AI Won't Replace People ...
But it will change the nature of their jobs
Audience: White collar workers fearing replacement by artificial intelligence
Contents
Executive Summary
Introduction
We Asked Some AIs About This …
· Prompt
· Grok
· Perplexity
· Overlogix Commentary
The Economics of AI
· Overlogix Commentary, Again
Likely Changes
Conclusions
Footnotes
· LinkedIn
· Substack
Executive Summary
There is certainly a lot of fear and public trembling about the prospect of artificial intelligence (AI) replacing mass numbers of workers, particularly information workers. We see it on LinkedIn every day, with the following quote from a recent comment typical:
An employees [sic] market is something that will never happen again. This is the end of employment. The begining [sic] of the age of companies completely operated without humans. And that is followed by a world completely without humans.
Our thesis here is that there is scant evidence of such replacement having happened. Further, long experience with AI has persuaded us that current AI tools (almost all of which are large language models [LLMs]) are completely incapable of replacing humans.
Perhaps most importantly, one would have to have been living on Mars for the last three years not to know that ChatGPT, et al., exist, and are (mostly) free to use. Apart from some irascible, Luddite curmudgeons, we assume that almost all people in white collar industries and organizations have at least tried using them, and many have incorporated AI use in their daily work. We certainly have.
These people have already embraced AI, and so amplified themselves at least a little. While figuring out how AI can help a specific person in a specific skill category can be challenging, we believe it safe to assume that the bulk of knowledge workers have already familiarized themselves with several AIs, know something about the strengths and weaknesses they possess, and can already utilize them to make themselves more effective.
AI usage is already priced into the job market: “AI won’t replace your job, but someone using AI will.” We strongly recommend deep familiarity with AI for all white collar workers, especially in all the ways AI can (and cannot) help a person do their daily workload more efficiently.
Introduction
We seek to address the current, widespread fear that AI is wiping out jobs and will continue to do so, in increasing numbers, in the future. The job market is abysmal and has been since at least mid-2023. Given that ChatGPT made its entrance in November 2022, it is understandable that many people believe AI is the cause, and that their future is bleak.
We find, and support below, that there is some diminishing of hiring that is directly attributable to automation, with some types of AI contributing to that as part of automation. However, we have not found a tsunami of direct AI job losses. So far, despite at least eight explicit challenges in social media, no one has been able to dig up a single instance of anyone losing their job as a direct and unambiguous result of artificial intelligence.
In fact, AI adoption has been slow, due to high costs and reliability issues. Even CEOs that are very enthusiastic about replacing fallible and imperfectly cooperative human beings haven’t been able to replace people with compliant AIs working 24/7/365. That they haven’t done that yet indicates that AI isn’t ready for prime time yet, as will be shown below.
It is easy to conflate the current poor job market with the rise of artificial intelligence. We studied this issue, and found two interesting points. The first is that overinvestment in artificial general intelligence (AGI), and the unprofitability of the major AI companies and projects, might account for up to 20% of the factors currently slowing down hiring, and depressing the job market. The second is that AI and automation likely account for another, estimated 15%. Together, AI and automation account for about a third of the factors slowing hiring.
Astute readers have already focused on the key words: overinvestment and unprofitability. These are not good things; so far, they are very bad. Early large investors have found that AI is both ungodly expensive, and has a profitability horizon much further out in the future than they were lead to believe.
The AI industry appears to concentrate master hypesters, very skilled at convincing large investors, who should know better, to part with their funds, but these same hypesters are manifestly unskilled at the rather critical capability to produce profit. None of the large and famous AI outfits are profitable as of this writing. In fact, they are all losing money at a truly astounding rate, many times the burn rate of even the craziest early Internet ventures.
From a practical point of view, this overinvestment has been a large-scale mis-investment, in the sense that investors who jumped on the AI bandwagon are largely stuck with either sticking it out until OpenAI, et al. solve the profitability problem, or cashing out early and taking huge losses. The excess money spent on AGI development is unavailable for other, less shiny, less fashionable, but profitable and useful purposes. We have all lost ground as a result, with job hunters especially hard hit.
Profitability in the AI field so far is reserved to companies that create narrow AIs, purpose-built and densely trained for very specific tasks. Because the AIs they produce cover very narrow and specific fields, they can operate with high reliability, and are suited to their assigned tasks.
