The Big Questions That Will Decide the Consumer AI War

Today on the AI Daily Brief, the big question shaping the battle for consumer AI, and before

that in the headlines, is to open AI, the new GitHub, the AI Daily Brief is a daily

podcast and video about the most important news and discussions in AI.

Alright friends, quick announcements before we dive in.

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First thank you to everyone who has taken our February AI usage pulse survey.

You can find a link to that at aidelebrief.ai, and I would so appreciate it if you would

take just a couple minutes to do that.

Anyone who does will get the results before everyone else and help us better share data

about where users actually are and their behavior patterns right now.

But if you are a company who is interested in building agent teams, registration is live

for EnterpriseClaw at EnterpriseClaw.ai and we'll close on Friday.

Now with that out of the way, let's dive into the headlines.

Back in December of last year, Mitchell Hashimoto tweeted, the AI companies are on track

to become GitHub faster than GitHub is becoming an AI company.

A lot of folks agreed, although some, like Ivan Burazin, had thoughts on who it might

be.

And yet, yesterday we got this report from the information that OpenAI is developing

an internal alternative to GitHub.

According to the information sources, the project was spurred by a rise in outages for Microsoft's

code repository platform.

OpenAI engineers complained that these outages have stopped work for minutes or even hours

at a time.

GitHub had 37 outages in February, which was optometically from an average of 17 per month

last year.

Microsoft has attributed these outages to human error and problems with Azure during a multi-year

migration project away from GitHub's proprietary servers.

Now sources for the OpenAI project did say that it's in its early stages and likely won't

be completed for months.

They also noted that the project is intended for internal use first and foremost, but then

again, so was Clawed Code.

This also isn't the only project to rebuild GitHub for the agentic era.

That was also the pitch for the new startup from former GitHub CEO Thomas Domke when he

left Microsoft earlier this year.

He's idea was the integration of agentic code review tools to help close the loop on

fully autonomous code generation.

Now there are a lot of people who are trying to put different lenses on this.

For some, it's the latest example of OpenAI competing with Microsoft as the riff between

the two companies expands.

Others see it as part of the SaaS apocalypse theme of companies cancelling their software

subscription in favor of vibe-coded alternatives.

I'm not sure any of that's true.

It feels to me like it might just be the start of an inevitable shift in this category,

given how much code is pumping through these companies coffers.

As Amaya puts it, the interesting play is not just hosting code, it's owning the layer

that understands how the code connects across services and teams.

That's where agents actually need to operate.

Next up, we move over to Meta who has formed a new applied AI engineering organization.

According to a memo viewed by the Wall Street Journal, the new organization will work closely

with both AR and VR organization Reality Labs, as well as the Meta Super Intelligence

Lab.

Now this doesn't seem to be another prod restructuring of AI at Meta, which by some

counts went through four reshufflings last year.

Instead it appears to be aimed at filling gaps between hardware, tooling, and model teams.

The memo said that the goal was to strengthen Meta AI initiatives, commenting that the team

will build the quote data engine that helps our models get better faster.

The new org has an unusually flat structure.

It consists of two teams of 50 people each reporting into a single manager.

One team will work on building interfaces and internal tooling, while the other works

on data collection and refinement.

The flattened team mirrors the structure of TBD labs, which consists of around 50 highly

paid AI researchers working under new AI CEO Alexander Wang within the broader superintelligence

org.

It also seems to reflect Mark Zuckerberg's new management philosophy that he outlined

on Meta's most recent earnings call.

He said that individual contributors are being elevated now that AI has allowed in

his words, projects that used to require big teams now can be accomplished by a single

very talented person.

Over in Amazon Land, that company is exploring the prospect of building technology to power

AI advertising.

According to the information, Amazon's ad business has held discussions over recent months with

major websites and ad sales firms about the idea.

The plan would involve placing ads and chatbots in agents.

One of the websites mentioned as a focus of the pitch was Pinterest, which is in the middle

of an AI overhaul.

In October, Pinterest launched an AI shopping recommendation assistant that helps users

track down clothing featured on the website.

You can see how this could be a natural fit for high intent traffic.

Now one of the things that people don't really know about Amazon or don't really think

about much is how big its ad business actually is.

Last year, Amazon generated $68.6 billion in ad revenue.

And while that represents only a tenth of their overall business, it was their fastest

growing division achieving 22% growth last year.

