What\u2019s making many people resent generative AI, and what impact does that have on the companies responsible?<\/h4>\n![\"\"](\"https:\/\/cdn-images-1.medium.com\/max\/1024\/0*ZQwqwXv8DoOwNnsC\")
Photo by Joshua Hoehne<\/a> on\u00a0Unsplash<\/a><\/figcaption><\/figure>\nThe recent reveal of DeepSeek-R1, the large scale LLM developed by a Chinese company (also named DeepSeek), has been a very interesting event for those of us who spend time observing and analyzing the cultural and social phenomena around AI. Evidence suggests that R1 was trained for a fraction of the price that it cost to train ChatGPT<\/a> (any of their recent models, really), and there are a few reasons that might be true. But that\u2019s not really what I want to talk about here\u200a\u2014\u200atons of thoughtful<\/a> writers<\/a> have<\/a> commented<\/a> on what DeepSeek-R1 is, and what really happened in the training\u00a0process.<\/p>\nWhat I\u2019m more interested in at the moment is how this news shifted some of the momentum in the AI space. Nvidia and other related stocks dropped precipitously when the news of DeepSeek-R1 came out<\/a>, largely (it seems) because it didn\u2019t require the newest GPUs to train, and by training more efficiently, it required less power than an OpenAI model. I had already been thinking about the cultural backlash that Big Generative AI was facing, and something like this opens up even more space for people to be critical of the practices and promises of generative AI companies.<\/p>\nWhere are we in terms of the critical voices against generative AI as a business or as a technology? Where is that coming from, and why might it be occurring?<\/p>\n
Schools of\u00a0Thought<\/h3>\n
The two often overlapping angles of criticism that I think are most interesting are first, the social or community good perspective, and second, the practical perspective. From a social good perspective, critiques of generative AI as a business and an industry are myriad, and I\u2019ve talked a lot about them in my writing here<\/a>. Making generative AI into something ubiquitous comes at extraordinary costs, from the environmental to the economic and\u00a0beyond.<\/p>\nAs a practical matter, it might be simplest to boil it down to \u201cthis technology doesn\u2019t work the way we were promised\u201d. Generative AI lies to us, or \u201challucinates\u201d, and it performs poorly on many of the kinds of tasks that we have most need for technological help on. We are led to believe we can trust this technology, but it fails to meet expectations, while simultaneously being used for such misery-inducing and criminal things as synthetic CSAM and deepfakes to undermine democracy.<\/p>\n
So when we look at these together, you can develop a pretty strong argument: this technology is not living up to the overhyped expectations, and in exchange for this underwhelming performance, we\u2019re giving up electricity, water, climate, money, culture, and jobs. Not a worthwhile trade, in many people\u2019s eyes, to put it\u00a0mildly!<\/p>\n
I do like to bring a little nuance to the space, because I think when we accept the limitations on what generative AI can do, and the harm it can cause, and don\u2019t play the overhype game, we can find a passable middle ground. I don\u2019t think we should be paying the steep price for training and for inference of these models unless the results are really, REALLY worth it. Developing new molecules for medical research? Maybe, yes. Helping kids cheat (poorly) on homework? No thanks. I\u2019m not even sure it\u2019s worth the externality cost to help me write code a little bit more efficiently at work, unless I\u2019m doing something really valuable. We need to be honest and realistic about the true price of both creating and using this technology.<\/p>\n
How we got\u00a0here<\/h3>\n
So, with that said, I\u2019d like to dive in and look at how this situation came to be. I wrote way back in September 2023 that machine learning had a public perception problem, and in the case of generative AI, I think that has been proven out by events. Specifically, if people don\u2019t have realistic expectations and understanding of what LLMs are good for and what they\u2019re not good for, they\u2019re going to bounce off, and backlash will\u00a0ensue.<\/p>\n
\u201cMy argument goes something like\u00a0this:<\/em><\/p><\/blockquote>\n1. People are not naturally prepared to understand and interact with machine learning.<\/em><\/p><\/blockquote>\n2. Without understanding these tools, some people may avoid or distrust\u00a0them.<\/em><\/p><\/blockquote>\n3. Worse, some individuals may misuse these tools due to misinformation, resulting in detrimental outcomes.<\/em><\/p><\/blockquote>\n4. After experiencing the negative consequences of misuse, people might become reluctant to adopt future machine learning tools that could enhance their lives and communities.\u201d<\/em><\/p><\/blockquote>\nme, in Machine Learning\u2019s Public Perception Problem, Sept\u00a02023<\/a>\n<\/p><\/blockquote>\nSo what happened? Well, the generative AI industry dove head first into the problem and we\u2019re seeing the repercussions.<\/p>\n
