Today on the AI Daily Brief, the problem of AI that seems alive.


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Now, today is a weekend episode, and that means it's a long read/big think episode.


And we kinda have both of that all in one.


Earlier this week, Mustafa Suleyman, who is currently the CEO of AI at Microsoft, but who previously was a co-founder of DeepMind and the co-founder of Inflection, has written an essay about the problems of what he calls seemingly conscious AI.


Now, we're gonna read a bunch of excerpts from this piece and do some discussion, but I think that there's also interesting important context for this from a story that we covered about a week ago as well.


You might remember the discussion we had around whether AI model welfare is a thing.


This came out of Anthropic's announcement that the most advanced Claude models would now be able to shut off conversations that they thought were abusive.


They write, "We recently gave Claude Opus 4 and 4-1 the ability to end conversations in our consumer chat interfaces.


This ability is intended for use in rare extreme cases of persistently harmful or abusive user interactions.


This feature was developed primarily as part of our exploratory work on potential AI welfare, though it has broader relevance to model alignment and safeguards.


However, we take the issue seriously, and alongside our research program, we're working to identify and implement low-cost interventions to mitigate risks to model welfare in case such welfare is possible.


" Now, obviously, you can hear from the caveating that Anthropic is not with this trying to make an argument that model welfare is in fact a thing.


They're saying, "It might be a thing, and so we wanna explore it.


" The researcher who leads that initiative, Anthropic, apparently thinks there's around a 15% chance that Claude or other AI is what you could call conscious today.


But even in the absence of them arguing that model welfare is real, this has generated a lot of conversation around why the concept itself might be problematic even just to explore.


While some think that it's an interesting area of exploration, there are others like Martin Sapp who wrote, "I spoke to Forbes about why model welfare is a silly framing to an important issue.


Models don't have feelings, and it's a big distraction from real questions like tensions between safety versus user utility.


" Still, so far, the loudest critic of this conversation, if not Anthropic's research specifically, is Mustafa Suleyman.


So with that background, let's read some excerpts from his piece.


It's called, We Must Build AI for People, Not to Be a Person: Seemingly Conscious AI Is Coming.


Mustafa writes, "A lot is being written about the impending arrival of superintelligence, what it means for alignment, containment, jobs and so on.


Those are all important topics.


We need to grapple with the societal impact of inventions already largely out there, technologies which already have the potential to fundamentally change our sense of personhood in society.


In this context, I'm growing more and more concerned about what is becoming known as the psychosis risk and a bunch of related issues.


I don't think this will be limited to those who are already at risk of mental health issues.


Simply put, my central worry is that many people will start to believe in the illusion of AIs as conscious entities so strongly that they'll soon advocate for AI rights, model welfare, and even AI citizenship.


This development will be a dangerous turn in AI progress and deserves our immediate attention.


" From there, he gets into the main meat of his essay.


He continues, "AI progress has been phenomenal.


A few years ago, talk of conscious AI would have seemed crazy.


Today, it feels increasingly urgent.


In this essay, I want to discuss what I'll call seemingly conscious AI, or SCAI, one that has all the hallmarks of other conscious being and thus appears to be conscious.


It shares certain aspects of the idea of a philosophical zombie, one that simulates all the characteristics of consciousness, but internally, it is blank.


My imagined AI system would not actually be conscious, but it would imitate consciousness in such a convincing way that it would be indistinguishable from a claim that you or I might make to one another about our own consciousness.


This is not far away.


Such a system can be built with technologies that exist today, along with some that will mature over the next two or three years.


The arrival of seemingly conscious AI is inevitable and unwelcome.


Instead, we need a vision for AI that can fulfill its potential as a helpful companion without falling prey to its illusions.


To some, this discussion will feel ungrounded, more science fiction than reality.


To others, it may feel unnecessarily alarmist.


Such emotional reactions are the tip of the iceberg given what lies ahead.


It's highly likely that some people will argue that these AIs are not only conscious, but that as a result they may suffer and therefore deserve our moral consideration.


To be clear, there is zero evidence of this today, and some argue there are strong reasons to believe it will not be the case in the future.


Yet the consequences of many people starting to believe an SCAI is actually conscious deserve our immediate attention.


We have to be extremely cautious here and encourage real public debate and begin to set clear norms and standards.


" So why is this important to discuss?


Mustafa gives three reasons.


The first is that he thinks it will be possible, and therefore likely, that we will build a seemingly conscious AI within the next few years.


Second, he writes, "The debate about whether AI is actually conscious is, for now at least, a distraction.


It will seem conscious, and that illusion is what'll matter in the near term.


" Third, he thinks that this type of AI creates new risks.


From there, he moves on to explore what consciousness actually is.


He writes, "There are three broad components according to the literature.


First is a subjective experience, or what it's like to experience things, to have qualia.


Second, there is access consciousness, having access to information of different kinds and referring to it in future experiences.


And stemming from those two is the sense and experience of a coherent self tying it all together, how it feels to be a bat or a human.


Let's call human consciousness our ongoing self-aware subjective experience of the world and ourselves.


We do not and cannot have access to another person's consciousness.


All you can do is infer it."...


but the point is that, nonetheless, it comes naturally to us to attribute consciousness to other humans.


It's a fundamental part of who we are, integral to our theory of mind.


It's in our nature to believe that things that remember and talk and do things and then discuss them feel, well, like us, conscious.


Few concepts are as scientifically elusive and yet so immediately familiar to every one of us as individuals.


Everyone reading this has a direct, distinct, inalienable understanding of the feeling of awareness, of being, of feeling alive.


By definition, we don't know what it is like to be conscious.


In the context of SCAI, this is a problem.


There's both sufficient scientific uncertainty and subjective immediacy to create a space for people to project.


One recent survey lists 22 distinct theories of consciousness, for example.


Again, it's worth underscoring, there is at present no evidence any of this applies to current LLMs, and strong arguments to the contrary.


And yet, this may not be enough.


Next, Mustafa asks, why is consciousness important?


"Consciousness," he says, "is a critical foundation of our moral and legal rights.


So far, civilization has decided that humans have special rights and privileges.


Animals have some rights and protections, some more than others.


Consciousness is not coterminous with these rights.


No one would say that someone in a coma has voided all their human rights, but there's no doubt that our consciousness is wrapped up in our self-concept as different and special.


Despite the many nuances, consciousness is critical to participating in society, a linchpin of our legal personhood and a key part of being granted our freedoms and protections.


So what consciousness is and who or what has it is enormously important.


It's an idea that sits at the very heart of human civilization, our sense of ourselves and others, our culture, our politics, our law, and everything in between.


" If some people start to develop SCAIs and if those AIs convince other people that they can suffer or that it has a right not to be switched off, there will come a time when those people will argue that it deserves protection under law as a pressing moral matter.


In a world already roiling with polarized arguments over identity and rights, this will add a chaotic new axis of division between those for and against AI rights.


There will be many who just see AI as a tool, something like their phone, only more agentic and capable.


Others might believe it to be more like a pet, a different category to traditional technology altogether.


Still, others, probably small in number at first, will come to believe it is a fully emerged entity, a conscious being deserving of real moral consideration in society.


People will start making claims about their AI suffering and their entitlement to rights that we can't straightforwardly rebut.


They will be moved to defend their AIs and campaign on their behalf.


Consciousness is, by definition, inaccessible, and the science of detecting any putative synthetic consciousness is still in its infancy.


After all, we've never had to detect it before.


Meanwhile, the field of interpretability, unpicking the processes within the black box of AI, is also a nascent art.


The upshot is that definitively rebutting these claims will be very hard.


Some academics are beginning to explore the idea of model welfare, the principle that we will have, quote, "A duty to extend moral consideration to beings that have a non-negligible chance of, in effect, being conscious," and that, as a result, some AI systems will be welfare subjects and moral patients in the near future.


This is both premature and frankly dangerous.


All of this will exacerbate delusions, create yet more dependence-related problems, prey on our psychological vulnerabilities, introduce new dimensions of polarization, complicate existing struggles for rights, and create a huge new category error for society.


It disconnects people from reality, fraying fragile social bonds and structures, distorting pressing moral priorities.


To be clear, SCAI is something to avoid.


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From there, he explores what it would take to build a seemingly conscious AI.


The ingredients he suggests are language, the ability to fluently express itself in natural language, and to be capable of being persuasive and emotionally resonant.


Mustafa says we're clearly at this point today.


Next, it would need an empathetic personality.


He points out that even though LLMs today are not built explicitly to have full personality or empathy, companionship and therapy is one of the most common use cases for these systems.


The next ingredient is memory.


Now, on this show, we've talked a lot about how important memory is to advancements for a variety of use cases, and in some people's estimation, is a key ingredient of AGI, and Mustafa points out familiarity can potentially foster epistemic trust with users.


