Loading...
Loading...

As AI products race toward deeper personalization, the most important competition may be over who controls user context rather than who has the best model. This episode explores how Google’s Gemini personal intelligence, Claude Cowork’s desktop access, OpenAI’s memory-first product strategy, and Apple’s still-untapped device data all fit into a broader battle to own the user relationship, while also questioning how valuable personalization really is for different types of AI users. In the headlines: mounting IPO speculation around OpenAI and Anthropic, Microsoft’s quiet but costly shift toward Anthropic models, OpenAI’s $10B Cerebras compute agreement, and a messy talent reshuffling at Thinking Machines Lab.
Brought to you by:
KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. https://www.kpmg.us/AIpodcasts
Zencoder - From vibe coding to AI-first engineering - http://zencoder.ai/zenflow
Optimizely Opal - The agent orchestration platform build for marketers - https://www.optimizely.com/theaidailybrief
AssemblyAI - The best way to build Voice AI apps - https://www.assemblyai.com/brief
LandfallIP - AI to Navigate the Patent Process - https://landfallip.com/
Robots & Pencils - Cloud-native AI solutions that power results https://robotsandpencils.com/
The Agent Readiness Audit from Superintelligent - Go to https://besuper.ai/ to request your company's agent readiness score.
The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614
Interested in sponsoring the show? [email protected]
Today on the AI Daily Brief, why the biggest battle in AI is the battle for your personal
context.
Before that in the headlines, could this be the biggest year for IPOs in history?
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.
First of all, thank you to today's sponsors, Assembly, Landfall, Zencoder, and Superintelligent
to get an ad-free version of the show, go to patreon.com slash AI Daily Brief or you can
subscribe on Apple Podcasts, if you are interested in sponsoring the show.
You can set us a note at sponsors at aidelebrief.ai and finally also at aidelebrief.ai you can
find out about all the other goings on in the world of AIDB, including AIDB Intel, our
new operators community, our AIDB New Year's self-guided skills course, and the new AIDB
Intel service which will have much more info coming in the next week or so.
But with that out of the way, let's dive in.
Welcome back to the AI Daily Brief headlines edition, all the daily AI news you need in
around 5 minutes.
We kick off today with two stories that I discussed in my 2026 predictions, one where it looks
like I might be wrong, one where it looks like I might be right.
The one where I might be wrong is that, contrary to my base case, that neither OpenAI or
Anthropic ultimately go public in 2026, the New York Times finds a lot of evidence that
they are getting ready.
They wrote, andthropic in OpenAI have taken early steps to go public, people familiar with
the companies said, and SpaceX Elon Musk's rocket company has interviewed banks to lead
an IPO, according to two people with knowledge of the situation.
Now, these three companies are already valued between $350 and $800 billion each, add
an premium for the public offering, and we can easily see multiple trillion dollar IPOs
this year.
That is extraordinarily rare.
The only real comparison at those levels are the $1.7 trillion valuation for the Saudi
Ramco IPO in 2019.
No tech startup has ever come close.
Morgan Stanley's Eddie Maloy said, we're going to get into a period of potentially unprecedented
IPO deal sizes, but we are confident they're executable given the scale of these companies
and the investor interest.
Now, Maloy in this case is referring to concerns that the public markets can't handle
deals of this size.
As part of all of the AI bubble chatter, there's been talk that investment banks might force
existing shareholders into a rolling unlock rather than the more usual six-month cliff
to stagger the selling.
shareholders expect demand to be so hot that selling won't be an issue.
Certainly, it is the case that despite claims that the current stock market is starting
to resemble the dot-com boom, we haven't seen anything comparable to the thousands of
IPOs for emerging tech companies that mark that era.
What that means is that for most investors, this would be the first time that they got
access to pure play companies developing AI.
Jeremy Abelson, a VC at Irving Investors, said, in two decades, I haven't seen private
companies that are this meaningful and are this impactful.
Not only are they bigger and more relevant, but they're incredible companies with numbers
that we've never seen before.
You can expect analysis of these big three to dominate finance discussions over the coming
year, with very loud opinions on both sides of the argument.
Some compared this moment to the record-breaking Facebook IPO in 2012.