Generative AI applications don\u2019t meet people\u2019s\u00a0needs<\/h3>\n
Part of the problem is that generative AI really can\u2019t effectively do everything the hype claims<\/a>. An LLM can\u2019t be reliably used to answer questions, because it\u2019s not a \u201cfacts machine\u201d. It\u2019s a \u201cprobable next word in a sentence machine\u201d. But we\u2019re seeing promises of all kinds that ignore these limitations, and tech companies are forcing generative AI features into every kind of software you can think of. People hated Microsoft\u2019s Clippy because it wasn\u2019t any good and they didn\u2019t want to have it shoved down their throats\u200a\u2014\u200aand one might say they\u2019re doing the same basic thing with an improved version, and we can see that some people still understandably resent\u00a0it<\/a>.<\/p>\nWhen someone goes to an LLM today and asks for the price of ingredients in a recipe at their local grocery store right now, there\u2019s absolutely no chance that model can answer that correctly, reliably. That is not within its capabilities, because the true data about those prices is not available to the model. The model might accidentally guess that a bag of carrots is $1.99 at Publix, but it\u2019s just that, an accident. In the future, with chaining models together in agentic forms, there\u2019s a chance we could develop a narrow model to do this kind of thing correctly, but right now it\u2019s absolutely bogus.<\/p>\n
But people are asking LLMs these questions today! And when they get to the store, they\u2019re very disappointed about being lied to by a technology that they thought was a magic answer box. If you\u2019re OpenAI or Anthropic, you might shrug, because if that person was paying you a monthly fee, well, you already got the cash. And if they weren\u2019t, well, you got the user number to tick up one more, and that\u2019s\u00a0growth.<\/p>\n
However, this is actually a major business problem. When your product fails like this, in an obvious, predictable (inevitable!) way, you\u2019re beginning to singe the bridge between that user and your product. It may not burn it all at once, but it\u2019s gradually tearing down the relationship the user has with your product, and you only get so many chances before someone gives up and goes from a user to a critic. In the case of generative AI, it seems to me like you don\u2019t get many chances at all. Plus, failure in one mode can make people mistrust the entire technology in all its forms. Is that user going to trust or believe you in a few years when you\u2019ve hooked up the LLM backend to realtime price APIs and can in fact correctly return grocery store prices? I doubt it. That user might not even let your model help revise emails to coworkers after it failed them on some other\u00a0task.<\/p>\n
From what I can see, tech companies think they can just wear people down, forcing them to accept that generative AI is an inescapable part of all their software now, whether it works or not. Maybe they can, but I think this is a self defeating strategy. Users may trudge along and accept the state of affairs, but they won\u2019t feel positive towards the tech or towards your brand as a result. Begrudging acceptance is not the kind of energy you want your brand to inspire among\u00a0users!<\/p>\n
What Silicon Valley has to do with\u00a0it<\/h3>\n
You might think, well, that\u2019s clear enough \u2014let\u2019s back off on the generative AI features in software, and just apply it to tasks where it can wow the user and works well. They\u2019ll have a good experience, and then as the technology gets better, we\u2019ll add more where it makes sense. And this would be somewhat reasonable thinking (although, as I mentioned before, the externality costs will be extremely high to our world and our communities).<\/p>\n
However, I don\u2019t think the big generative AI players can really do that, and here\u2019s why. Tech leaders have spent a truly exorbitant amount of money on creating and trying to improve this technology\u200a\u2014\u200afrom investing in companies that develop it<\/a>, to building power plants and data centers<\/a>, to lobbying to avoid copyright laws, there are hundreds of billions of dollars sunk into this space already with more soon to\u00a0come.<\/p>\nIn the tech industry, profit expectations are quite different from what you might encounter in other sectors\u200a\u2014\u200aa VC funded software startup has to make back 10\u2013100x what\u2019s invested (depending on stage) to look like a really standout success<\/a>. So investors in tech push companies, explicitly or implicitly, to take bigger swings and bigger risks in order to make higher returns plausible. This starts to develop into what we call a \u201cbubble\u201d\u200a\u2014\u200avaluations become out of alignment with the real economic possibilities, escalating higher and higher with no hope of ever becoming reality.