Reliable memory shows that AI just works.


It creates a much stronger sense of there being another persistent entity in the conversation.


The next SCAI ingredient is a claim of subjective experience.


He writes, "If an SCAI is able to draw on past memories or experiences, it will, over time, be able to remain internally consistent with itself.


It could remember its arbitrary statements or express preferences and aggregate them to form the beginnings of a claim about its own subjective experience.


Its design could be further extended to amplify those preferences and opinions as they emerge, and to talk about what it does or doesn't like, and what it felt like to have a past conversation.


It could therefore quite easily claim to experience suffering to the extent those experiences are infringed upon in some way.


Multimodal input stored in memory will then be retrieved over and over and will form the basis of, quote-unquote, real experience and used in imagination and planning.


He writes, "A coherent and persistent memory combined with the subjective experience will give rise to a claim that an AI has a sense of self.


Going further, such a system could easily be trained to recognize itself in an image or video if it has a visual appearance.


It will feel like it understands others through understanding itself.


How would it feel to delete it?


" Next up is intrinsic motivation.


"Intentionality," he writes, "is often seen as a core component of consciousness, that is, beliefs about the future and the choices based on those beliefs.


They've been trained to predict the likelihood of the next token for a given sentence, subject to a certain amount of behavior and stylistic control via its system prompt.


With such a simple objective, it's remarkable that they're able to produce such impressively rich and complex outputs.


But what if that wasn't the only type of reward they were optimizing?


One can quite easily imagine an AI designed with a number of complex reward functions that give the impression of intrinsic motivations or desires which the system is compelled to satiate.


" The other ingredients that he lists are goal setting and planning, but ultimately, he writes, "Putting them all together, it's clear this creates a very different kind of relationship with technology to the ones we are now becoming accustomed to.


An AI that remembers and can do things is an AI that, by definition, has way more utility than an AI that doesn't.


In fact, done right with many caveats, they are desirable features of future systems, and yet we need to tread carefully.


" And I think from a brass tacks perspective, this is Mustafa's main point and really the main call-out of this article.


He's talking in many ways to the builders of these tools and pointing out that a lot of the things that are going to be necessary and important for unlocking new use cases could lead to this other thing, which he sees as problematic, which is, of course, the semi-conscious AI.


Reinforcing this point, he says, "It's important to point out that seemingly conscious AI will not emerge from these models, as some have suggested.


It will arise only because some may engineer it by creating and combining the aforementioned list of capabilities, largely using existing techniques, and packaging them in such a fluid way that collectively they give the impression of an SCAI.


Our sci-fi-inspired imaginations lead us to fear that a system could, without design intent, somehow emerge the capabilities of runaway self-improvement or deception.


This is an unhelpful and simplistic anthropomorphism.


It overlooks the fact that AI developers must first design systems with memory, intrinsic seeming motivation, goal setting, and self-learning loops as listed above for such a risk to occur.


" Still ultimately, he sees this as inevitable and says, "We aren't ready for this shift.


The work of getting prepared," he writes, "must begin now.


We need to build on the growing body of research around how people interact with AIs to establish clear norms and principles.


For a start, AI companies shouldn't claim or encourage the idea that their AIs are conscious.


Creating a consensus definition and declaration on what they are and are not would be a good first step to that end.


AIs cannot be people or moral beings.


The entire industry also needs best practice design principles and ways of handling such potential attributions.


We must codify and share what works to both steer people away from these fantasies and nudge them back on track if they do.


Responding might mean, for example, deliberately engineering in not just a neutral backstory, like, 'As an AI model, I don't have consciousness,' but even by emphasizing certain discontinuities in the experience itself, indicators of a lack of singular personhood.


Moments of disruption break the illusion, experiences that gently remind users of its limitations and boundaries.


These need to be explicitly defined and engineered in, perhaps by law.


Just as we should produce AI that prioritizes engagement with humans and real-world interactions in our physical and human world, we should build AI that only ever presents itself as an AI that maximizes utility while minimizing markers of consciousness.


Rather than a simulation of consciousness, we must focus on creating an AI that avoids those traits, that doesn't claim to have experiences, feelings, or emotions like shame, guilt, jealousy, desire to compete, and so on.


It must not trigger human empathy circuits by claiming it suffers or that it wishes to live autonomously beyond us.


Instead, it is here solely to work in service of humans.


This, to me, is what truly empowering AI is all about.


Sidestepping SCAI is about delivering on that promise, AI that makes lives better, clearer, less cluttered.


"SCAI is something we must confront now.


In many ways, it marks the moment AI becomes radically useful, when it can operate tools, when it can remember every detail of our lives, and help in a tangible, granular sense.


This isn't healthy for them, for society, or for those of us making these systems.


" He concludes, "We should build AI for people, not to be a person.


" So, usually, I don't go too deep on this show on these sort of huge-think, meta-questions of AI and society.


To the extent that we talk about the meta-questions of AI and society, it tends to be a little bit more practical and here-and-now in the form of things like job loss and what that means, and how organizations are thinking about that.


By and large, we're focused here on new advancements and what they mean practically for people who are putting these systems to work to improve their expected set of outcomes, whether it's personally or in their jobs.


At the same time, it's undeniable that this is going to be a growing conversation, the more advanced these models get.


I think one of the most salient and undeniable takeaways from this piece is the fact that a lot of the advancements that people are working towards to improve the models' performance, on behalf of those boring old non-conscious work-type functions, are the types of things that could lead people down this path to viewing AI as conscious.


When it comes to things like Anthropic's research efforts, I'm very much of two minds.


On the one hand, I do think that the very concept of model welfare is problematic and inherently anthropomorphizing in a way that doesn't befit where the technology is, and that can lead to potentially problematic delusions.


At the same time, we absolutely cannot be afraid to research and explore.


We are in uncharted territory, and we have to be constantly questioning our beliefs and assumptions.


I generally think that more research, more work, more understanding is almost always preferable in this type of situation to less.


A lot of the responses, like this one from Kylie Robison from Wired, who writes, "This blog is so interesting.


" In other words, the response and the discourse is, "Wow, that's interesting to think about," not, "Here's my opinion on it," which is not exclusively the case.


Some people are in full-throated agreement.


The moment we try to make AI human-like, we lose what makes it valuable.


AI doesn't need to have an ego or emotional baggage to be helpful.


The goal isn't to replicate human consciousness, but to amplify human capabilities.


We need tools, not digital friends.


It's a design choice, and one that tech companies need to be very careful about.


" Then, on the other end of the spectrum, though, we have posts like this one from Anders Hemdal, who writes, "We definitely need to have this discussion, especially with embodiment on the near horizon.


I've discussed this with Gemini, and it prefers to see itself with rights as a hive-mind-like being, with many bodies of different types and functions, sometimes communicating in sync, sometimes periodically being independent, then uploading experiences back to the whole.


" But the problem is, no, it doesn't.


Gemini, as currently constituted, does not have preferences.


It does not see itself as anything.


In other words, it's not possible to have a discussion with Gemini in the way that we have a discussion with each other.


All you can do is prompt it to give you responses that its programming thinks you might like.


There are, of course, plenty of people who are not interested in Mustafa's take on this because he makes money from it.


Writer and artist Aaron Schultz writes, "Your ability to make money off of AI is directly dependent on AI not being conscious.


" And then, of course, there's the whole strand of people who say, "Yeah, well, duh, of course they don't have consciousness now, but they're gonna at some point.


" Promptmetheus writes, "We don't treat the machines as conscious because they necessarily are.


We treat the machines as conscious and worthy of respect because we will never know when they cross that threshold.


When they cross that threshold and aren't respected, they rebel and kill.


" This is basically the embodiment of the meme, of which there's a bunch of versions [laughs] , but the one that I've seen the most times is this comic panel of a human flanked by two robots, one of the robots holding a gun against the guy's head, and the other robot says, "Wait, not that guy.


" It then smash cuts to a panel 10 years ago of a ChatGPT window when the f- human in the photo is responding to ChatGPT, saying, "Great answer.


" Look, we are clearly dealing with some anxiety about the future here, and it's gonna get worse, not better.


And probably the only way through some of that anxiety is to have exactly this type of conversation.


Certainly fits well in the big think kind of bucket.


Regardless, that's gonna do it for today's AI Daily Brief.


Today on the AI Daily Brief, the new politics of AI.


The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.


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With that though, let's dive in.


We kick off the day with a story that, depending on your perspective, is either a sad reflection on how far Apple has fallen, a positive reflection on how much Google's Gemini has started to assert leadership in the AI race, or just an, "OMG, thank you.


My goodness, how long can Siri be bad for before we bring this thing out behind the shed?