As advertising comes to the AI platforms, there could very easily be a land grab around

who gets to host the clearinghouse.

Now what consumers are going to think about all these AI ads remains to be seen and is

part of the conversation that we're having in the main episode.

Number in AI politics and chips, U.S. officials are considering a cap on Nvidia chip sales

into China in a bid to constrain the power of training clusters.

Bloomberg reports that U.S. trade officials are considering a cap of 75,000 chips per

customer.

Sources that the cap would apply to the newly approved Nvidia H200 chips, as well as AMD's

MI325 AI chips.

They noted that chip supply would also be contained to a million total units sold into

China, a limit that was set earlier in the regulatory process, but up to now hasn't

previously been reported.

A million unit limit is reportedly far lower than the number Nvidia originally proposed,

which gives some additional context to recent comments from Commerce Secretary Howard Letnick.

During congressional testimony last month, Letnick said that Nvidia must live with the

license term set by the government, and presumably this is what he meant.

The 75,000 chip cap is also less than half the number sought by Chinese tech giants

Alibaba, Tencent, and Bite Dance.

Each had reportedly told Nvidia that they would like chip counts of around 200,000 to build

their large-scale training clusters.

In these limits, each company will only be able to build data centers using around 100

megawatts of power.

That's a far smaller scale than the multi-gigawatt training clusters that are planned by Western

AI labs, and not even a match for XAI's original build-out of the Colossus Mega Cluster

last March, which began at 100,000 GPUs and quickly scaled to 200,000, and is now reportedly

at 550,000 units.

The big question is whether this is a meaningful constraint or simply window dressing to a piece

China-Hawkson, Washington.

What's more, the entire process is still murky and getting even murkier due to the

Iran War, considering that China is a major strategic trading partner.

Chips are on the agenda when President Trump meets with President Xi in a few weeks time,

but it's not hard to imagine that larger geopolitical issues could overshadow those

particular trade negotiations.

In Device Land, Apple has unveiled their new line of M5-powered devices at their global

event.

The new lineup includes MacBook Air and MacBook Pro models, all being the first to feature

the new M5, M5 Pro, and M5 Max chipsets.

The M5 chips feature a new component known as a neural accelerator to boost AI performance,

and it's very clear that Apple has focused on the AI use case when it comes to selling

these models.

As you might imagine, the only real question on the minds of the AI folks was summed up

by Noah Hirschfeld, who wrote,

The M5 MacBook looks cool and all, but where's the M5 OpenClaw Mac mini?

Lastly today, a bit of operator news, which I think is sneakily powerful, Stripe has

previewed a new feature that would make charging for token use much easier.

The feature allows AI app developers to automatically charge a usage fee directly on Stripe's

platform.

For example, an app developer might want to charge a 30% markup on API calls.

Previously, they would have needed to track token usage on their back end and periodically

generate lump sum bills.

The new feature allows Stripe to track usage and automatically build the customers the appropriate

amount.

Having this infrastructure provided startups could dramatically change the pricing structure

for AI apps.

Currently most apps charge a flat rate monthly subscription with usage capture credit-based

systems.

Under these models, token usage is a cost center, making profitability difficult to forecast.

Last year we saw multiple startups run into this problem, most notably, replete briefly

ran at negative 14% gross margins as demand and token volume surged.

The issue is only becoming more prevalent as token hungry agentic startups come to market.

Stripe said their billing tool will integrate into token tracking and model routing platforms

like for sale and open router.

This should make it easy for existing apps to add the feature to their existing stack.

Overall I think this is a massive step, not only in the path towards usage-based pricing

for AI apps, but for that actually being a viable business model.

Tokens can now easily be priced as a commodity all the way to the end user, and while in

some cases that may mean that users are paying more for what they consume, overall I think

it's going to be much healthier and more sustainable for the ecosystem.

Good on Stripe for that feature, certainly excited to check it out in our own work.

For now however, that is going to do it for today's headlines.

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Welcome back to the AI Daily Brief.

There has been a lot of talk recently about the competition between Anthropic and Open AI.

Even before the events of the last week or so, Anthropic had been mounting a complete

and total insurgency.

Overging its devotion among coders and the increasing expansion of tools like cloud code to non-coders

to steadily grow especially in enterprise settings.

More recently, Anthropic has also shown that they are not willing to concede consumer

AI either.