<\/a> As Gerrit De Vynck in the Washington Post noted<\/a>, \u201c\u2026 Wall Street analysts are expecting Big Tech companies to spend around $60 billion a year on developing AI models by 2026, but reap only around $20 billion a year in revenue from AI by that point\u2026 Venture capitalists have also poured billions more into thousands of AI start-ups. The AI boom has helped contribute to the $55.6 billion that venture investors put into U.S. start-ups in the second quarter of 2024, the highest amount in a single quarter in two years, according to venture capital data firm PitchBook.\u201d<\/p>\nSo, given the billions invested, there are serious arguments to be made that the amount invested in developing generative AI to date is impossible to match with returns.<\/a> There just isn\u2019t that much money to be made here, by this technology, certainly not in comparison to the amount that\u2019s been invested. But, companies are certainly going to try. I believe that\u2019s part of the reason why we\u2019re seeing generative AI inserted into all manner of use cases where it might not actually be particularly helpful, effective, or welcomed. In a way, \u201cwe\u2019ve spent all this money on this technology, so we have to find a way sell it\u201d is kind of the framework. Keep in mind, too, that the investments are continuing to be sunk in to try and make the tech work better, but any LLM advancement these days is proving very slow and incremental.<\/p>\nWhere to\u00a0now?<\/h3>\n
Generative AI tools are not proving essential to people\u2019s lives, so the economic calculus is not working to make a product available and convince folks to buy it. So, we\u2019re seeing companies move to the \u201cfeature\u201d model of generative AI, which I theorized could happen in my article from August 2024<\/a>. However, the approach is taking a very heavy hand, as with Microsoft adding generative AI to Office365 and making the features and the accompanying price increase both mandatory. I admit I hadn\u2019t made the connection between the public image problem and the feature vs product model problem until recently\u200a\u2014\u200abut now we can see that they are intertwined. Giving people a feature that has the functionality problems we\u2019re seeing, and then upcharging them for it, is still a real problem for companies. Maybe when something just doesn\u2019t work for a task, it\u2019s neither a product nor a feature? If that turns out to be the case, then investors in generative AI will have a real problem on their hands, so companies are committing to generative AI features, whether they work well or\u00a0not.<\/p>\nI\u2019m going to be watching with great interest to see how things progress in this space. I do not expect any great leaps in generative AI functionality, although depending on how things turn out with DeepSeek, we may see some leaps in efficiency, at least in training. If companies listen to their users\u2019 complaints and pivot, to target generative AI at the applications it\u2019s actually useful for, they may have a better chance of weathering the backlash, for better or for worse. However, that to me seems highly, highly unlikely to be compatible with the desperate profit incentive they\u2019re facing. Along the way, we\u2019ll end up wasting tremendous resources on foolish uses of generative AI, instead of focusing our efforts on advancing the applications of the technology that are really worth the\u00a0trade.<\/p>\n
Read more of my work at www.stephaniekirmer.com<\/a>.<\/p>\nFurther Reading<\/h3>\n
https:\/\/www.bbc.com\/news\/articles\/c5yv5976z9po<\/a><\/p>\nhttps:\/\/www.cnbc.com\/2025\/01\/27\/nvidia-sheds-almost-600-billion-in-market-cap-biggest-drop-ever.html<\/a><\/p>\nhttps:\/\/medium.com\/towards-data-science\/environmental-implications-of-the-ai-boom-279300a24184<\/a><\/p>\nhttps:\/\/hbr.org\/2023\/06\/the-ai-hype-cycle-is-distracting-companies<\/a><\/p>\nhttps:\/\/www.theverge.com\/2025\/1\/16\/24345051\/microsoft-365-personal-family-copilot-office-ai-price-rises<\/a><\/p>\nhttps:\/\/www.reuters.com\/technology\/artificial-intelligence\/openai-talks-investment-round-valuing-it-up-340-billion-wsj-reports-2025-01-30\/<\/a><\/p>\nhttps:\/\/www.cnn.com\/2025\/01\/21\/tech\/openai-oracle-softbank-trump-ai-investment\/index.html<\/a><\/p>\nhttps:\/\/www.washingtonpost.com\/technology\/2024\/07\/24\/ai-bubble-big-tech-stocks-goldman-sachs\/<\/a><\/p>\nhttps:\/\/www.wheresyoured.at\/oai-business\/<\/a><\/p>\nhttps:\/\/medium.com\/towards-data-science\/economics-of-generative-ai-75f550288097<\/a><\/p>\n![\"\"](\"https:\/\/medium.com\/_\/stat?event=post.clientViewed&referrerSource=full_rss&postId=30372d3b9080\")
<\/p>\n