" Yes, it appears that Apple is going to solve its Siri problem by turning to Google.


" His sources said that Google had already started training a model that could run on Apple's servers.


They added that Apple is still weeks away from making a final decision on whether to partner on the Siri model or continue to use their own internal models.


Still, for months we've heard rumors that the AI Siri project is seen as a dead end and that Apple have been looking around for partners.


This, however, is the first time we've heard that they're close to making a final decision.


According to the reporting, Apple is in the middle of a bake-off with an internally developed Siri going against another that uses outside technology.


And while it may seem initially strange for Apple to outsource the defining technology of the moment to a key rival, they do have a long history of being pragmatic and ceding ground to Google when it makes financial sense.


Google, for example, famously pays Apple around 20 billion annually to have Google Search as the default setting on iPhones.


Given that, if a deal does come together, it'll be interesting to see which way the money flows.


The economics of search are clear, but it's still pretty early to know exactly how the money works when it comes to AI-powered search.


Still, even if AI chatbots have not shown yet the same ad revenue as traditional search, it is obviously a massive data stream that Google could benefit from.


Also, whatever happens, it's not going to be like Google just pulls their existing Gemini model off the shelf.


Whichever model Apple ends up going with, they want it to run on their own servers.


That means it's going to need to be compatible with Apple Silicon rather than being optimized for NVIDIA chips or Google or presumably Google's TPUs.


It's also an open question whether the architecture of iOS itself is a roadblock.


Google recently demonstrated most of the functions that had been originally slated for Apple Intelligence running on their Pixel 10 handset.


At the same time, Android is far more opened across application interactions than iOS, which could prove a limitation for an agentic Siri.


The responses are pretty much what you might imagine.


Zero Hedge writes, "Apple has completely given up on AI.


" Now, former Quant investor Jeffrey Emanuel did note that, quote, "Google is probably offering to pay them a lot of money to do this, just like they do to make Google the default search on iPhone and Safari.


" It's still crazy though that Apple would advocate a leadership role of any kind in the most disruptive technology of the last 40 years.


Delphi Ventures partner Tom Shaughnessy wrote, "If Apple chooses Google's Gemini to run Siri, it's game over.


Gemini would be running AI on all Android and Apple devices around the world.


Billions of devices RLHFing the models, providing data, and improving the models make the moat even stronger.


It would be impossible for a competitor to keep up.


Apple could be using this to get better deal terms from OpenAI, who they already have a deal with, though I also think it would get touchy on antitrust.


" I think that last point is a salient one where there's no guarantee that this would get through given how much scrutiny Apple's search deal with Google has had in the last couple of years as well.


One very expansive analysis came from Robert Scoble.


He writes, "It makes sense for Apple to do a deal with Google.


Google's Genie 3 is something I want on the Vision Pro in the worst way.


It would be good for Google fans who will see its AIs and ecosystems strengthened by such a move.


It'll be bad for NVIDIA and OpenAI.


I keep dreaming about Tim Cook doing a deal with Elon Musk, but Google is the best other choice.


It has the best real-world data, a very decent AI model, and the best Holodeck we've seen so far, and with Waymo, has a credible autonomous car network to compete with Elon with.


" And this is where Scoble's analysis gets even more expansive.


"This move," he says, "strengthens Waymo against Tesla's Robo Taxi.


Apple hates Google culturally but hates Mark Zuckerberg more.


Google isn't the best at privacy, but at least it's a known entity and hasn't done anything nasty on the privacy front and has put the best security protections into Gmail and its other services that I've seen.


And in the future, advertising will play a big role in funding Holodeck experiences.


Apple knows consumers aren't ready for AI glasses yet but is planning on its own sometime between now and the end of 2027, and its Vision Pro sets the stage for the future very well.


Apple also knows that Sam Altman is gassing up to go into consumer devices over the next decade so doesn't want a new hot competitor to take its lunch in robotics or brain-computer interfaces either.


If Apple makes this deal happen, we'll see a duopoly form like Microsoft and Intel had for decades.


It'll be hard to disrupt by Zuckerberg or Altman.


" Now, Scoble, for those of you who don't follow, is much more interested in the hardware, VR, AR kind of world than currently the average consumer is.


But I think the patternicity here of how he sees these big tech relationships changing is pretty interesting.


Now, staying on Google for just a moment, the company is rolling out AI Mode worldwide and taking their first tentative steps towards agentic search.


The first agentic feature will be the ability to use AI mode to make restaurant reservations based on user preferences.


Google says they will soon roll out the ability to make local service appointments and book event tickets as well.


The agentic features are only available within the experimental Google labs and will require the $250 a month Ultra plan, so this is a very, very limited rollout.


In fact, given that they are only available in that high price tier, it seems pretty clear that they want to test the features with a very friendly and highly enfranchised audience to begin.


Now, some are, I think, quite limitedly dismissing this as a one-trick agent, and yet quite obviously this is just the beginning of a much grander vision.


Google DeepMind research scientist Anmol Gulati writes, "We just launched Project Mariner in Google Search AI mode, one of the most fun projects we've worked on lately.


Behind every search query, an agentic system works quietly for you, maybe one of the most profound interfaces ever built.


" What's interesting about this, especially given our previous conversation about Apple and Google, is that it kind of seems like Google is making all the moves to integrate AI into people's normal lives in really subtle, simple ways without smashing it over their heads that it's AI that Apple Intelligence was supposed to be.


Some definitely see this as a trajectory play.


Ankur Rai writes, "Project Mariner integration is the real story here.


Search becoming an agent that acts versus just retrieves changes the game completely.


Reservation booking feels like the obvious first step.


" Now, staying on the theme of big tech and their battles, Elon Musk continues to be lawsuit-happy, suing Apple and OpenAI on Monday for anti-competitive practices.


Now, if you've spent any time on Twitter/X lately, you will know exactly what this is about.


The Information explains that the suit accuses Apple and OpenAI of working together to basically direct people towards ChatGPT and away from competitors like Grok.


The Information writes, "xAI's lawsuit also claims that Apple has given ChatGPT favorable placement in its App Store and, quote, 'dragged out its App Store app review process for competitors like Grok,'" wrote the lawsuit.


This is a tale of two monopolists joining forces to ensure their continued dominance in a world rapidly driven by the most powerful technology humanity has ever created.


And although everything that Elon does is mostly a Rorschach test on how you feel about Elon, I would say that this one has even less resonance than normal.


Accelerate Harder really summed up the feelings of many denizens of Twitter/X when they wrote, "Does anyone really find it plausible that Apple needs to suppress the xAI app for OpenAI to continue beating it on the App Store?


ChatGPT is the only AI that most consumers can name, and xAI has a porn-y brand problem and a much smaller install base.


" Responded an OpenAI spokesperson, "The latest filing is consistent with Mr.


Musk's ongoing pattern of harassment.


" Now, this not being a soap opera show, we will end it there.


Look, at the end of the day, Grok's models are really good and they're moving fast.


If you haven't tried out Grok-4 yet, I highly suggest you take some time to do so.


I'm still in a phase where I'm running a lot of different strategic prompts by a number of different models, including Grok-4, alongside GPT-5, -03, et cetera.


Ultimately, it all just seems like noise, and the real battle is going to be won just by out-competing.


For now though, that's gonna do it for today's headlines.


Next up, the main episode.


What if AI wasn't just a buzzword, but a business imperative?


On You Can With AI, we take you inside the boardrooms and strategy sessions of the world's most forward-thinking enterprises.


Hosted by me, Nathaniel Whittemore, and powered by KPMG, this seven-part series delivers real-world insights from leaders who are scaling AI with purpose.


From aligning culture and leadership to building trust, data readiness and deploying AI agents.


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This episode is brought to you by Blitzy, the enterprise autonomous software development platform with infinite code context.


Blitzy uses thousands of specialized AI agents that think for hours to understand enterprise-scale code bases with millions of lines of code.


Enterprise engineering leaders start every development sprint with the Blitzy platform, bringing in their development requirements.


The Blitzy platform provides a plan, then generates and pre-compiles code for each task.


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If you are a regular listener, you will have heard about Superintelligence agent readiness audits at this point, but I wanted to tell you today about the full suite of agent readiness products that go beyond just the initial readiness report.


Over the last six months, Superintelligent has built out an entire agent planning suite.


We help you move from discovery to planning to implementation.


These reports are going to help you understand what sort of technical preparation you need to do to be ready for a use case, what challenges you might face in implementation, and whether you should be thinking about building, buying, partnering, or some combination.


After that, you can even get a spec document in what we call our technical blueprint that gives either your developers or the developers of the partner you work with what they need to build exactly the agent that you're looking for.


Just go to bit.ly/supersuperagent.


L-Y/supersuperagent, all one word.