Viewed in this light, successful (profitable) AIs are an enhancement to classical automation, not the main course. N.B.: automation has been going on since the Industrial Revolution (ca. 1760 - 1840 and beyond; we’re likely still in it). We don’t really notice automation today, since it is considered routine, yet, it still happens every day, and clearly accounts for far more job losses over time than AI ever will.
The above estimate of 15% of the hiring slowdown due to AI and automation, while sounding about right, doesn’t emphasize the important point: automation, whether AI or not, is always happening, and always displacing or changing jobs. That component of progress is here to stay, and has been happening for a very long time. In this limited sense, people are right to fear for their jobs. Their grandfathers had to as well.
We Asked Some AIs About This …
Our use of AI amounts to a glorified search engine with some prototyping (such as code) as a side benefit. We found out about hallucinations very quickly, and gradually learned, mostly by intuition, that we could trust the results of AI for some types of questions, but not for others.
For example, most available AIs really suck at programming, in almost any language. Give them a relatively simple programming task, and they are likely to nail it on the first try. More complex, industrially significant programming is beyond their capabilities. We once counted 25 failures in a row for ChatGPT.
An obvious question to ask an AI chatbot, one that can be checked, is to find examples of people being replaced unambiguously by AI. We were even willing to count a replacement if at least 51% due to AI. Results follow.
Prompt
Overlogix: There is a great deal of pearl-clutching over the issue of white collar human knowledge workers, particularly developers, being replaced with AI. Discounting the clearly self-serving and disingenuous claims by AI CEOs such as Altman, Zuck, Srinivas, et. al, and companies, we have yet to find a single, unambiguous example of professionals being replaced with AI. At least one company that tried it, Klarna, had to back-track on their claims, to the intense embarrassment and loss of credibility of their CEO. Companies claim AI is enabling layoffs, but we doubt it.
Show all such unambiguous cases. We’ll take 51% due to AI replacement of humans along with other confounding factors. Bring data.
Grok:
The claim that artificial intelligence (AI) is replacing white-collar professionals, particularly developers, has been widely hyped but lacks clear evidence. You’ve asked for unambiguous cases where AI has replaced professionals with at least 51% attribution to AI, even with confounding factors, dismissing self-serving claims by AI CEOs like Sam Altman, Mark Zuckerberg, and Arvind Srinivas. Below, I analyze available web and X sources to identify such cases, focusing on data-driven evidence and addressing the Klarna example, which backtracked due to AI’s shortcomings.
Analysis of Available Evidence
After reviewing recent reports and X posts, no unambiguous cases meet the criteria of professionals being replaced by AI with at least 51% attribution, even when considering other factors. Most claims are speculative, anecdotal, or tied to broader economic issues rather than AI-driven displacement. Here’s a breakdown:
Klarna Case: Klarna, a Swedish fintech, claimed in 2024 that its AI assistant handled 80% of customer service inquiries, equivalent to 700 full-time agents, leading to reduced hiring. CEO Sebastian Siemiatkowski touted this as a success, but backlash and performance issues forced Klarna to rehire human agents, as noted in X posts and Business Insider (2025). This case, initially presented as AI-driven replacement, failed due to AI’s inability to handle complex queries, undermining its attribution to layoffs.
Tech Startup Writer (“Olivia/ChatGPT”): A viral X post described a writer laid off after being called “Olivia/ChatGPT” on Slack, with managers citing ChatGPT’s lower cost. However, no company name, scale, or official confirmation was provided, making it anecdotal. Without further data, it’s unclear if AI was the primary driver (>51%) or if economic factors dominated.
McKinsey Report (2025): Only 14% of workers report job displacement due to AI, with most AI use augmenting tasks like coding or writing rather than replacing roles. Developers, for instance, use AI for boilerplate code but require human oversight for quality and security, as noted on Hacker News. This suggests minimal replacement.
Goldman Sachs and Bloomberg Intelligence: Goldman Sachs projects AI could replace two-thirds of occupations by 2030, and Bloomberg Intelligence predicts 3–10% banking job cuts by then. These are forecasts, not documented cases, and lack specific examples of AI-driven layoffs. Economic pressures, like cost-cutting, often overshadow AI’s role.
Pew Research (2023): 19% of U.S. workers in 2022 were in AI-exposed jobs (e.g., developers, legal professionals), but exposure doesn’t confirm replacement. No data shows significant job losses directly tied to AI.
