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Why didn't AI “join the workforce” in 2025?

327 comments · 22,484 words

Complete Created: Jan 6, 11:23 AM (00:09:36)

Models: Gemini 3 Pro (analyze) · Gemini 3 Pro (tag) · Gemini 3 Flash (summarize)

Article URL: https://calnewport.com/why-didnt-ai-join-the-workforce-in-2025/ (1,653 words)

Article Summary

Cal Newport examines why the confident predictions that 2025 would be the year AI agents "joined the workforce" failed to materialize. Despite forecasts from industry leaders like Sam Altman and Kevin Weil that AI would autonomously handle complex tasks like booking hotels or filling out paperwork, actual releases such as ChatGPT Agent struggled with basic interactions. Newport cites skepticism from experts like Gary Marcus regarding the limitations of the underlying Large Language Models and concludes that society should move past hype-driven prophecies to focus on the tangible, albeit limited, capabilities AI currently possesses.

Comment Summary

The discussion reflects a sharp divide between skeptics who view LLMs as overhyped pattern-matchers and proponents who experience significant productivity gains in specific workflows. A major focus is on software development, where tools like Claude Code and Codex are praised for accelerating prototyping and refactoring, yet criticized for producing difficult-to-verify code and lacking true reasoning. Broader themes include the economic implications of AI (the "bubble" debate), the redefinition of "work" from creation to orchestration, and fears regarding job displacement, skill atrophy, and the proliferation of "bullshit jobs" managed by automation.

Topics

AI-Assisted Coding Reality (61 comments) (Divergent experiences with tools like Claude Code and Codex. While some report massive productivity boosts and shipping entire features solo, others describe "lazy" AI, subtle logic bugs in generated tests (e.g., SQL query validation), and the danger of unverified code bloat.)
Corporate Hype vs. Utility (56 comments) (Cynicism toward executive predictions (Altman, Hinton) viewed as efforts to pump stock prices or attract investment. Users contrast "corporate puffery" and "vaporware" with the practical, often mundane utility of AI in specific B2B workflows like insurance claim processing or data extraction.)
Reasoning vs. Pattern Matching (52 comments) (Debates on whether LLMs truly think or merely predict tokens based on training data. Includes comparisons to human cognition, the definition of "reasoning" as argument production versus evaluation, and the argument that LLMs are "lobotomized" without external loops or formalization.)
Workforce Displacement and Automation (48 comments) (Fears and anecdotes regarding job security, including a "Staff SWE" preferring AI to coworkers and contractors losing bids to smaller, AI-equipped teams. Discussions cover the automation of "bullshit jobs," the potential for a "winner take all" economy, and management incentives to cut labor costs.)
The AI Economic Bubble (47 comments) (Comparisons to the dot-com crash, with arguments that current valuation relies on "science fiction fantasies" and hype rather than revenue. Counter-arguments suggest the infrastructure (datacenters, GPUs) provides real value similar to the fiber build-out, even if a market correction is imminent.)
Productivity Metrics and Paradoxes (40 comments) (Skepticism regarding "2x productivity" claims. Commenters argue that generating more code doesn't equal value, noting that debugging, communicating, and context-gathering are the real bottlenecks, and that AI might simply be increasing the volume of low-quality output or "slop.")
Verification and Hallucination Risks (33 comments) (The critical need for external validation mechanisms. Commenters note that coding agents succeed because compilers/linters act as truth-checkers, whereas open-ended tasks (spreadsheets, emails) lack rigorous feedback loops, making hallucinations and "truthy" errors dangerous and hard to detect.)
The Secretary vs. Replacement Model (25 comments) (The shift in expectations from AI as an autonomous employee to AI as a productivity-enhancing assistant. Users describe workflows where humans act as orchestrators or managers of AI output rather than performing the rote work, effectively reviving the role of the personal secretary.)
Definition of Agentic Success (19 comments) (Disagreement over whether AI "joined the workforce." Some argue failing to replace humans entirely (the "secretary" model) is a failure of 2025 predictions, while others claim deep integration as a tool (automating loops, drafting emails) constitutes a successful, albeit different, type of joining.)
Impact on Skill and Learning (18 comments) (Concerns about the long-term effects on human expertise. Topics include "skill atrophy" where juniors bypass learning fundamentals, the educational crisis evidenced by Chegg's collapse, and the difficulty of debugging AI code without deep institutional knowledge or "muscle memory" of the system.)
Software Engineering Evolution (16 comments) (Predictions that the discipline is shifting from "writing code" to "managing entropy" and system design. Some view this as empowering "cowboy devs" to move fast, while others fear a future of unmaintainable "vibe coded" software that no human fully understands.)
Integration into Legacy Systems (12 comments) (The challenge of applying AI to real-world, messy environments versus greenfield demos. Discussion includes the difficulty of getting agents to work with proprietary codebases, expensive dependencies, lack of documentation for obscure vendor tools, and the failure of browser agents on standard web forms.)
Medical and Specialized Fields (11 comments) (Debates on AI in radiology and medicine. While some see potential in automated reporting and "second opinions" to catch errors, professionals argue that current models struggle with complex cases, over-report issues, and lack the nuance required for high-stakes diagnostics.)
Formalization of Natural Language (9 comments) (Theoretical discussions on overcoming LLM limitations by mapping natural language to formal logic or proof systems (like Lean). Skeptics argue human language is too "mushy" or context-dependent for this to be a silver bullet for AGI or perfect reasoning.)