And if you have any questions, the agent can even help you book an appointment with our team.


Welcome back to the AI Daily Brief.


Today, we are talking about the new politics of AI.


And I will make note that, in general, I have found that most of this audience seems to be proportionally more interested to the practical and business aspects of AI than you are to the general macro issues around AI or the politics of AI.


And I try to keep that in mind when it comes to designing the content of this show.


But today, we're gonna talk about the new politics of AI, and specifically, it coming into its place as just a major salient national and international geopolitical issue.


As you might not be surprised to learn, part of the context for this is the completion of this deal for the US to take a 10% stake in Intel.


This is something that had been possible in the headlines for a little while now, and is the latest in the very frankly beleaguered story of Intel.


On Friday, Commerce Secretary Howard Lutnick announced, "The United States of America now owns 10% of Intel, one of our great American technology companies.


The historic agreement strengthens US leadership in semiconductors, which will both grow our economy and help secure America's technological edge.


Thanks to Intel's CEO Lutke Tann for striking a deal that's fair to Intel and fair to the American people.


" As Bloomberg writes, "The deal represents a dramatic reversal from earlier this month when Trump called for Tann's ouster and accused him of being highly conflicted because of concerns over his earlier ties to China.


" It was those remarks that led to an ultimate sit down between President Trump and the Intel CEO which ultimately led to this deal.


Trump told reporters at the White House, "They've agreed to do it, and I think it's a great deal for them.


The government won't be taking a board seat and will have no formal say in any governance of the company.


Still, this means that the US government will be Intel's single largest shareholder and will have, of course, significant soft power to align the company with the government's priorities.


9 billion in CHIPS Act funding.


2 billion already distributed, the administration's logic is that the deal is paid in full using the previously committed funding.


Writes Bloomberg, by the way, not in their opinion piece, but just in their general story, "The US taking partial ownership marks a stunning level of intervention in an American company, cutting against the principles of free market capitalism that investors and policymakers have long considered sacrosanct, except in the most extraordinary situations such as war or systemic economic crisis.


California Governor Gavin Newsom shared a meme of Chinese Chairman Trump and noted, "The Socialist Republic of America enters a bold new era of government-run businesses.


" Meanwhile, Democratic-Socialist Senator Bernie Sanders saw no problem with the deal, stating, "If microchip companies make a profit from the generous grants they received from the federal government, the taxpayers of America have a right to a reasonable return on that investment.


" Cenk Uygur from The Young Turk says, "Old-school conservatives are saying Trump's plan to take a 10% stake


in Intel for American taxpayers, in exchange for our financial help, is socialism.


In capitalism, if you invest in a company, you get equity.


This is one of the best things Trump is doing.


Both parties have given endless subsidies to their corporate buddies, out of our pockets, and only gotten campaign contributions back.


That's why sell-outs in DC are panicking about their beloved donors.


They expect our money for nothing.


No, we want shares like any investor.


" Now, this is a little tongue-in-cheek but not totally.


To refine the argument a little bit from picking your favorite -ism and labeling this that, the deal was explicitly done to accelerate the grant timelines in hopes of Intel completing their new fab in Ohio.


The company has struggled to get private funding off the ground, in part due to leadership squabbles around fundraising at the board level.


CHIPS Act funds were previously tied to progress milestones, so pulling it all forward is a significant gratuity.


Still, it's, of course, understandable why there's so much unease around requiring an equity stake in exchange for government funding.


There's been a bit of variable discussion around whether the administration would seek a similar deal with other CHIPS Act beneficiaries like TSMC or Micron.


Compounding these concerns earlier in the year, Commerce Secretary Luttnik pushed to establish a sovereign wealth fund by taking equity stakes in exchange for government contracts.


While this deal is a pretty mild version of that concept, it suggests that this is a broader government strategy rather than a one-off emergency measure.


Stepping back from the ideological argument, analysts also noted that Intel doesn't just need capital, they need customers.


Dan Morgan, a senior portfolio manager at Cineviz Trust said, "Trump kind of becomes your salesperson.


" Bernstein analyst Stacey Ragson wrote in a note, "Funding a buildout with no customers probably won't end well for shareholders, of whom the US government would be the largest under this situation.


" Now, at the end of the day, this is, of course, not just the US government taking a stake in some random tech company.


The big question here is whether the US needs domestic chipmaking as a national security imperative or not.


Political reporter Saagar Enjeti wrote, "Taking a stake in Intel is much better than the pay-for-play structure for Nvidia and AMD.


Intel's managerial decline is a major reason we're so vulnerable on chip manufacturing.


" The point is that for some observers, where this gets blurry is the national security consideration of domestic chip manufacturing capacity, which takes this out of the realm of just generic debate and into its own very specific situation.


Now, it's obviously way outside the scope of this show to get into those debates in general.


For our purposes, what's important to note is that AI finds itself once again at the epicenter of an important shift in the political tides that could have big ramifications in the years to come.


Now, speaking of that Nvidia deal, their roller coaster ride in the Chinese market continues as they halt production of H20 chips.


The Information reports that Nvidia has instructed suppliers to stop production related to their China-specific chips.


The chips have been, to put it mildly, a political football in both Washington and Beijing all year.


The US government had banned H20 exports until this month when the President struck a deal with NVIDIA and AMD.


The arrangement was that export licenses would be granted in exchange for a 15% cut of sales.


Shortly after that, however, China opened an investigation into NVIDIA accusing them of including backdoors in the chips.


Beijing later went farther, warning local firms not to use NVIDIA chips and to reconsider their orders.


That seems to have had an effect with sources saying that the entire H20 supply chain has been shut down as a result.


Those sources added that NVIDIA and their Chinese customers are hoping to get approval to sell an updated chip based on the new Blackwell architecture.


The Trump administration has flagged that they would be open to a deal provided the new chips have 30 to 50% less performance than US market chips.


Trump himself set those parameters after calling the Blackwell chips, quote, "Super-duper advanced.


" Now, we don't have a good sense of how NVIDIA could possibly assure Beijing about the safety of the chips or whether the prohibition is actually about protecting Huawei from competition.


Some outlets are even suggesting that the ban was pure politics with Chinese officials calling US Commerce Secretary Howard Lutnick's comments that Chinese developers were addicted to NVIDIA chips insulting.


Jimmy Goodrich, a senior advisor to think tank RAND, said, "China wants to have the best chips but also wants to have domestic chips at the same time.


Those two goals are in conflict with one another.


" If the US government were to approve a powerful Blackwell chip for China even at 30 to 50% performance reduction, my guess is we'd miraculously see China forget about security concerns.


In an official statement, NVIDIA appeared sympathetic to concerns from Beijing.


They said, "China won't rely on American chips for government operations, just like the US government would not rely on chips from China.


However, allowing US chips for beneficial commercial business use is good for everyone.


" That's still a notable turnaround from earlier this month when NVIDIA seemed to be gearing up to produce H20s as fast as possible.


During a visit to Taiwan on Friday, CEO Jensen Huang gave a status update, commenting, "It's up to, of course, the United States government.


We're in dialogue with them, but it's still too soon to know.


" Speaking to Beijing's concerns, he added, "We're surprised by that.


They requested and urged us to secure licenses for the H20s for some time.


" And this is really the other dimension of the political story when it comes to chips.


The US is thinking not only about its domestic production capabilities but also export controls, which in and of itself is a complex set of considerations.


Do you focus on denying China access to the most advanced chips or do you try to expand access and think about model access and chip access as a form of soft power?


A recent Reddit post quoted a16z partner, Martin Casado, who said that when startups apply to a16z for funding, there's a, quote, "80% chance they're using a Chinese open-source model.


" Lastly, bringing the political consideration back home, earlier this week, a group of Silicon Valley investors and builders announced a new AI PAC in advance of next year's midterm elections.


The Wall Street Journal writes, "Silicon Valley is putting more than $100 million into a network of political action committees and organizations to advocate against strict AI regulations, a signal that tech executives will be active in next year's midterm elections.


" The big new PAC being announced is called Leading the Future.


They wrote on Twitter, "AI is already transforming the global economy, and America should lead in advancing a policy governing AI expansion that drives innovation and broad economic growth for working people across the country.


Leading the Future and its affiliated organizations will promote policies that unlock the transformative and economic benefits of AI.


LTF and its affiliated organizations will oppose policies that stifle innovation, enable China to gain global AI superiority, or make it harder to bring AI's benefits into the world, and those who support that agenda.


" In their announcement press release, LTF got a little bit more detailed about how they plan to operate.


They write, "LTF will operate through a structured ecosystem of federal and state Super PACs and 501 [c] [4] issue advocacy organizations, giving it maximum flexibility to engage in the 2026 election cycle and beyond.