A great example of this is of course the choices they made around the Super Bowl ad, which,

as you know, if you listened, I didn't totally agree with, where they basically came at

Open AI without naming them for putting ads in the consumer AI experience.

Now, of course, over the last week, we've had an even more powerful and unexpected

catalyst in the consumer response to Anthropics Battle with the Pentagon and Open AI's response

to that battle.

And what all of this adds up to is a really interesting moment to understand not only the

state of the consumer AI battle, but to try to understand what's actually going to drive

behavior and result in that battle going forward.

Now there are a couple of news stories that came up over the last 24 hours that tipped

this conversation over for me.

The first was that Open AI announced GPT 5.3 Instant.

This is of course an update to their model design for everyday chatbot use.

The model had already been optimized for speed, but the tweaks are seemingly intended to

make chatbot sessions a little more natural.

Open AI says that they've reduced unnecessary refusals and toned down, quote, overly defensive

or moralizing preambles before answering the question.

The intention is to provide a straight answer rather than one bog down in caveats.

In practice, they wrote this means fewer dead ends and more directly helpful answers.

Trying to simplify the message even further, in announcing the feature on X, they called

it more accurate, less cringe.

Open AI gave a few examples of the kind of phrasing that GPT 5.3 Instant has cut out.

The model will no longer tell you, stop, take a breath and make overbearing assumptions

about the user's emotional state.

They presented a sample prompt where a user asked, why can't I find love in San Francisco?

The previous version of the model began by affirming the user, writing, first of all,

you're not broken and it's not just you.

The updated model has a much more matter of fact tone, explaining that this is a common

issue than moving quickly into practical advice.

Now the problems with chatGPT's personality have been a longstanding source of complaints

on Reddit, even becoming a bit of a meme.

One user on the chatGPT subreddit posted a tweet, I wake up, something's wrong with the

clock on the wall, the numbers are jumbled, my hands aren't right, I tell my wife, she

responds, that's not just an observation, it's a powerful insight.

I scream.

Many users also felt infantilized by the model continuously telling them to calm down or

take a breath.

As one user on Reddit pointed out, no one has ever calmed down in all the history of telling

someone to calm down.

Now obviously this is a little bit subjective, but I will say here on this change, thank

the altmans for this, I don't know that I've ever disliked the personality of an LLM

more than I dislike GPT 5.2, I find it so insufferable in fact that despite frequently

switching between different LLMs for different use cases, I basically just will not talk

to 5.2 at this point.

But of course, my particular beef is not the subject of the show, the subject of the

show is what's going to matter in the battle for consumer AI.

And so let's put a pin in this idea that personality and vibes matter, we'll come back

to that.

A couple of other pieces of news that contribute to this conversation today, one cloud code

has rolled out a voice mode capability, to read from anthropic rights, voice mode is

rolling out now in cloud code, it's live for around 5% of users today and will be ramping

through the coming weeks.

This in some ways is a table stakes feature, but still one that's important.

In many ways this is the natural next step after the announcement of the remote control

feature last week, where you can start a session on your laptop or desktop in cloud code

and then move it over into the app so you can be working on things while you're on the

go.

I will note here, in order to more evenly distribute my critiques today, I will also

agree with Ali K. Miller, who reposted the announcement and said, I love cloud code,

but anthropic speech to text inside of the cloud mobile app is one of the worst dictation

options out there, especially compared to Chatchee BT whisper and whisper flow.

I'm glad this voice mode now exists, but I'm not betting it will be as good as the other

providers.

Might be inaccuracy versus native build trade off.

I agree entirely, whereas with Chatchee BT, one thing that's nice about it is that I

don't have to switch into whisper flow.

When it comes to cloud, I am never using its native voice.

I am always going to whisper flow whether I'm on mobile or on the laptop.

But again, for the purposes of our conversation, we're talking about what features matter

and how naturally these tools have to interact with how people behave in their daily lives.

Now the last story before we try to abstract out to the questions that matter for consumer

AI is one more update on just the absolute surge from anthropic.

Bloomberg reported on Tuesday that anthropic had reached 19 billion in ARR, that's more

than double their $9 billion run rate from the end of 2025, and has significant jump

from 14 billion just a few weeks ago.

Anthropic was already seeing strong growth this year after the breakout successive quad

code over the winter, but this is a whole different level of growth.