\nThe Cultural Backlash Against Generative AI<\/a> was originally published in Towards Data Science<\/a> on Medium, where people are continuing the conversation by highlighting and responding to this story.<\/p>\n<\/div>\n
The recent reveal of DeepSeek-R1, the large scale LLM developed by a Chinese company (also named DeepSeek), has been a very interesting event for those of us who spend time observing and analyzing the cultural and social phenomena around AI. Evidence suggests that R1 was trained for a fraction of the price that it cost to train ChatGPT<\/a> (any of their recent models, really), and there are a few reasons that might be true. But that\u2019s not really what I want to talk about here\u200a\u2014\u200atons of thoughtful<\/a> writers<\/a> have<\/a> commented<\/a> on what DeepSeek-R1 is, and what really happened in the training\u00a0process.<\/p>\n What I\u2019m more interested in at the moment is how this news shifted some of the momentum in the AI space. Nvidia and other related stocks dropped precipitously when the news of DeepSeek-R1 came out<\/a>, largely (it seems) because it didn\u2019t require the newest GPUs to train, and by training more efficiently, it required less power than an OpenAI model. I had already been thinking about the cultural backlash that Big Generative AI was facing, and something like this opens up even more space for people to be critical of the practices and promises of generative AI companies.<\/p>\n Where are we in terms of the critical voices against generative AI as a business or as a technology? Where is that coming from, and why might it be occurring?<\/p>\n The two often overlapping angles of criticism that I think are most interesting are first, the social or community good perspective, and second, the practical perspective. From a social good perspective, critiques of generative AI as a business and an industry are myriad, and I\u2019ve talked a lot about them in my writing here<\/a>. Making generative AI into something ubiquitous comes at extraordinary costs, from the environmental to the economic and\u00a0beyond.<\/p>\n As a practical matter, it might be simplest to boil it down to \u201cthis technology doesn\u2019t work the way we were promised\u201d. Generative AI lies to us, or \u201challucinates\u201d, and it performs poorly on many of the kinds of tasks that we have most need for technological help on. We are led to believe we can trust this technology, but it fails to meet expectations, while simultaneously being used for such misery-inducing and criminal things as synthetic CSAM and deepfakes to undermine democracy.<\/p>\n So when we look at these together, you can develop a pretty strong argument: this technology is not living up to the overhyped expectations, and in exchange for this underwhelming performance, we\u2019re giving up electricity, water, climate, money, culture, and jobs. Not a worthwhile trade, in many people\u2019s eyes, to put it\u00a0mildly!<\/p>\n I do like to bring a little nuance to the space, because I think when we accept the limitations on what generative AI can do, and the harm it can cause, and don\u2019t play the overhype game, we can find a passable middle ground. I don\u2019t think we should be paying the steep price for training and for inference of these models unless the results are really, REALLY worth it. Developing new molecules for medical research? Maybe, yes. Helping kids cheat (poorly) on homework? No thanks. I\u2019m not even sure it\u2019s worth the externality cost to help me write code a little bit more efficiently at work, unless I\u2019m doing something really valuable. We need to be honest and realistic about the true price of both creating and using this technology.<\/p>\n So, with that said, I\u2019d like to dive in and look at how this situation came to be. I wrote way back in September 2023 that machine learning had a public perception problem, and in the case of generative AI, I think that has been proven out by events. Specifically, if people don\u2019t have realistic expectations and understanding of what LLMs are good for and what they\u2019re not good for, they\u2019re going to bounce off, and backlash will\u00a0ensue.<\/p>\n \u201cMy argument goes something like\u00a0this:<\/em><\/p><\/blockquote>\n 1. People are not naturally prepared to understand and interact with machine learning.<\/em><\/p><\/blockquote>\n 2. Without understanding these tools, some people may avoid or distrust\u00a0them.<\/em><\/p><\/blockquote>\n 3. Worse, some individuals may misuse these tools due to misinformation, resulting in detrimental outcomes.<\/em><\/p><\/blockquote>\n 4. After experiencing the negative consequences of misuse, people might become reluctant to adopt future machine learning tools that could enhance their lives and communities.\u201d<\/em><\/p><\/blockquote>\n me, in Machine Learning\u2019s Public Perception Problem, Sept\u00a02023<\/a>\n<\/p><\/blockquote>\n So what happened? Well, the generative AI industry dove head first into the problem and we\u2019re seeing the repercussions.