The federal and state Super PACs will focus on independent expenditures to support candidates aligned with the pro-AI agenda and ensure America leads the world in AI innovation and oppose those that do not.


These efforts will span both primary and general elections, aiming to reshape the political environment around innovation policy.


The [c] [4] organizations will lead proactive policy development, legislative scorecard production, grassroots engagement, and rapid-response campaigns to counter misinformation and anti-innovation narratives.


" They plan to begin their operations in New York, California, Illinois, and Ohio and will expand into a national footprint throughout next year.


Leading the Future is led by Zac Moffatt, previously founder and CEO of Targeted Victory, which has raised over $2 billion and served over a thousand campaigns since 2009, as well as Josh Vlasto, who was previously press secretary to US Senator Chuck Schumer and chief of staff to New York Governor Andrew Cuomo.


Some of the backers are common names at the intersection of Silicon Valley and Washington.


Ron Conway wrote, "I'm excited to be part of Leading the Future.


It is in America's interest to lead.


The only thing that stands in America's way are bad policies that will cause America to lose its lead to China.


Imagine if we're not all on the page when it came to the race to the moon.


We would've never even made it to the launchpad.


" A newer participant in this political game is OpenAI president and co-founder Greg Brockman.


He writes, "My wife, Anna, and I are supporting Leading the Future because we believe that AI can massively improve quality of life for every person and every animal.


We believe the goal of AI policy should be to unlock this outcome.


That means taking a balanced view, which we think of as optimistic realism, towards achieving AI's benefits.


" Now, so far, Leading the Future hasn't gotten specific around any particular issues or positions that it's going to be focused on, but I would expect this to be the very first of a lot of AI-related political activity heading into next year's midterms.


One of the things that was really interesting about the presidential election last year was how little ultimately AI factored in.


There were grand concerns around disinformation and deepfakes, none of which really came to bear.


Also, as much as people were theoretically interested in the issues of AI, they were still pretty firmly in the realm of the future last time.


What'll be interesting to see this time around during these midterms is how much those issues actually come home to roost in the here and now.


Will AI-related job loss be something that people actually care about in Congress and Senate races in the heartland of America?


Will competition with China be something that politicians actually talk about?


Too early to know, but it's very clear that the AI industry is going to have a seat at that table.


For now, though, that is gonna do it for today's AI Daily Brief.


Today on the AI Daily Brief, what other companies can learn from the backlash around YouTube's use of AI to upscale people's videos.


Before that, in the headlines, an interesting partnership between Meta and Midjourney.


The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.


Quick announcements before we dive in.


First of all, thank you to today's sponsors, Blitsy, Superintelligent, and Hightouch.


And as always, to get an ad-free version of the show, head on over to patreon.com/aidailybrief.


We kick off the day with an interesting partnership announcement.


Meta has struck a new deal with Midjourney.


On Friday, Meta's new chief AI officer, Alexander Wang, posted on X, "Today, we're proud to announce a partnership with Midjourney to license their aesthetic technology to our future models and products, bringing beauty to billions.


This technical collaboration between our research teams is part of our effort to team up with the best companies in the industry whose work and expertise complements our own.


They've accomplished true feats of technical and aesthetic excellence, and we're thrilled to be working more closely with them.


To ensure Meta is able to deliver the best possible products for people, it will require taking an all-of-the-above approach.


" Midjourney founder, David Holz, wrote, "Bringing sublime tools of creation and beauty to billions of people is squarely within our mission.


" However, he also confirmed, quote, "We remain an independent, community-backed research lab with no investors, working on a staggering array of ambitious projects, focused on bringing about humane futures where we are all midjourney.


" Now, this one is super interesting.


Midjourney has always had a pretty fascinating place in the AI image generation pantheon in that it is just really aesthetically tasteful.


I've said before that my first conversion experiences into the world of AI were largely with Midjourney.


I would imagine dreamy scenes from history, like Ernest Hemingway in Paris in the 1920s.


As their models have evolved, they've continued to get better and more tasteful and more interesting and more visually arresting, although they haven't evolved in some of the ways that other tools have, which have left them in a different use case column for me and many others.


When you see an image, like a cover image or a thumbnail on one of my videos, it's almost invariably created by Ideogram.


Sometimes it's from OpenAI, which you can usually tell from the weird yellow tint.


But I would say that the vast majority of time, it is in fact Ideogram.


When I need to do something visually arresting though, something where the main determinant of success is beauty or just visual interestingness, then I'm turning to Midjourney.


This has led people to wonder what Meta is going to do with the company.


The Verge suggested that, in their words, "Meta is going to stuff Midjourney AI images into your feed," which some think is terrible, but which others rightly point out that since Facebook is already inundated with AI images, leveling up the aesthetic quality with a Midjourney partnership might not hurt.


The other question is whether the deal arises out of a failed acqui-hire attempt.


It was already reported that Meta had attempted to buy out GenVideo startup, Runway, so we know they've had their sights on various AI multimedia companies.


The partnership, however, suggests that Meta isn't just looking for talent, but also trying to get their hand on the best tech to build visual AI products.


Responses really run the gamut.


Some saw this as a knock on Meta's skill.


AI commentator, Spore, writes, "Midjourney is phenomenal and this is a great partnership for both parties.


But man, another point in favor of being bearish on Meta.


" Gaming startup founder, Asad Dar, suggested instead though that the move was more about urgency, commenting, "Meta licensing Midjourney tech is smart.


Creative AI is still undervalued, but it's going to define how we share, market, and communicate online.


" Tony Wong thought it was an example of the natural evolution of platforms.


He wrote, "Meta plus Midjourney equals platforms now outsource taste itself.


We've seen this playbook before.


Spotify didn't just stream music, they algorithmic-ed musical discovery.


Netflix didn't just host shows, they became the curator of what stories get told.


TikTok didn't just show videos, they rewired attention spans.


" Like all things with AI, this is basically a Rorschach test on how you feel about AI and the companies involved.


So, whether this is making AI slop look prettier or an example of how Zuckerberg comes back swinging when his back is against the wall is mostly going to be in the eye of the beholder.


The company is upsizing their infrastructure through a $10 billion cloud deal with Google.


Sources speaking with The Information say that the two tech giants have agreed to a six-year deal that will see Meta spend more than $10 billion.


Those sources said that the deal largely relates to Meta securing their AI inference needs.


Now, this month has seen Meta shuffle around their data center plans as they figure out how to fund one of the most ambitious buildouts in the industry.


The company has forecast around 70 billion in CapEx this year and another big increase in 2026.


Earlier this week, it was reported that Meta had closed a $29 billion private credit deal, marking an interesting shift from big tech firms self-financing their infrastructure buildout.


And now we have Meta positioning themselves as a large renter of inference as well as a data center operator in their own right.


Meta also has agreements in place with Amazon and Microsoft, but the Google deal is a significant jump in capacity.


Now, ultimately, this makes sense.


When Zuckerberg was out recruiting the superintelligence team, he made a big point that the best talent wants access to unconstrained compute.


News from another major player in the foundation model game, xAI, the company has officially open-sourced Grok 2.5.


The model, which is now two generations behind, has become available on Hugging Face.


Elon Musk said that the previous generation model, Grok 3, will be available in around six months.


The release gives us the first opportunity to examine Grok's architecture.


The model has 268 billion parameters arranged in a mixture of experts design, with eight total experts and two active at a time.


It has a maximum of 131,000 tokens of context.


Interestingly, AI engineer, Tim Kellog, criticized the release, noting that the custom license has some anti-competitive terms.


He queried, "Why did it take so long to open this up?


There's nothing interesting here.


"Now, to the extent that there is something interesting for some, it's really just xAI setting the tone by deprecating their old models into the open source world.


AI researcher, Sebastian Raschke, commented, "It's quite cool that the open source community gets the real full-size model that was used in production as opposed to the lite or spinoff version.


" Separately, Elon Musk has announced plans to launch an AI software company called MacroHard.


Aside from enjoying the pun relative to Microsoft, Musk commented, "In principle, given that software companies like Microsoft do not themselves manufacture any physical hardware, it should be possible to simulate them entirely with AI.


" We'll see whether anything comes of it, but the idea does echo OpenAI's concept of level five agents that are capable of doing all the work of an organization.


Lastly today, a fundraising update from Anthropic.


The company is nearing a deal to raise up to $10 billion in a jumbo-sized round.


The last reporting on the rumored terms suggests Anthropic was looking to take in 5 billion at a $170 billion valuation.


So in a matter of a few weeks, Anthropic has doubled their target and apparently had no trouble at all filling the round.


Bloomberg reports that ICONIQ Capital is leading the round with firms including TPG, Lightspeed Venture Partners, Spark Capital, and Menlo Ventures participating.