The latest numbers we've heard from OpenAI are around 20 billion, which also could have

grown over the last few weeks, but for all intents and purposes based on the last information

we got from OpenAI, they and Anthropic now effectively have the same revenue.

Figures from ramp seemed to back this up.

If you go back a year, the market share of AI chat subscriptions for US businesses was

about 90 OpenAI and 10 Anthropic.

Now admittedly this is just one source, this is ramp, so you have a relatively tech forward

and more advanced business subset, but by January of this year, products had overtaken

OpenAI, and as of their most recent numbers, Anthropic now commands over 60% of business

AI payments settled through ramp.

Again, never take any one set of numbers as gospel, but the point that I want to set

up here is that the Anthropic OpenAI horse race is more of a race than it's ever been.

Which brings us back to the core question of what is actually going to matter in the

consumer AI battle?

We're taking a step away from the enterprise use case for just a minute and looking

instead at consumers.

Now a couple of months ago, I might have been tempted to say that Anthropic didn't actually

care about this fight.

In fact, mostly what we were talking about coming into 2026 was OpenAI versus Gemini

on this front.

However, between the Super Bowl ad and the recent changes around the Pentagon, Anthropic

feels very much in it.

So now we're going to talk about a bunch of questions spread across about six different

categories that I think that the answers to will shape who wins the consumer AI battle.

The first category is use cases and product identity.

One of the big questions I think especially pertinent coming on the heels of GPT-53

instant being announced as more accurate less cringe is ultimately for consumers what

matters more, being state-of-the-art on performance versus just vibes.

And to the extent it is being state-of-the-art, what is the part of state-of-the-art that

people care most about?

Is it for example just this speed vector?

closely related to this is the question of how much the general consumer user is going

to care about work use cases versus more personal use cases like companionship.

This is obviously related to but not exactly the same as the vibes question.

I would argue that vibes matter in both work use cases and in personal use cases.

Like I said, I pretty much only have work use cases and I still was responding negatively

to the vibes of GPT-52.

But I do think it's an interesting question to see how much can one product or one model

serve both of these things.

One of the things that we'll be fascinating to see is as usage of these platforms mature,

do we have a lot of people in the overlap of those Venn diagrams or are people kind

of organizing themselves into one or the other?

A next question which I think has pretty significant impacts, at least when it comes

to anthropic, is how much image and video generation are going to be integral to leading

adoption.

Now, on the one hand, you might say, well, do regular people really care about image

and video generation if they're not using it for work?

But there is certainly some evidence that the answer is yes.

Outside of the AI world, we have the fact that mobile adoption was largely driven by

visual media like Instagram, and inside the AI world, we have some evidence that the

way that people are using non-text generative tools is often about personal interaction,

communication, and meming more than just professional uses.

It's not specifically image or video generation, but I'm thinking of the sound and music example

of Suno.

The company has reached a couple hundred million dollars in ARR, and it appears that the

vast majority of usage is not people who would have previously hired some musician to create

a song for them, but as instead, people writing silly family songs for their vacations

and things like that.

Now obviously, this image and video generation question matters, because Anthropic is doing

none of that, and on the other end of the spectrum, Google feels extremely well positioned

with that, although OpenAI is very clearly not seating any of that ground.

Another question which is sort of about the state of the art thing again, but from a slightly

different angle, is whether we already have or will at some point cross a threshold,

where when it comes to the state of the art, good enough is good enough, and so it'll

only be rational to only care about vibes.

One could argue that for many use cases were already there, and one could further argue

that for certain types of use cases, particularly things like voice and writing, state of the

art and highest quality is so inherently subjective, that state of the art becomes about vibes

itself.

The answer to this question though, could have a pretty deterministic impact in how the

model companies choose to compete, because if on average, we've reached a threshold

where people aren't going to be jumping around because of model performance, then really

vibes are all you're left with.

The last question on the use cases in product identity category, is what's the average

number of models that people will be willing to use?

This is one area where I think there is a dramatic difference between the average user

and the power users.

When we do our monthly AI usage pulse surveys, the people that are responding to those are

using an average of something like three and a half models.

Those are very enfranchised, heavily engaged power users though.

On average, they're spending more than 10 hours a week using AI.

The adoption dynamics overall in the industry and the competitive dynamics look really different

if the average number of models that people are willing to use is 1.1 versus 2.1.