<\/p>\n Part of the problem is that generative AI really can\u2019t effectively do everything the hype claims<\/a>. An LLM can\u2019t be reliably used to answer questions, because it\u2019s not a \u201cfacts machine\u201d. It\u2019s a \u201cprobable next word in a sentence machine\u201d. But we\u2019re seeing promises of all kinds that ignore these limitations, and tech companies are forcing generative AI features into every kind of software you can think of. People hated Microsoft\u2019s Clippy because it wasn\u2019t any good and they didn\u2019t want to have it shoved down their throats\u200a\u2014\u200aand one might say they\u2019re doing the same basic thing with an improved version, and we can see that some people still understandably resent\u00a0it<\/a>.<\/p>\n When someone goes to an LLM today and asks for the price of ingredients in a recipe at their local grocery store right now, there\u2019s absolutely no chance that model can answer that correctly, reliably. That is not within its capabilities, because the true data about those prices is not available to the model. The model might accidentally guess that a bag of carrots is $1.99 at Publix, but it\u2019s just that, an accident. In the future, with chaining models together in agentic forms, there\u2019s a chance we could develop a narrow model to do this kind of thing correctly, but right now it\u2019s absolutely bogus.<\/p>\n But people are asking LLMs these questions today! And when they get to the store, they\u2019re very disappointed about being lied to by a technology that they thought was a magic answer box. If you\u2019re OpenAI or Anthropic, you might shrug, because if that person was paying you a monthly fee, well, you already got the cash. And if they weren\u2019t, well, you got the user number to tick up one more, and that\u2019s\u00a0growth.<\/p>\n However, this is actually a major business problem. When your product fails like this, in an obvious, predictable (inevitable!) way, you\u2019re beginning to singe the bridge between that user and your product. It may not burn it all at once, but it\u2019s gradually tearing down the relationship the user has with your product, and you only get so many chances before someone gives up and goes from a user to a critic. In the case of generative AI, it seems to me like you don\u2019t get many chances at all. Plus, failure in one mode can make people mistrust the entire technology in all its forms. Is that user going to trust or believe you in a few years when you\u2019ve hooked up the LLM backend to realtime price APIs and can in fact correctly return grocery store prices? I doubt it. That user might not even let your model help revise emails to coworkers after it failed them on some other\u00a0task.<\/p>\n From what I can see, tech companies think they can just wear people down, forcing them to accept that generative AI is an inescapable part of all their software now, whether it works or not. Maybe they can, but I think this is a self defeating strategy. Users may trudge along and accept the state of affairs, but they won\u2019t feel positive towards the tech or towards your brand as a result. Begrudging acceptance is not the kind of energy you want your brand to inspire among\u00a0users!<\/p>\n You might think, well, that\u2019s clear enough \u2014let\u2019s back off on the generative AI features in software, and just apply it to tasks where it can wow the user and works well. They\u2019ll have a good experience, and then as the technology gets better, we\u2019ll add more where it makes sense. And this would be somewhat reasonable thinking (although, as I mentioned before, the externality costs will be extremely high to our world and our communities).<\/p>\n However, I don\u2019t think the big generative AI players can really do that, and here\u2019s why. Tech leaders have spent a truly exorbitant amount of money on creating and trying to improve this technology\u200a\u2014\u200afrom investing in companies that develop it<\/a>, to building power plants and data centers<\/a>, to lobbying to avoid copyright laws, there are hundreds of billions of dollars sunk into this space already with more soon to\u00a0come.<\/p>\n In the tech industry, profit expectations are quite different from what you might encounter in other sectors\u200a\u2014\u200aa VC funded software startup has to make back 10\u2013100x what\u2019s invested (depending on stage) to look like a really standout success<\/a>. So investors in tech push companies, explicitly or implicitly, to take bigger swings and bigger risks in order to make higher returns plausible. This starts to develop into what we call a \u201cbubble\u201d\u200a\u2014\u200avaluations become out of alignment with the real economic possibilities, escalating higher and higher with no hope of ever becoming reality.