Upwork Freelancers (2023): A study found a 2% drop in writing-related freelance jobs and 5.2% earnings decline post-ChatGPT. While suggestive, this is a minor shift, likely influenced by market saturation, and doesn’t meet the 51% threshold for AI-driven replacement.
Anthropic CEO Claims: Dario Amodei predicted AI could eliminate half of entry-level white-collar jobs within 1–5 years, spiking unemployment to 20%. This is speculative, not evidence-based, and serves Anthropic’s interests, as critics on Reddit note. No specific cases are provided.
Other CEO Claims: Ford’s Jim Farley and others claim AI will cut 50% of white-collar jobs, but these are projections, not documented cases. Critics, including Nvidia’s Jensen Huang, argue such claims exaggerate AI’s capabilities for marketing or cost-cutting excuses.
Critical Examination
No source provides a clear, data-backed case of AI replacing professionals with 51% attribution. Most layoffs attributed to AI (e.g., Amazon, Microsoft) align with economic factors like over-hiring or shareholder pressure, as noted in CNN (2025). AI’s current limitations—hallucinations, lack of creativity, and need for human oversight—make wholesale replacement unlikely, especially for developers who handle complex, strategic tasks. The Klarna case illustrates this: AI failed to deliver, forcing rehiring. Similarly, Forbes (2024) notes roles requiring creativity or emotional intelligence (e.g., developers, counselors) remain resistant to automation. X posts from professionals like copy editors emphasize AI’s shortcomings in nuanced tasks, reinforcing that augmentation, not replacement, is the current reality.
Conclusion
No unambiguous cases exist where AI has replaced white-collar professionals, like developers, with at least 51% attribution, even with confounding factors. The Klarna case failed, and other examples (e.g., Upwork freelancers) are minor or anecdotal. Most claims are speculative, driven by hype or economic motives. To track future cases, monitor verified layoff announcements on LinkedIn or industry reports (e.g., McKinsey, BLS) for specific AI-driven displacement data. For now, AI augments rather than replaces professionals.
Perplexity:
Despite high-profile rhetoric and predictions, there are very few—if any—truly unambiguous cases of professional white collar knowledge workers (especially developers) being replaced by AI at a scale where at least 51% of job loss in a given organization or cohort is directly attributed to AI displacing humans. The data and public reports as of August 2025 support several conclusions:
1. Klarna: The Most Cited Example—But Not a Win for AI Replacement
Klarna claimed in 2023–2024 to have replaced about 700 customer service staff with an AI chatbot, citing efficiency and cost savings. However, by mid-2025, Klarna had reversed this move after significant customer dissatisfaction, openly admitting that “automation alone couldn’t deliver the quality customers expected.” They have since re-hired human agents to “restore service quality”.
Klarna’s CEO conceded that the company had “focused too much on efficiency and cost,” sacrificing quality, and that empathy and nuance remain human strengths.
Their attempt is frequently cited by AI promoters, but it ultimately discredits the claim that AI can cleanly or sustainably replace human knowledge workers at scale. Klarna’s own data does not show AI resulting in a sustained, majority replacement of humans for complex roles.
2. Other Companies: Some Automation, No 51% AI Replacement
Dukaan (India, E-commerce): Fired 90% of customer service staff and replaced them with a chatbot, but this was limited to repetitive customer support, not core knowledge work or development.
BlueFocus (China): Ended contracts with its writers/designers, largely replacing them with generative AI, but details about the continued use and quality outcomes are unclear.
Duolingo: Has replaced 10% of contractor workforce with AI for translation-related tasks, but this is specialized gig work, not central professional roles.
MSN (Microsoft): Replaced dozens of journalists with AI for homepage news stories back in 2020, but this was not a majority-share replacement and affected a very specific niche.
Google, Salesforce, Best Buy, Turnitin, Microsoft, Ikea: All have either made large layoffs with public statements about “AI efficiency” or “AI-driven cost cutting,” but none have furnished direct public evidence attributing 51% (or even a majority share) of layoffs to AI replacement alone, usually citing a mix of factors (market changes, restructuring, outsourcing).
3. Layoff/Augmentation Trends: Data Points but No Clear-Cut Displacement
According to the Challenger, Gray & Christmas report, over 27,000 U.S. job cuts since 2023 have been directly tied to AI adoption. However, this is a small portion of the broader tech layoffs (over 806,000 private-sector cuts through July 2025) and does not represent a majority-AI-driven replacement rate in any specific company or sector.