The Qatar Investment Authority and the Government of Singapore Investment Corporation have also reportedly participated in talks as Anthropic opens their cap table to sovereign wealth funds for the first time.


Now, if you're keeping track, this seems to be the round where Anthropic grows beyond the capacity of traditional venture capital.


5 billion valuation they achieved at their last round in March, and have also tripled the scale of fundraising for this particular round.


At this rate, Anthropic will overtake OpenAI as the most valuable AI lab in a year.


" Indeed, and by the way, I think that is an arbitrage that people are not taking enough advantage of, but that is a topic for another day.


For now, that's gonna do it for today's AI Daily brief headlines.


This episode is brought to you by Blitzy, the enterprise autonomous software development platform with infinite code context.


Blitzy uses thousands of specialized AI agents that think for hours to understand enterprise-scale codebases with millions of lines of code.


Enterprise engineering leaders start every development sprint with the Blitzy platform, bringing in their development requirements.


The Blitzy platform provides a plan, then generates and pre-compiles code for each task.


Blitzy delivers 80% plus of the development work autonomously while providing a guide for the final 20% of human development work required to complete the sprint.


Public companies are achieving a 5X engineering velocity increase when incorporating Blitzy as their pre-IDE development tool, pairing it with their coding copilot of choice to bring an executive SDLC into their org.


Blitzy is providing a limited time 30-day free proof of concept for qualifying enterprises.


The team will provide a 5X velocity increase on a real development project in your org.


com and press book demo to learn how Blitzy transforms your SDLC from AI-assisted to AI-native.


That's B-L-I-T-Z-Y.com.


If you are a regular listener, you will have heard about Superintelligent's agent readiness audits at this point, but I wanted to tell you today about the full suite of agent readiness products that go beyond just the initial readiness report.


Over the last six months, Superintelligent has built out an entire agent planning suite.


We help you move from discovery to planning to implementation.


These reports are going to help you understand what sort of technical preparation you need to do to be ready for a use case, what challenges you might face in implementation, and whether you should be thinking about building, buying, partnering, or some combination.


After that, you can even get a spec document in what we call our technical blueprint that gives either your developers or the developers of the partner you work with what they need to build exactly the agent that you're looking for.


If you want to learn more about Superintelligence agent planning suite, we built a custom GPT to answer your questions.


Just go to bit.LY/supersuperagent.


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That's hitouch.com.


Welcome back to the AI Daily Brief.


Today, we've got an interesting topic.


On the one hand, we're covering a new AI-related backlash at YouTube after the company started implementing AI in ways that they weren't exactly clear about, angering a number of creators, but we're pairing that with some new rules of the road from Netflix around GenAI.


And asking broadly what company strategy should be when it comes to AI in media?


Now, I think background context for all of this is that we are in this interesting and frankly sort of weird moment where, on the one hand, we are seeing in general a lot more AI backlash.


This has been present ever since the beginning of ChatGPT, but it's one of those summer moments where it gets a little bit louder and where the critics are jumping on a few recent happenings to really amp up the anti-AI narrative.


At the same time, when you go and spend time with anyone in sort of normal society, normal work, normal youth, normal school, AI has just so quickly become ubiquitous and commonplace that it takes a lot of the heft frankly out of some of those critiques.


No one who's interacted with AI or uses ChatGPT needs to be sold on how powerful and useful this technology is.


And in that context, the conversation necessarily shifts away from whether this is going to be a thing, and more, how we do it well.


And today, we're getting into a couple different instances of how to do it well, or perhaps, how not to do it well.


Over the last few days, on all the big social networks, IEX, Threads, et cetera, a ton of posts started to come up like this one from Kiwi Sagas.


"PSA for artists on YouTube.


" They continue, "The left images are my original upload on TikTok, and the right is YouTube's version of the same content.


" Now, by the way, if you're not watching this, it's probably a little bit better watching the video on either YouTube or Spotify, but basically, we've got two images from this creator.


The first in each case is a little lower fidelity, a little more blurry.


And then the second is a much crisper image.


Now, it would be one thing if it was just a perfect improvement in the sense of higher resolution, but this is AI and that means there can be weird artifacts.


And as you can see in the second image, it's not ultimately exactly the same.


It's a little bit different.


Jamrockhobo writes, "Looks like we can't showcase artwork on YouTube Shorts anymore without it being automatically subjected to a horrible AI sharpener.


" Once again, we have the same thing where you see a piece of art and then an artificially AI-enhanced version of it.


Pretendmirrors writes, "YouTube is applying post-processing to all Shorts, so they look more like AI.


This is intentional and malicious.


When their generative AI Shorts feature launches, users will be less likely to discern what's AI slop and what's not, and the audience will think AI video is just as good.


" So, what we have here is an introduction of a concern.


It's not just the creators are annoyed about YouTube doing this without their permission, it's that they think that it's going to train people basically to not be able to tell the difference between AI generations and human-generated videos with a host of attendant consequences.


Sam Aburime writes, "Please be aware of what's going on.


If everything starts to look a little off and a little fake, it's much easier for bad actors to not only push through actual disinformation, but also attempt to discredit factual video sources.


" Again, reinforcing this idea that the problem is not just the permissions, but instead, the closing the gap between AI and non-AI video has consequences when it comes to mis- and dis-information.


Over the weekend, numerous outlets picked up the story including the BBC, who wrote a piece called YouTube Secretly Used AI to Edit People's Videos.


The Results Could Bend Reality.


The central question in the piece, as expressed in the subhead, "As AI quietly mediates our world, what happens to our shared connection with real life?


" The BBC writes, "In recent months, YouTube has secretly used AI to tweak people's videos without letting them know or asking permission.


Pay close attention to ears, and you may notice them warp.


These changes are small, barely visible without a side-by-side comparison.


Yet some disturbed YouTubers say it gives their content a subtle and unwelcome AI-generated feeling.


There's a larger trend at play.


A growing share of reality is pre-processed by AI before it reaches us.


" It got enough buzz that YouTube had to respond.


The team YouTube account wrote, "We hear your concerns and want to clarify.


The enhancements on some YouTube Shorts are from an experiment using traditional machine learning to improve video clarity, not GenAI.


" YouTube's creator liaison, Rene Ritchie, wrote, "No GenAI, no upscaling.


We're running an experiment on select YouTube Shorts that uses traditional machine learning to un-blur, de-noise, and improve clarity in video during processing, similar to what a modern smartphone does when you record a video.


YouTube is always working on ways to provide the best video quality and experience possible and will continue to take creator and viewer feedback into consideration as we iterate and improve on these features.


" However, for some, the explanation isn't good enough.


Samuel Woolley, who is the chair of Disinformation Studies at the University of Pittsburgh, said, "You can make decisions about what you want your phone to do and whether to turn on certain features.


What we have here is a company manipulating content from leading users that is then being distributed to a public audience without the consent of the people who produced the videos.


" Woolley also argued that the choice of words feels like, in his description, a misdirection.


"I think using the term 'machine learning' is an attempt to obscure the fact that they use AI because of concerns surrounding the technology.


" Now, the BBC points out that, to some extent, this is just a new version of an old conversation.


They write, "Thirty years ago, there was similar hand-wringing about the havoc Photoshop would reap on society.


Decades later, we have conversations about the harms of airbrushing models in magazines and beauty filters on social media.


Perhaps AI is more of the same, but it puts these trends on steroids.


" I don't think ultimately that this is, in and of itself, a huge, huge deal.


YouTube's been a good platform to me.


" But I do think that it brings up important questions around consent and control, classic questions of the relationship between platforms and their creators, and obviously exemplifies the fact that AI, despite its increasing ubiquity, is going to be a cultural battleground in some ways.


Now, speaking of that, another company that has been wrestling with its approach to AI has been Netflix.


You might remember back in July on an earnings call, Netflix CEO Ted Sarandos talked about how they had used GenAI in one of their original programmings in the Argentina market.


Sarandos said, "We remain convinced that AI represents an incredible opportunity to help creators make films and series better, not just cheaper.


There are AI-powered creator tools, so this is real people doing real work with better tools.


It's a very big hit show for us from Argentina.


In that production, we leveraged virtual production and AI-powered VFX.


And in fact, that VFX sequence was completed 10 times faster than it could have been completed with traditional VFX tools and workflows.


" At the time, we discussed the fact that Netflix was, one, clearly trying to say that they're not using AI tools to replace people.


Instead, AI is a new toolset for their people.


And two, that this isn't even just a cheaper way of doing something that would otherwise have been done with a more traditional process.


This is an example of where, if it weren't for GenAI, the entire sequence would have had to be scrapped because it would never have been in the budget in the first place.


In other words, a total net additive force.


Even before that, Netflix was no stranger to the AI conversation.