Raw Files

HN HTML Article HTML HN Text Article Text

Execution Log

[2026-01-06T19:23:44.343Z] Starting step: fetch_pages (attempt 1)
[2026-01-06T19:23:44.391Z] Fetching HN page: https://news.ycombinator.com/item?id=46505735
[2026-01-06T19:23:44.582Z] Fetched HN page: 538580 bytes
[2026-01-06T19:23:44.845Z] Extracted title: Why didn't AI “join the workforce” in 2025?
[2026-01-06T19:23:44.875Z] Extracted linked URL: https://calnewport.com/why-didnt-ai-join-the-workforce-in-2025/
[2026-01-06T19:23:44.911Z] Fetching linked article: https://calnewport.com/why-didnt-ai-join-the-workforce-in-2025/
[2026-01-06T19:23:44.991Z] Fetched linked article: 132163 bytes
[2026-01-06T19:23:45.257Z] Completed step: fetch_pages in 884ms
[2026-01-06T19:23:45.541Z] Starting step: extract_text (attempt 1)
[2026-01-06T19:23:45.685Z] Extracted HN text: 148836 chars
[2026-01-06T19:23:45.898Z] Extracted 327 comments
[2026-01-06T19:23:46.102Z] Extracted linked article text: 9657 chars, 1653 words
[2026-01-06T19:23:46.279Z] Comment word count: 22484
[2026-01-06T19:23:46.374Z] Completed step: extract_text in 805ms
[2026-01-06T19:23:46.576Z] Starting step: analyze_content (attempt 1)
[2026-01-06T19:23:46.776Z] Calling gemini-3-pro-preview (article: 9657 chars, 327 comments)
[2026-01-06T19:24:34.354Z] Analysis complete: 14 topics, 32552 input tokens, 1158 output tokens
[2026-01-06T19:24:34.461Z] Completed step: analyze_content in 47844ms
[2026-01-06T19:24:34.695Z] Starting step: tag_comments (attempt 1)
[2026-01-06T19:24:34.804Z] Tagging 327 comments with 14 topics (batch size: 50)
[2026-01-06T19:24:34.840Z] Processing batch 1/7 (50 comments)
[2026-01-06T19:25:25.138Z] Batch 1 complete: 67 tags assigned
[2026-01-06T19:25:25.287Z] Processing batch 2/7 (50 comments)
[2026-01-06T19:26:23.783Z] Batch 2 complete: 73 tags assigned
[2026-01-06T19:26:23.830Z] Processing batch 3/7 (50 comments)
[2026-01-06T19:27:23.734Z] Batch 3 complete: 89 tags assigned
[2026-01-06T19:27:23.761Z] Processing batch 4/7 (50 comments)
[2026-01-06T19:28:28.670Z] Batch 4 complete: 84 tags assigned
[2026-01-06T19:28:28.702Z] Processing batch 5/7 (50 comments)
[2026-01-06T19:29:20.608Z] Batch 5 complete: 86 tags assigned
[2026-01-06T19:29:20.647Z] Processing batch 6/7 (50 comments)
[2026-01-06T19:30:20.040Z] Batch 6 complete: 76 tags assigned
[2026-01-06T19:30:20.095Z] Processing batch 7/7 (27 comments)
[2026-01-06T19:31:08.532Z] Batch 7 complete: 40 tags assigned
[2026-01-06T19:31:08.564Z] Tagging complete: 515 total tags, 46472 input tokens, 7573 output tokens
[2026-01-06T19:31:08.594Z] Completed step: tag_comments in 393857ms
[2026-01-06T19:31:08.792Z] Starting step: summarize_topics (attempt 1)
[2026-01-06T19:31:08.882Z] Summarizing 14 topics
[2026-01-06T19:31:08.987Z] Summarizing topic 1/14: "Reasoning vs. Pattern Matching # Debates on whether LLMs truly think or merely predict tokens based on training data. Includes comparisons to human cognition, the definition of "reasoning" as argument production versus evaluation, and the argument that LLMs are "lobotomized" without external loops or formalization." (52 comments)
[2026-01-06T19:31:16.171Z] Topic 1 summarized (5929 in, 152 out)
[2026-01-06T19:31:16.235Z] Summarizing topic 2/14: "AI-Assisted Coding Reality # Divergent experiences with tools like Claude Code and Codex. While some report massive productivity boosts and shipping entire features solo, others describe "lazy" AI, subtle logic bugs in generated tests (e.g., SQL query validation), and the danger of unverified code bloat." (61 comments)
[2026-01-06T19:31:24.341Z] Topic 2 summarized (8244 in, 176 out)
[2026-01-06T19:31:24.410Z] Summarizing topic 3/14: "The AI Economic Bubble # Comparisons to the dot-com crash, with arguments that current valuation relies on "science fiction fantasies" and hype rather than revenue. Counter-arguments suggest the infrastructure (datacenters, GPUs) provides real value similar to the fiber build-out, even if a market correction is imminent." (47 comments)
[2026-01-06T19:31:30.915Z] Topic 3 summarized (4420 in, 124 out)
[2026-01-06T19:31:30.968Z] Summarizing topic 4/14: "Workforce Displacement and Automation # Fears and anecdotes regarding job security, including a "Staff SWE" preferring AI to coworkers and contractors losing bids to smaller, AI-equipped teams. Discussions cover the automation of "bullshit jobs," the potential for a "winner take all" economy, and management incentives to cut labor costs." (48 comments)
[2026-01-06T19:31:46.996Z] Topic 4 summarized (5639 in, 156 out)