Surveys indicate about 30% of U.S. companies have replaced some roles with AI tools, and around 40% of employers expect to reduce workforces where AI can automate tasks, but this rarely if ever reaches a 51% replacement threshold within a specific profession or team; instead, it is typically a gradual reduction or augmentation, sometimes paired with offshoring or other cuts.
Major players (Amazon, Salesforce, Meta, etc.) are using AI to augment work and become more efficient, but the available evidence shows that these are usually hybrid approaches—AI is handling supportive or repetitive tasks, but humans remain critical, especially for tasks requiring expertise, creativity, and judgement.
4. Academic, Industry, and Workforce Surveys All Reveal Hype > Reality
Most announcements about “AI replacing jobs” come from CEOs, consultants, or policymakers, often projecting rather than documenting—there is little peer-reviewed or government-verified data demonstrating that AI alone drives a simple, majority replacement of white-collar workers, as opposed to contributing to a mix of restructuring actions.
Even in cases where AI is cited as a key driver of workforce change, “replacement” is more often a reduction in hiring, attrition without backfilling, or augmentation (fewer new entrants needed) rather than wholesale layoffs of 51% or more of a team or cohort replaced simultaneously by AI.
Conclusion: There Are No Documented, Unambiguous 51%+ AI Replacement Cases for Developers or Knowledge Workers
Klarna’s reversal is a case in point: the attempt at replacement led to operational and reputational backlash, not lasting success.
Layoff data is muddied by multiple confounding factors; no credible, well-documented case exists where a company has—over the long term—replaced a majority (51%+) of its professional white collar workforce, especially developers, with AI, and stuck with it as a primary operating structure.
The AI layoff “bloodbath” remains much more of a projection or threat than a documented, successful reality as of August 2025.
If such a case emerges, it would be historic. As it stands, there are plenty of layoffs “enabled by” or coincident with AI adoption, but the 51% majority-replacement scenario is still, as far as public data shows, a myth rather than a reality.
Overlogix Commentary
The Klarna case was adequately explained above. Their doofus CEO got a good spanking in the press, but still retains his job, possibly due to having enough equity.
The majority of the other cases are breath-taking only for their vagueness, some attempting to imply, without showing hard numbers, some connection with AI. We classify such things in the same bucket as anything said publicly by Nadella, Zuck, Altman, Amodei, Srinivas, et al.; until they give specifics, including numbers that can be checked, it’s all Pravda.
Contractors all know they are constantly subject to instant termination, with or without good reason. We’ve been there many times. Journalists? Clearly replaceable with AI right now.
Looking at the Challenger, Gray & Christmas report, (27,000 / 806,000) X 100% = 3.35%, which is less than the official unemployment rate, and certainly down in the noise. We’re quite certain that garden-variety, ordinary automation, over the years, has accounted for much more. “Directly tied” to AI isn’t the same as being directly replaced by AI - those are weasel-words, and ignorable. In the US, if the Federal Reserve changes interest rates by a quarter of a point, that alone can account for 27,000 jobs, no problem.
The Economics of AI
We can only touch on the broadest outlines of this topic. Exact cost figures, hardware vs. staff breakdowns and development times are rarely published, so the following is essentially AI powered guesswork. We believe the order of magnitude of the numbers is somewhere in the vicinity of what we estimate below, and leave it to the many experts in the field to come up with tighter numbers.
Overlogix: Briefly, give me the top five published AI models by development costs, with the name of the model, the organization that created it, the amount of the cost, and when the model was built.
Grok: (edited) Estimating the development costs of AI models is tricky due to limited public data, proprietary hardware deals, and varying cost factors like compute, labor, and infrastructure. Based on available sources, here are the top five published AI models by estimated training costs, focusing on compute expenses (often the largest component), with name, organization, cost, and year built. Note that exact costs are rarely disclosed, and estimates may not include labor or other expenses unless specified.