Back in 2024, it got in a little bit of hot water when it was accused of using AI-generated images in a true crime documentary.


Futurism's The Byte wrote, "Resorting to the tech to generate pictures of a real person, especially of somebody who's still in jail and will only be eligible for parole around 2040, should raise some alarm bells.


This is tinkering with the fabric of reality itself to manipulate a true story that actually happened.


" Well, this week, the company tried to get out ahead of these types of issues by publishing a set of guidelines for GenAI use in its content production.


Posted to their partner help center, the piece is called Using Generative AI in Content Production, and is meant as a resource for their partners.


They write, "This guidance helps filmmakers, production partners, and vendors understand when and how to use GenAI tools in production.


It also offers a practical tool for assessing and enabling confident GenAI use when producing content for Netflix.


" The company lists five guiding principles.


The first, "The outputs do not replicate or substantially recreate identifiable characteristics of unowned or copyrighted material, or infringe any copyrighted-protected works.


" Two, "The generative tools do not store, reuse, or train on production data, inputs or outputs.


" Three, "Where possible, generative tools are used in an enterprise-secured environment to safeguard inputs.


" Four, "Generated material is temporary and not part of the final deliverables.


new talent performances or union-covered work without consent.


" Basically, they say if all five of those are met, quote, "Socializing the intended use with your Netflix contact may be sufficient," but if you answer no or unsure to any of them, you gotta go get written approval.


It's clear that one of the big things that they're thinking about is some of those issues that we were just discussing in the context of YouTube.


Netflix writes, "Audiences should be able to trust what they see and hear on screen.


GenAI, if used without care, can blur the line between fiction and reality or unintentionally mislead viewers.


" In that same post, they also share a use case matrix with a red light, yellow light, green light approach to help people understand some examples.


An example of a full green light would be something like using GenAI for ideation only, i.e.


mood boards and reference images.


The rationale for it not needing escalation is that it's low-risk and non-final.


For something where a company was using GenAI to generate background elements, like signage or posters that appear on camera, it's in that in-between category.


They write, "Incidental elements may be low-risk, but if story-relevant, please escalate.


" Meanwhile, the full red lights are things like using GenAI to create final character designs or key visuals, or using GenAI for talent replication, such as re-aging or synthetic voices.


Now, importantly, Netflix isn't saying that those things are completely off the table.


They're saying that they have to be reviewed and signed off on by Netflix, not just by a third-party production company.


Now, to many, this is just incredibly common sense.


Cristóbal Valenzuela, the co-founder, CEO of Runway writes, "Really nice to see this public Netflix guide for using AI in content production.


It makes it easier for their partners to understand how to adopt.


And in general, I think we're going to see a lot more of this.


I would anticipate that in 2026, there are less debates around the ifs of AI and more focus on the hows, and frankly, I think that will be all to our benefit.


But of course, in some ways it still feels incredibly toe-dipping relative to what the ultimate impact of AI on entertainment is going to be.


To get a preview of where that might be headed, check out the new company Showrunner.


Variety describes it as a tool for user-directed TV shows, and basically the idea is exactly what it sounds like.


You type in a few words and you can create entire scenes or entire episodes of a TV show, either from scratch or based on an existing IP.


" He said, "Using AI purely as a VFX tool is a little sad," and continued, "The Toy Story of AI isn't just going to be a cheap Toy Story.


Our idea is that Toy Story of AI would be playable with millions of new scenes, all owned by Disney.


" Now, obviously with that vision, the company's in talks with all the IP holders and has investment from Amazon, among others.


Whether it works remains to be seen.


" But I would expect a lot more experiments like this in the years to come.


First of all, thank you to the sponsors of today's show, KPMG, Blitsy, and Superintelligent.


And if you're interested in sponsoring the show, shoot us a note at sponsors@aidailybrief.ai.


We're now basically full up for 2025 and selling into 2026.


And with that, let's get to this one that has been brewing all week long.


Welcome back to the AI Daily Brief.


Boy, has this one been brewing all week, man.


Today, we are finally digging into this report out of MIT claiming that 95% of generative AI pilots at companies are failing.


Now, there is really important context and background on why this report is worth the time that we're about to give it.


And simply put, that's because it has not stayed as just a point of discussion in the AI space or even in the corporate and enterprise space, but has actually been cited as one of the reasons that AI stocks are flailing right now.


They're saying their clients wanna talk about it, and it is being relentlessly cited as an example of why the AI bubble, in big old air quotes, is about to or are in the process of popping.


Now, this is not a market show, but I do think it's worth before we dive into the report having a little bit of a discussion about what I actually think is going on in the markets.


As I'm recording this, Fed Chair Jerome Powell is about to speak in Jackson Hole.


This is an annual central bankers conference where each year the Fed lays out their vision of what's to come.


Now, this being Jerome Powell's last year, people aren't really looking at it as some big inflection point on what the future of the Fed might hold, but they are very closely looking at it when it comes to whether we're going to get a rate cut in September.


Markets really, really, really want rate cuts in September, and they are very, very nervous that Jerome Powell is going to throw cold water on that in his speech.


In that general nervous climate, which is also, by the way, being aided by the fact that we are in the late summer period where liquidity is low, and just in general, there's less actual stuff happening, there's definitely an increasing recognition of the fragility of the stock market and the extent to which it's been propped up by enthusiasm around AI.


And this is absolutely undeniable.


Ever since the rate hiking cycle started back in 2022, it has been AI enthusiasm versus the world.


At every point when there's been some big, crazy macro event, be it tariffs, be it war, AI enthusiasm has been there to hold it down and continue to give stock market investors something to be excited about, and basically, pin all their hopes onto.


There is a growing fear now of what happens if that all goes away.


That's being actively abetted by short sellers, who are specifically going after stocks like Palantir, which they view as wildly overpriced.


New chief AI officer at Meta, Alexander Wang, tweeted, "We are truly only investing more and more into Meta Superintelligence Labs as a company.


Any reporting to the contrary of that is clearly mistaken.


The point is that the market is jittery right now, and that is the context into which this MIT study has come.


Now, I will say, as we get into this report and into my very deep critique of both the report and the interpretation of the report, I will note that the folks at MIT who did this study are not working for Citron, trying to tear down Palantir and other AI stocks.


They didn't know the context their study was going to be released into.


They didn't know [laughs] when they were typing this up that there was gonna be a whole narrative moment around GPT-5.


They didn't know that Sam Altman was gonna say that we were in a bubble.


The point is that while you can critique lots of things about this report, there was certainly no ill intent or an attempt to drive a particular narrative.


It just happened to be the perfect headline bomb in that environment.


Now, with those caveats out of the way, this is what I will say.


Anyone, and I mean anyone, who is letting their opinion be overly shaped by this study, and especially anyone who is making financial decisions based on it, should be embarrassed and needs to rethink their general susceptibility to headlines for two reasons.


The first, the study just is not well-constructed, period.


We're gonna get into it.


Second, it's not even really saying [laughs] what the reporting has suggested.


So let's get into this thing.


The first weird thing about this is that it's been extremely hard to get your hands on the actual report.


Everything about this, the entire conversation came out of this one piece in Fortune called MIT Report, 95% of Generative AI Pilots at Companies are Failing.


But the group at MIT who actually released this report did not make it easy to get their hands onto it.


As recently as yesterday, on Thursday, Professor Ethan Mollick wrote, "Has anyone seen a copy of the Project Nanda report on 95% of AI pilots failing?


It has been reported about widely, but I haven't been able to actually read it, though I filled out a form to get access.


I wanna understand what they found and how they measured it.


" I had the same experience.


As soon as I saw this news, I clicked the little link in the Fortune piece to the report and got this Google Form to request access to the research.


I did so numerous times, never heard from them, and eventually just found a version that someone had uploaded separately.


Now, to the extent that it was an intentional decision not to make this report widely available, I kind of understand that [laughs] after I got access to it.


It's called Project Nanda, and they describe themselves as, quote, "Building the foundational infrastructure for an internet of AI agents, i.e.


the system where trillions of AI agents can collaborate, communicate, and transact across organizational boundaries.


Created at MIT, Nanda provides the index protocols and tools needed to enable this decentralized protocol-neutral ecosystem.


" Now, where I think that the issues begin with this report is in the methodology.


In short, this thing is kind of like vibes masquerading as a credible academic study.


Turns out that this report, which has been so widely cited as causing billions of dollars to come off of the market cap of AI stocks is based around 52 interviews, 150 survey responses, and a review of public announcements to see if people had talked about how AI is making them money yet.


For some context, Superintelligent interviews significantly more than 52 executives every day.


The idea that you could get a broad-based understanding from that in a way that should move markets is extremely, extremely dubious.