[2026-01-06T19:31:47.057Z] Summarizing topic 5/14: "Definition of Agentic Success # Disagreement over whether AI "joined the workforce." Some argue failing to replace humans entirely (the "secretary" model) is a failure of 2025 predictions, while others claim deep integration as a tool (automating loops, drafting emails) constitutes a successful, albeit different, type of joining." (19 comments)
[2026-01-06T19:31:59.600Z] Topic 5 summarized (3412 in, 159 out)
[2026-01-06T19:31:59.662Z] Summarizing topic 6/14: "Verification and Hallucination Risks # The critical need for external validation mechanisms. Commenters note that coding agents succeed because compilers/linters act as truth-checkers, whereas open-ended tasks (spreadsheets, emails) lack rigorous feedback loops, making hallucinations and "truthy" errors dangerous and hard to detect." (33 comments)
[2026-01-06T19:32:06.958Z] Topic 6 summarized (4679 in, 157 out)
[2026-01-06T19:32:07.007Z] Summarizing topic 7/14: "Impact on Skill and Learning # Concerns about the long-term effects on human expertise. Topics include "skill atrophy" where juniors bypass learning fundamentals, the educational crisis evidenced by Chegg's collapse, and the difficulty of debugging AI code without deep institutional knowledge or "muscle memory" of the system." (18 comments)
[2026-01-06T19:32:15.390Z] Topic 7 summarized (3526 in, 160 out)
[2026-01-06T19:32:15.443Z] Summarizing topic 8/14: "Corporate Hype vs. Utility # Cynicism toward executive predictions (Altman, Hinton) viewed as efforts to pump stock prices or attract investment. Users contrast "corporate puffery" and "vaporware" with the practical, often mundane utility of AI in specific B2B workflows like insurance claim processing or data extraction." (56 comments)
[2026-01-06T19:32:21.545Z] Topic 8 summarized (5885 in, 156 out)
[2026-01-06T19:32:21.622Z] Summarizing topic 9/14: "Integration into Legacy Systems # The challenge of applying AI to real-world, messy environments versus greenfield demos. Discussion includes the difficulty of getting agents to work with proprietary codebases, expensive dependencies, lack of documentation for obscure vendor tools, and the failure of browser agents on standard web forms." (12 comments)
[2026-01-06T19:32:30.763Z] Topic 9 summarized (1516 in, 160 out)
[2026-01-06T19:32:31.031Z] Summarizing topic 10/14: "Formalization of Natural Language # Theoretical discussions on overcoming LLM limitations by mapping natural language to formal logic or proof systems (like Lean). Skeptics argue human language is too "mushy" or context-dependent for this to be a silver bullet for AGI or perfect reasoning." (9 comments)
[2026-01-06T19:32:42.308Z] Topic 10 summarized (1278 in, 131 out)
[2026-01-06T19:32:42.440Z] Summarizing topic 11/14: "Medical and Specialized Fields # Debates on AI in radiology and medicine. While some see potential in automated reporting and "second opinions" to catch errors, professionals argue that current models struggle with complex cases, over-report issues, and lack the nuance required for high-stakes diagnostics." (11 comments)
[2026-01-06T19:32:49.537Z] Topic 11 summarized (1637 in, 128 out)
[2026-01-06T19:32:49.607Z] Summarizing topic 12/14: "The Secretary vs. Replacement Model # The shift in expectations from AI as an autonomous employee to AI as a productivity-enhancing assistant. Users describe workflows where humans act as orchestrators or managers of AI output rather than performing the rote work, effectively reviving the role of the personal secretary." (25 comments)
[2026-01-06T19:32:58.294Z] Topic 12 summarized (4089 in, 158 out)
[2026-01-06T19:32:58.352Z] Summarizing topic 13/14: "Software Engineering Evolution # Predictions that the discipline is shifting from "writing code" to "managing entropy" and system design. Some view this as empowering "cowboy devs" to move fast, while others fear a future of unmaintainable "vibe coded" software that no human fully understands." (16 comments)
[2026-01-06T19:33:06.131Z] Topic 13 summarized (2555 in, 174 out)
[2026-01-06T19:33:06.186Z] Summarizing topic 14/14: "Productivity Metrics and Paradoxes # Skepticism regarding "2x productivity" claims. Commenters argue that generating more code doesn't equal value, noting that debugging, communicating, and context-gathering are the real bottlenecks, and that AI might simply be increasing the volume of low-quality output or "slop."" (40 comments)
[2026-01-06T19:33:15.650Z] Topic 14 summarized (4711 in, 165 out)
[2026-01-06T19:33:15.678Z] Summarization complete: 14 topics, 57520 input tokens, 2156 output tokens
[2026-01-06T19:33:15.709Z] Completed step: summarize_topics in 126861ms
[2026-01-06T19:33:15.820Z] Job completed successfully