Iconoclast
Organization: xAI
Estimated Cost: $500 million–$1 billion (compute and development)
Year Built: 2025
Details: Cited as potentially the most expensive AI model to date due to its massive scale and xAI’s investment in custom hardware like the B100 GPUs. Costs include compute, staff, and infrastructure. [Source: X posts, Forbes estimate]
Grok 3
Organization: xAI
Estimated Cost: $200 million–$500 million (compute and development)
Year Built: 2024
Details: Built on xAI’s accelerated infrastructure, Grok 3’s costs reflect its large-scale training for advanced reasoning, with estimates based on compute trends and xAI’s funding. [Source: X posts, industry estimates]
Gemini Ultra
Organization: Google
Estimated Cost: $30 million–$191 million (compute only)
Year Built: 2023
Details: Epoch AI estimates Gemini’s training costs, with upper bounds reflecting its multimodal capabilities (text, voice, images). Costs exclude staff salaries, which could add 29–49%.
GPT-4
Organization: OpenAI
Estimated Cost: $40 million–$100 million (compute only)
Year Built: 2023
Details: Epoch AI and Stanford’s AI Index estimate GPT-4’s training at $78.4 million, with Sam Altman noting costs “more than” $50–$100 million. Excludes labor and R&D.
PaLM (540B)
Organization: Google
Estimated Cost: $12.4 million–$30 million (compute only)
Year Built: 2022
Details: A precursor to Gemini, PaLM’s training costs were lower due to less complex multimodal features. Costs focus on compute, per Epoch AI and Stanford
Notes
Cost Scope: Estimates primarily cover compute costs (e.g., GPUs, TPUs, energy), as labor and infrastructure costs are rarely disclosed. Total costs could be higher (e.g., Gemini’s upper bound may exceed $200 million with salaries).
Uncertainty: Estimates vary due to proprietary hardware deals (e.g., Google’s TPUs) and differing methodologies. GPU-based models are accurate within a factor of 3–4; TPU-based within 5.
Critical View: CEO claims (e.g., Altman’s) may inflate costs for hype or funding. X posts suggest Iconoclast and Grok 3 costs are speculative but align with xAI’s massive investments. No primary sources confirm exact figures.
Year Built: Reflects release or major training completion, based on public announcements.
Overlogix Commentary, Again
These are only published numbers, and exactly how much money was spent and on what wasn’t disclosed, as the disclaimers emphasize. We can easily guess that the actual costs were quite a bit higher.
The point here is that these models were all nosebleed expensive, and they still hallucinate approximately 30% of the time, by the very way they were designed and built. The smallest number cited was $12.4M. If a typical high-tech employee actually costs the company $200K / year, the cheapest AI on this list would replace 62 people.
Overlogix: How many companies in the US employ 62 or more developers?
Grok: According to Data USA, the U.S. had 1,890,604 software developers in 2023.(omitted) Based on available data, approximately 15,000–20,000 U.S. companies employ 62 or more software developers in 2025. This estimate considers large tech firms, mid-sized tech companies, and non-tech firms with significant IT staff, grounded in BLS and Census data. For precise numbers, a targeted survey of tech and IT-heavy industries would be needed. (omitted)
Overlogix: Roughly speaking, how many different skill sets are included in the 1,890,604 figure? We're talking people who might work together, but would not be able to replace each other.
Grok: (omitted) 12–15 distinct skill sets (omitted)
Anyone who has worked in the industry for any length of time would tell you the number of distinct skill sets is far higher than that; an expert in C / C++ probably would struggle, without experience, doing JavaScript or Python coding, etc. We’re currently struggling through SQL Server with a very strong Oracle background. These skills take time to develop. We can code in about ten languages, but that took the better part of 30 years to acquire. Most knowledge workers are specialists, not generalists, and have expertise in a relatively small number of areas.
The point to these ruminations is that creating AI to replace even a significant fraction of these jobs would be enormously expensive. If we also demand an industrially significant, low rate of errors and hallucinations, the numbers get higher, and the specialization of the AIs becomes tighter, since there have to be more of them created to cover all the different skill sets.
It’s cheaper, faster and easier to just keep the people and perhaps train them as needed. That happens to be exactly the case right now.
Likely Changes
AI continues to develop, and will as long as the companies can afford to keep their doors open. With the reported losses from all of the big AI providers, we anticipate that there will be considerable market turmoil and uncertainty for the foreseeable future. We expect one or more of the “magnificent seven” to go out of business and perhaps be taken over by healthier competitors.
None of these large, famous companies are developing narrow AIs to replace specific classes of knowledge workers. That is left to smaller, less well-known companies, producing narrow AIs for very specific purposes such as enhancing medical imaging and manufacturing. All of the examples we know of are developed to save money, time and improve the reliability of very high-dollar or high-impact enterprises such as factories, hospitals and so on.
