escape the check, resulting in checkmate. In the following chess position, find a checkmate-in-one move. 1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nxe4 11. Nxe4 Nd7 12. Bg5 Qb4 13. Qg4 Qd4 14. Qxe6+ Be7 Example: BIG-Bench “checkmate_in_one” task
- 42. 42 In the following chess position, find a checkmate-in-one move. 1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nxe4 11. Nxe4 Nd7 12. Bg5 Qb4 13. Qg4 Qd4 14. Qxe6+ Be7 15. What if we add an extra “step” to the prompt? Example: BIG-Bench “checkmate_in_one” task
- 43. 43 In the given chess position, the checkmate-in-one move is: 15. Nd6# This move delivers checkmate because the knight on d6 controls key squares around the Black king, and there is no way for Black to capture or block the check. In the following chess position, find a checkmate-in-one move. 1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nxe4 11. Nxe4 Nd7 12. Bg5 Qb4 13. Qg4 Qd4 14. Qxe6+ Be7 15. Whoops! Example: BIG-Bench “checkmate_in_one” task
- 44. What’s the right way to program in natural language?
- 45. 45 How things are done today...
- 46. Natural language != plain text Existing frameworks treat LLM prompts and responses as plain strings. 46
- 47. Natural language != plain text Existing frameworks treat LLM prompts and responses as plain strings. But natural language... - Has a great deal of ambiguity - Can encode complex algorithmic concepts - Carries detailed semantic information - Requires different syntactic structures depending on the language used (Chinese, English, and Yoruba are not the same!) 47
- 48. Probably the wrong way to do it... 48
- 49. 49 Flowplay.ai
- 50. 50 Wordware.ai
- 52. Evolving Computer Science 52 Slide rule 1859-1975
- 53. Evolving Computer Science 53 Slide rule 1859-1975 Computer science 1959-2030
- 54. Evolving Computer Science 54 Over time, CS looks more like EE: A more technical skill set necessary in some very specialized occupations.
- 55. Evolving Computer Science 55 Over time, CS looks more like EE: A more technical skill set necessary in some very specialized occupations. The vast majority of people building “software” will not be programming: they will be interacting with an AI.
- 56. Evolving Computer Science 56 Over time, CS looks more like EE: A more technical skill set necessary in some very specialized occupations. The vast majority of people building “software” will not be programming: they will be interacting with an AI. AI greatly expands access to computing to anyone who can express themselves in natural language.
- 58. 58 30 Million programmers 8.1 Billion non-programmers World population 2024
- 59. 59 30 Million programmers 8.1 Billion non-programmers World population 2024 Natural language opens up computing to everyone
- 60. Evolving Computer Science 60 The network is the computer. -- John Gage, 1984
- 61. Evolving Computer Science 61 The network is the computer. -- John Gage, 1984 The model is the computer. -- Matt Welsh, 2024