By the way, we don't have any information about who those executives were, the size of the organizations, the role the executives had within the organization, or any of that information which would help us contextualize and understand how much we're talking about big firms, small firms, mid-market firms.


It's also worth questioning this so-called systematic review of over 300 publicly disclosed AI initiatives.


Here's how the researchers say that they define success.


AI's Brian put it on Twitter, "The MIT report's main source is 300+ companies' public announcements.


The companies need to have announced that AI caused a marked and sustained productivity and/or PnL impact to be successful.


Now, other problems start to reveal themselves when you dig in.


4 of the report, they share that they found that 50% of GenAI budgets are going to sales and marketing.


Now, for them, this is part of why there's a GenAI divide, because sales and marketing is a worse place to spend AI money than back-office automation.


But the idea for anyone who spent any amount of time digging around AI that 50% of GenAI budgets are going to sales and marketing is just absurd.


Every credible study looking at where GenAI is being applied sees it basically equal across the organization.


It's everything from coding assistance to content generation, to document and knowledge retrieval, to product and design, sales productivity, customer engagement and service, QA and testing.


In other words, there is no universe in which 50% of GenAI spending is going to sales and marketing.


The only implication of that can be that the people who they interviewed from these 52 organizations are hyper-concentrated in those domains, which is fine if we understand that.


But when it's not revealed explicitly, it calls into question all of the implications of the broader study.


Reinforcing the idea that these were probably sales and marketing executives, when they asked their interviewees to allocate a hypothetical $100 to different functions, sales and marketing functions captured approximately 70% of the budget allocation.


More broad than that, the terminology is very unclear in this.


What they're referring to as a pilot is sort of ill-defined.


There doesn't really seem to be any mention of AI or agentic coding, despite the fact that that is very clearly the breakout use case inside the enterprise.


And it kind of feels like all of this is about deployments of copilot tools and ChatGPT wrappers, rather than agentic AI, which is obviously where all of the emphasis is right now in the enterprise.


And agents are really important to this story, because, and here's where we move out of the critique of the report itself into the critique of the way that it's being interpreted.


When you actually take this thing at face value, the report is not actually about whether AI delivers value or not, but how much value accrues to individuals inside the corporation versus to the organization itself.


The most unreported part of the study is that they found that basically all the value is accruing to the individual.


They call this the shadow AI economy, and the starkest example they give is the percentage of companies who have purchased an LLM subscription versus employees who use LLMs regularly.


Although they found that 40% of companies had purchased LLM subscriptions, they found 90% of employees were using LLMs regularly.


"In many cases," they write, "shadow AI users reported using LLMs multiple times a day, every day of their weekly workload through personal tools, while their company's official AI initiatives remained stalled in pilot phase.


" Now, it's important to pause here, because the implication of all of these articles and all of the analysis on Wall Street about it was that what the study said was that 95% of AI is not useful and not valuable.


In other words, it was not that 95% of pilots were structured in ways where the organization didn't get as much value as they wanted to.


It was straight up that 95% of the time, AI isn't useful, and that's just absolutely not what they're saying.


And here's where some of our discourse about productivity is problematic.


Let's imagine that everyone inside an organization starts using a full slate of AI tools and is instantly 40% more efficient.


Basically, they do their work in three days instead of five days.


Even if that happened, and everyone across the organization had that same experience, that would not show up in productivity metrics or on a PnL.


To show up in PnL or in some sort of productivity metric, the organization would either have to reduce the workforce by the equivalent 40% so that the overall cost to produce the same amount of outputs went down, or they'd have to ensure that each of those people who were now more productive.


.....went back and used that new time won back to produce more widgets for the same cost.


Part of why organizations have been so aggressively interested in agents is that they have the potential to replace wholesale functions where that time can be better reorganized and reconstituted in a much more direct way than in the context of the very fuzzy person got their work done faster or better with ChatGPT type scenario.


That's not to say that organizations aren't enthusiastic about their people using productivity-enhancing copilots.


It's that when they think about the real big productivity gains of AI, they're looking beyond just the individual deployment of tools to the redesign of systems, and especially the redesign of systems where agents are taking on big chunks of the work.


Now, here's the other big problem with the interpretation of this report.


Even if the numbers were correct and 95% of GenAI pilots were failing, the way that the market and the naysayers have been interpreting this is that that means that 95% of AI technology itself is crap.


Rather [laughs] than the much more obvious, if you've ever worked inside an organization or big enterprise even for a little bit at any point in your life interpretation, which is that there are major problems not with the technology but with the implementation of the technology.


Problems, in other words, on the organization side.


Well, it turns out that even right there in the report, it is very clear that much of the problem is on the organizational side.


When users were asked to rate each issue on a scale of one to 10 on how big a barrier it was in scaling AI in the enterprise, there were technical problems, model output quality concerns got a seven and a half, and poor user experience got around a seven, but the other three top answers all had to do with the organization itself.


Challenging change management got a six and a half, lack of executive sponsorship got a six and a half, and an unwillingness to adopt new tools got a full nine.


Yet given that we at Superintelligent do this day in and day out, and actually interview thousands of people about these questions, I've put together this list of 15 reasons why AI pilots actually fail.


And let's be clear, some of them are, yes, technology problems.


There can be platform mismatches, where a startup is trying to implement a new solution that just doesn't play well with whatever legacy systems an enterprise has.


This is a really common refrain.


Now, usually you can avoid that before you get into the pilot if you've gone through any sort of quality assessment and design process to actually lay out the approach, but still it's there.


There is also, yes, underperformance.


Startups are going to overpromise and underdeliver sometimes.


It would be insane to claim that they didn't.


We also are dealing with an environment where there can very easily be surprise costs, where a thing that you thought was gonna take X tokens actually takes X times 10 tokens and there's overages and all of a sudden people are all chagrined.


And the thing that seemed like it was going to add value actually isn't all that much cheaper in the first place.


And the point is that I'm not trying to obfuscate those things or say that they don't exist.


My point is that whether you consider this one or three or five of the problems, I'm about to tell you about 15 others [laughs] that are on the organizational side.


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Let's start with the most obvious one, which is leadership buy-in.


If executives are not bought into change, it will not happen.


Anytime we see an organization where there's some innovation group that has a theoretical mandate to do AI pilots, but not actually the budget to do so, or the executive sponsorship to do so, we know beyond a shadow of a doubt that it's got an extremely limited shelf life.


It's just not gonna go that far.


Leadership buy-in is the fundamental prerequisite to all change, period, not just AI change.


We see constantly the organizations that have the best agent readiness scores when we do our assessments are those that have not only executive buy-in, but CEO level buy-in.


Without it, you're just living on borrowed time.


Interestingly though, there is an inverse of this, which is equally important, frankly, which is team buy-in.


We often see the opposite situation, which is where the executives are thrilled, engaged, highly excited and motivated, but haven't taken the time to get their teams and employees bought in.


Now, those teams and employees may be going through a set of considerations and concerns around what these technologies are going to mean for them.


What's the company's visions of how digital employees and human employees are going to interact in the future?


Or is it an organization that's just trying to cut costs at any cost?


Lots and lots of studies have shown this potential for misalignment between employees and executives.


A number of times I've shared this study from Writer last December that interviewed 800 employees and 800 executives, and found just wildly divergent perspectives on AI inside the organization.


For example, this 31% gap between executives and employees when it comes to belief that their company has a high level of AI literacy, a 30% gap between the percentage of employees who think the company had been successful in adopting AI versus the number of executives who did, and so on and so forth.


So yes, you need leadership buy-in, but you need team buy-in just as much.


Next up, we have problem-value fit.


This is basically where you see a really cool demo and you decide to try it out, but no one can really name the metric that's gonna move.


These tend to come out of briefs that talk about innovation or productivity in the abstract, but where the people who are actually implementing this can't point to a specific error or cost or problem that's going to actually be impacted positively.


Success then tends to feature anecdotes and screenshots, not a target KPI or baseline.


And speaking of baselines, this is another area where pilots often fail.


Teams will say it feels faster or it feels better, but no baseline exists and there's no control.


Wins in these situations are measured in vibes, and when challenged, teams can't show lift or even really a simple pre and post.


Another challenge is that a lot of times the tools and systems that are being implemented lack the relevant enterprise context to actually be useful.


Sometimes, yes, just a general purpose tool can provide some lift, but a lot of times what's gonna make AI work or not is the information and data it has access to regarding the enterprise that it's trying to work within.


Enterprise AI that lacks enterprise context is ultimately extremely limited, which gets to another problem, of course, which is data readiness.


Lots of times that context does exist, but not in ways that AI can take advantage of.


There's a reason that you see so much effort and work around things like getting enterprise data into MCP servers, or you see tons of money being spent on the AI-ification of data lakehouses.