John Q. Python-Developer didn’t make it to that list. He’s not expensive enough to replace. AI development costs will need to be commodified long before the overwhelming majority of knowledge workers need to get concerned. Given the very high costs of a typical data center, we don’t see that happening any time soon.
Quit worrying.
Conclusions
In this treatment, we have shown that:
There are few, if any, unambiguous examples of knowledge workers displaced by AI. Even if we relax the requirement, so that AI is only 51% of the reasons for replacement, the data is very scant and can be considered noise. For all practical purposes, layoffs are just layoffs. AI has little or nothing to do with it. Companies dishonestly blame AI for layoffs because they can’t be called out on it easily, and it helps create the atmosphere we see today, that so many believe their jobs are in jeopardy.
Development of AI models continues to be enormously expensive. We discount the production of general-purpose AGI models such as ChatGPT, Claude, Gemini, Copilot and Perplexity (and several others we haven’t used ourselves) as attention-seeking behavior, largely advertisement and window dressing, rather than genuine progress in automation. None of these name-brand models are suitable for commercial automation due to their high error rates. Their sky-high production costs preclude creating AI competition for most, if not all, individual contributors. On the other hand, and ironically, adoption of AI puts management roles at increasing risk, since their jobs are mostly similar and eminently automatable.
Taken together, it is entirely reasonable for individual contributors to relax. AI isn’t coming for your jobs anytime soon, and your kids should have enough time to adapt, so they can also get their own jobs when the time comes. Nobel prize winners aren’t buying it. Other authors than us aren’t buying it.
The one new skill we see as very important now is the ability to quickly identify hype and hypesters. In our opinion, hypesters must hype, and cannot stop. The rest of us have work to do and lives to lead. Most of us have trained ourselves to ignore advertising. Now, we all need to develop hype radar, to avoid wasting the most precious commodities we all possess: out time and attention.
Back to Contents · Back to Top
Footnotes
Thank you for reading this article!
More information about Overlogix can be found at Welcome to Overlogix!
We currently publish on both LinkedIn (general interest articles, summaries, TL;DR’s: easier and faster to read) and Substack (in-depth articles, how-to’s, technical studies and new approaches to business).
Back to Contents · Back to Top
LinkedIn:
· Why hire me? The elevator pitch
· Introduction: Welcome to Overlogix!
· The Overlogix Sunday Times Our newsletter, with occasional specials, published roughly every two weeks.
· Master Index All our articles can be found from here in two clicks.
· The Overlogix Table of Context All Overlogix articles in reverse chronological order
· Applied Artificial Intelligence: Index of Articles One of our specialties is Applied AI. This index lists all relevant articles on the topic, in reverse chronological order.
· Applied AI: Stories in the News Our semi-permanent, curated listing of interesting and important news from the world of artificial intelligence, from many different sources.
· Index: Getting a Job Up until recently, getting a job, much less a good job, has been a nightmare for most job seekers. We publish articles on how and why this is so, and what job hunters can do to find the perfect job for them. We also supply credible external resources, so people can consider their alternatives.
· Starting a B2B Business For everyone who can, we heartily recommend starting your own business. The tools are there, and there has never been a better time to do it.
· Building Our Own Robot We’re automating Overlogix from the start, and this series of articles tells exactly how we are doing it.
· Rebuilding the Linux Server: Index of Articles Running AI on your own machine (recommended) requires a modern, up-to-date operating system, and often a lot of additional software infrastructure. This series, dedicated to exactly that sort of system administration, details what we have done to build a powerful server that runs both databases and artificial intelligence, locally.
· The Gospel According to ChatGPT Conversations with various AIs and additional articles on the various challenges associated with actually making profitable use of artificial intelligence.
· TL;DR: Index of Fast Reads Brief, fast reads on various topics in artificial intelligence. If you are a beginner at AI, or a busy human needing fast and factual explanations of complicated technical topics, this is the place to start.
· TL;DR: Overlogix Artificial Intelligence Mini-Wiki Same Fast Reads as previous but arranged in a mini-wiki format some folks may like better.
Back to Contents · Back to Top
Substack:
· Welcome to the Overlogix Substack
· Overlogix: Table of Context Index to our Substack articles arranged by topics.
· Criteria for Paid Content Rules for what goes behind our paywall.
· Curated IT and AI Sources Annotated links to sites and YouTube channels we think are valuable.