<\/a> As Gerrit De Vynck in the Washington Post noted<\/a>, \u201c\u2026 Wall Street analysts are expecting Big Tech companies to spend around $60 billion a year on developing AI models by 2026, but reap only around $20 billion a year in revenue from AI by that point\u2026 Venture capitalists have also poured billions more into thousands of AI start-ups. The AI boom has helped contribute to the $55.6 billion that venture investors put into U.S. start-ups in the second quarter of 2024, the highest amount in a single quarter in two years, according to venture capital data firm PitchBook.\u201d<\/p>\n So, given the billions invested, there are serious arguments to be made that the amount invested in developing generative AI to date is impossible to match with returns.<\/a> There just isn\u2019t that much money to be made here, by this technology, certainly not in comparison to the amount that\u2019s been invested. But, companies are certainly going to try. I believe that\u2019s part of the reason why we\u2019re seeing generative AI inserted into all manner of use cases where it might not actually be particularly helpful, effective, or welcomed. In a way, \u201cwe\u2019ve spent all this money on this technology, so we have to find a way sell it\u201d is kind of the framework. Keep in mind, too, that the investments are continuing to be sunk in to try and make the tech work better, but any LLM advancement these days is proving very slow and incremental.<\/p>\n Generative AI tools are not proving essential to people\u2019s lives, so the economic calculus is not working to make a product available and convince folks to buy it. So, we\u2019re seeing companies move to the \u201cfeature\u201d model of generative AI, which I theorized could happen in my article from August 2024<\/a>. However, the approach is taking a very heavy hand, as with Microsoft adding generative AI to Office365 and making the features and the accompanying price increase both mandatory. I admit I hadn\u2019t made the connection between the public image problem and the feature vs product model problem until recently\u200a\u2014\u200abut now we can see that they are intertwined. Giving people a feature that has the functionality problems we\u2019re seeing, and then upcharging them for it, is still a real problem for companies. Maybe when something just doesn\u2019t work for a task, it\u2019s neither a product nor a feature? If that turns out to be the case, then investors in generative AI will have a real problem on their hands, so companies are committing to generative AI features, whether they work well or\u00a0not.<\/p>\n I\u2019m going to be watching with great interest to see how things progress in this space. I do not expect any great leaps in generative AI functionality, although depending on how things turn out with DeepSeek, we may see some leaps in efficiency, at least in training. If companies listen to their users\u2019 complaints and pivot, to target generative AI at the applications it\u2019s actually useful for, they may have a better chance of weathering the backlash, for better or for worse. However, that to me seems highly, highly unlikely to be compatible with the desperate profit incentive they\u2019re facing. Along the way, we\u2019ll end up wasting tremendous resources on foolish uses of generative AI, instead of focusing our efforts on advancing the applications of the technology that are really worth the\u00a0trade.<\/p>\n Read more of my work at www.stephaniekirmer.com<\/a>.<\/p>\n https:\/\/www.bbc.com\/news\/articles\/c5yv5976z9po<\/a><\/p>\n https:\/\/www.cnbc.com\/2025\/01\/27\/nvidia-sheds-almost-600-billion-in-market-cap-biggest-drop-ever.html<\/a><\/p>\n https:\/\/medium.com\/towards-data-science\/environmental-implications-of-the-ai-boom-279300a24184<\/a><\/p>\n https:\/\/hbr.org\/2023\/06\/the-ai-hype-cycle-is-distracting-companies<\/a><\/p>\n https:\/\/www.theverge.com\/2025\/1\/16\/24345051\/microsoft-365-personal-family-copilot-office-ai-price-rises<\/a><\/p>\n https:\/\/www.reuters.com\/technology\/artificial-intelligence\/openai-talks-investment-round-valuing-it-up-340-billion-wsj-reports-2025-01-30\/<\/a><\/p>\n https:\/\/www.cnn.com\/2025\/01\/21\/tech\/openai-oracle-softbank-trump-ai-investment\/index.html<\/a><\/p>\n https:\/\/www.washingtonpost.com\/technology\/2024\/07\/24\/ai-bubble-big-tech-stocks-goldman-sachs\/<\/a><\/p>\n https:\/\/www.wheresyoured.at\/oai-business\/<\/a><\/p>\n https:\/\/medium.com\/towards-data-science\/economics-of-generative-ai-75f550288097<\/a><\/p>\n The Cultural Backlash Against Generative AI<\/a> was originally published in Towards Data Science<\/a> on Medium, where people are continuing the conversation by highlighting and responding to this story.<\/p>\n<\/div>\nSchools of\u00a0Thought<\/h3>\n
How we got\u00a0here<\/h3>\n
Generative AI applications don\u2019t meet people\u2019s\u00a0needs<\/h3>\n
What Silicon Valley has to do with\u00a0it<\/h3>\n
Where to\u00a0now?<\/h3>\n
Further Reading<\/h3>\n
<\/p>\n
\n