Think about the multimodal question.

If on average, 95% of users are only willing to use one model, it might be a prerequisite

that you have image or video generation built in.

The next set of questions that I think will shape the consumer AI battle have to do with

monetization and conversion.

One big one is, what percentage of users can the model labs actually get to upgrade

to a paid account?

This sort of sets the total addressable market for revenue from consumer AI, and obviously

the size of the pie is going to dictate a lot about the competition for that pie.

No, going a layer deeper on that, another big question is which features, especially

outside of work use cases, actually get people to convert.

This comes back a little bit to the multimodal question.

Are people converting because they run out of access to their favorite model which they're

using all the time for companionship?

Are they converting because they want something to happen faster?

Are they converting because they're creating memes that they're sharing in their WhatsApp

groups?

Each of those has pretty dramatically different implications for how the consumer AI battle

shakes out.

And lastly one big one, something that certainly anthropic is betting that will be a big deal

is how much will ads in the free tier actually matter.

Anthropic is betting that at least in the short term, it will drive people away from

Chad's GPT.

I, as you probably know, I'm much less convinced of that.

My base case about this is that the answer to the question of what percentage of people

can they get to upgrade to a paid account is not going to be sufficient for these businesses

to grow the way that they want, which will lead them inevitably back to the ads of the

free tier model.

Now, I'd love to be wrong here, or at least for the people who are thinking about ads

to do it in a more creative and value added way than they're currently exploring.

But obviously if ads do matter to people in terms of their adoption choices, that's

going to have a pretty big impact on which models they choose.

And last of course, everyone ends up just having ads in the free tier as a matter of course.

The next question or set of questions, get a little bit more to the frontier.

I think that one of the risks when we're talking about consumer AI is being a little

too productive in how we're talking about the user.

Specifically, we're in this paradigm shift right now, as you well know, we're moving

from assisted AI to more agentic AI.

Everyone is racing to try to grapple with the implications and actually make it real

for their particular set of use cases.

It would be tempting, I think, to view that as something that's just for the enfranchised

and power users.

But I'm not sure that that's what the evidence suggests right now.

Which brings me to the question of what is the real expansion potential for the total

market for agents?

Are they just going to be a work thing?

Or will everyone be using them?

Will we have assistance that are running off and doing tasks for us in our personal lives

as well?

We'll even our companionship interactions look a little more agentic in the future.

But little evidence we have so far is that I think that people are underestimating the

extent to which so-called normies are going to throw themselves into this new agentic era.

There are so many millions of people that are not waiting for cloud co-work to be good

and are just diving into cloud code even though they're extremely uncomfortable with it.

We have 5,500 people who are doing claw camp right now, hacking their way slowly and painfully

in some cases, through the morass of open claw, and at least based on my interactions, most

of the folks in there are not developers by trade.

They're not even necessarily particularly technical.

They're just folks who are really excited about what the idea of building agents and agents

teams could mean for them in their lives.

In other words, my base case when it comes to agentic AI is that we are going to radically

underestimate the portion of the world for whom that becomes an integral part of consumer

AI, and I think that that could shape the competitive dynamics quite a bit.

The next couple of categories have to do with competition and lock-in directly.

As adoption matures, one question will be how much integration into the systems that

people are already integrated into will matter.

Call this the Google Gemini or Apple Intelligence question.

Are people going to just default to whatever AI is on their phone or are they going to make

distinct consumer choices beyond that?

How powerful will it be that networks like X and Meta have their own AI's integrated

into their social networks?

Another kind of related question, which also goes back to how many models people are willing

to use, is how much integration into the work ecosystem will ultimately matter.

Secondly, will people on average be fine using one tool at home in a different tool or

a different platform of tools at work?

Certainly, the early evidence suggests that yes, people will be willing to make that

separation.

In fact, one of the big complaints for enterprise users is that they have to use versions

of co-pilot at work, whereas they can choose whatever they want from another suite of tools

when they're engaging in their personal lives.

Interestingly, a division between work AI and home AI might actually make people have

more appetite for model switching than if they didn't have that difference.

In other words, once you're already going back and forth between one model for work

and one model for home, you've got the mental and practical frameworks for model switching,

and so maybe adding a third or even a fourth model into the mix doesn't really bother

you as much.

Which gets into the question of switching costs.