LLM Invocations (Total: $0.2980)

Time	Purpose	Model	Duration	Outcome	Input	Output	Cost
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11:25 AM	Tag comments	gemini-3-pro-preview	49.8s	Success	Input (7,447)	Output (1,111)	$0.0282
11:26 AM	Tag comments	gemini-3-pro-preview	57.9s	Success	Input (7,120)	Output (1,139)	$0.0279
11:27 AM	Tag comments	gemini-3-pro-preview	59.4s	Success	Input (7,667)	Output (1,199)	$0.0297
11:28 AM	Tag comments	gemini-3-pro-preview	1.1m	Success	Input (8,222)	Output (1,173)	$0.0305
11:29 AM	Tag comments	gemini-3-pro-preview	50.9s	Success	Input (6,962)	Output (1,176)	$0.0280
11:30 AM	Tag comments	gemini-3-pro-preview	59.0s	Success	Input (5,628)	Output (1,150)	$0.0251
11:31 AM	Tag comments	gemini-3-pro-preview	48.0s	Success	Input (3,426)	Output (625)	$0.0144
11:31 AM	Summarize topic	gemini-3-flash-preview	6.8s	Success	Input (5,929)	Output (152)	$0.0034
11:31 AM	Summarize topic	gemini-3-flash-preview	7.5s	Success	Input (8,244)	Output (176)	$0.0046
11:31 AM	Summarize topic	gemini-3-flash-preview	6.1s	Success	Input (4,420)	Output (124)	$0.0026
11:31 AM	Summarize topic	gemini-3-flash-preview	6.7s	Success	Input (5,639)	Output (156)	$0.0033
11:31 AM	Summarize topic	gemini-3-flash-preview	12.1s	Success	Input (3,412)	Output (159)	$0.0022
11:32 AM	Summarize topic	gemini-3-flash-preview	6.9s	Success	Input (4,679)	Output (157)	$0.0028
11:32 AM	Summarize topic	gemini-3-flash-preview	8.0s	Success	Input (3,526)	Output (160)	$0.0022
11:32 AM	Summarize topic	gemini-3-flash-preview	5.6s	Success	Input (5,885)	Output (156)	$0.0034
11:32 AM	Summarize topic	gemini-3-flash-preview	8.2s	Success	Input (1,516)	Output (160)	$0.0012
11:32 AM	Summarize topic	gemini-3-flash-preview	10.3s	Success	Input (1,278)	Output (131)	$0.0010
11:32 AM	Summarize topic	gemini-3-flash-preview	6.5s	Success	Input (1,637)	Output (128)	$0.0012
11:32 AM	Summarize topic	gemini-3-flash-preview	8.3s	Success	Input (4,089)	Output (158)	$0.0025
11:33 AM	Summarize topic	gemini-3-flash-preview	7.3s	Success	Input (2,555)	Output (174)	$0.0018
11:33 AM	Summarize topic	gemini-3-flash-preview	9.1s	Success	Input (4,711)	Output (165)	$0.0029

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