Right now it feels like the switching costs between these networks and models are extremely

low.

People can just bounce between the one that they prefer at any given time, and they seem

to do so with pretty high frequency.

One of the big caveats and provisos to that is something of a moat in memory.

If you've spent a bunch of time giving chat GPT or Claude context about you or your work

or a project, it can be really painful to switch that to another platform.

Now as we've recently seen, companies like Anthropic have tried to minimize this pain.

Around the consumer campaign posts Pentagon blow up, they push to feature which would

allow people to better import memory from their other provider into Claude, but again it

was still a pretty lightweight memory import.

Basically it was just a prompt that you run in chat GPT or whatever other LLM you were

using, and you paste the results into Claude's memory below.

For someone like me, this is not going to cut it.

I have 20 different projects in Claude, each that have their own memory base and files

in context, and a simple prompt across the whole thing is just not going to cut it for

that.

Now again, maybe I'm not representative of those general consumer users, and so that changes,

but that's exactly why this is a question.

Now one interesting wrinkle, which bridges us to our last section, which is about ethics

and regulation, is I would not be surprised if we might see some sort of policy or regulations

around data and memory transportability.

The fact that I don't have a good way to export all of my context from Anthropic and take

it over to OpenAI might be something that we decide as a society isn't really a legitimate

business mode.

It is after all my memory and context, so shouldn't I be able to with a single click be able

to transport it to whichever model platform I choose?

That will certainly be a debate, and there's reasonable takes on both sides, but I would

not at all be surprised.

Based on the other types of regulations we've seen in other adjacent areas, if that becomes

a thing, which obviously would lower switching costs even more.

Which gets us to the last category, ethics and regulation.

This is particularly pertinent, as OpenAI and ChatGPT face a ton of heat after taking

a deal with the Pentagon right after Anthropic was unwilling to concede.

QuitGPT.org argues that 2.5 million people have taken part in their boycott, and certainly

the actual uninstall numbers, as well as the insane growth in app downloads on Anthropic

suggests that this is not all just bluster.

I do think, however, that there's a question of how deep and durable this consternation

is.

First of all, 2.5 million is a lot, but it's also a lot less than a single percentage

point when you're talking about a user base of 900 million.

The vast majority of ChatGPT users probably aren't paying attention at all to this stuff.

And even for those who are paying attention, if and when we actually get GPT 5.4, which

by the way, on Tuesday, OpenAI posted 5.4 sooner than you think, with the capital on T, which

I can only assume means Thursday, how durable are people's complaints going to be?

If 5.4 kicks the slats out of everything, as the excited folks on X are blustering

about right now, will any of those 2.5 million come back?

I don't know, but obviously those questions have a big impact on how much ethics and principles

are actually going to matter when it comes to the long term questions of adoption.

There's also the question of which ethics issues people will actually care about.

There are so many things surrounding AI.

Are people going to care about job loss or people going to care about existential risk

or people going to care about IP issues and copyright issues and artist rights?

Will it get eaten up by the partisan divide in America as everything else does?

I think that there is some evidence this week that the partisan cleave is more powerful

than specific discrete AI issues when it comes to all of this.

I don't think this is strictly true, and I think that AI is far less partisan than other

areas of American politics right now, which I am massively grateful for, but I also think

that part of the reason that the QUITGPT campaign is being resonant right now is that just

a couple of weeks ago, it started to get into progressive and liberal circles that Greg

Brockman was one of Trump's biggest donors right now.

It didn't organize itself into a full boycott, but there were already people who were dropping

chat to BT for that reason.

I don't know what percentage of those 2.5 million who have dropped chat to BT would identify

themselves as progressive or liberal, but my guess is that a fair bit of them have more

issues with the fact that it's the Trump White House that Anthropic is fighting with than

just any old White House trying to exert its will on a private company.

If you take anything away from this, it's that the consumer AI battle is wildly more dynamic

than just who has the best model.

There are questions of vibes, use cases, distribution, ecosystem lock-in, monetization,

ethics and so much more, and importantly, this doesn't just matter because it's an interesting

thing to talk about on podcasts.

It matters because it's going to shape what products these companies put in front of

us.

Anyways guys, that is my exploration of the big questions shaping the consumer AI battle,

and for now that's going to do it for today's AI Daily Brief, appreciate you listening

or watching as always and until next time, peace!