That listing both solidified social media as a major category, and was also seen as
an utter catastrophe in the market, with the Wall Street Journal calling it a fiasco.
The stock was down 15% after a week and took 14 months to trade above its IPO price.
At the time, it was the third largest IPO in history.
Although in the current frame of reference, its $90 billion valuation was exceedingly modest.
Jeff Thomas, the head of listings at NASDAQ, said, when these mega deals happen, it takes
some of the air out of the room.
You want to try to get ahead of it.
Others noted that transparent information on the leading AI companies could diffuse the
bubble chatter.
Said notable capitals, Jeff Richards, there is such a big information gap right now.
The biggest positive for this entire market would be if a bunch of these companies went
public and people could actually see the numbers.
My argument for why I didn't think that open AI and anthropic would ultimately go public
this year is a public market reporting as a total pain in the butt, especially when they're
in the category that is already the most under scrutiny, and b, I think there is plenty
of capital still available in private markets for their financing needs.
Now b, might be more dubious that I'm giving credit for, not because there isn't private
capital, but just because the scale of the need might be so huge, and there is also, of
course, a competitive dynamic thing.
There are lots of good reasons, for example, for anthropic to want to scoot in ahead of open
AI, and lots of good reasons for open AI to not want to let that happen.
For that reason alone, we may be headed into a very big year when it comes to public markets.
Now the prediction I did a little bit better on then is one where I said that anthropic
was going to continue to be hard to displace when it came to its lead in coding, and that
I thought that Microsoft was likely to get much closer to anthropic over the course of
the year.
According to the information, Microsoft has indeed quietly become one of anthropic's largest
customers over recent months.
As of July last year, Microsoft began using anthropic to power coding agents in GitHub
co-pilot, but the big shift began in September when open AI and Microsoft agreed to the
emicable end of their exclusive partnership.
Microsoft quickly announced they would add support for anthropic's models within their
co-pilot products.
The new multi-model version of co-pilot routes each task to the most appropriate model,
and for many of the tasks in Microsoft's productivity suite, the right choice seems
to be an anthropic model.
The report stated that Claude Sonnet 4.5 has a 15% performance advantage over GPT-40
in agent mode for complex Excel tasks, although why the hell anyone's using 40 is beyond
me, while the super long context window of Claude Opus 4.1 is being used for mass summarization
and analysis tasks.
High Coup 4.5 is also seeing heavy use due to its cost and speed advantage for smaller tasks.
Business customers didn't have to upgrade or change anything in their plans, but as
of last week, they're now receiving access to anthropic models by default.
The information reports that Microsoft has spent more than 40 million per month with anthropic
starting in July, a $500 million annualized pace that is likely a lot higher now, given
that the models are seeing more use.
In addition, the report states that Microsoft Cloud staff have been incentivized to sell
anthropic products, and finally deepening the new ties, andthropic will reportedly work
with Microsoft to develop new cloud power features for co-pilot over the coming months.
A liquid on Twitter said what I think a lot of people feel, andthropic and Microsoft
was the partnership that made sense all along.
Speaking of partnerships and OpenAI, chip startup cerebris has landed a $10 billion compute
deal with the company.
The three-year deal will see cerebris provide OpenAI with 750 megawatts worth of AI inference
compute.
A press release said that OpenAI planned to integrate cerebris's chips into their broader
computing network to provide faster response time.
Cerebris CEO Andrew Feldman posted, this has been a decade in the making.
Deployment begins in early 2026, and when fully rolled out, it will be the largest high-speed
AI inference deployment in the world.
He claimed cerebris's uniquely designed chips are now able to deliver 15x faster inference
without sacrificing model size or accuracy.
He added, as models grow more capable, speed becomes the bottleneck.
Those systems limit what users can do, how often they engage, and whether AI becomes
infrastructure or remains a novelty.
Matthew Berman wrote,
�I�ve always wondered why OpenAI didn�t use Grocker cerebris, they are so fast.
Now, we know why GROC was bought by Nvidia.
Everything is moving to specialized chips.
Revenue is made at inference.
ChATGBT is about to be 100 times faster.
Now, lastly today, we stay on OpenAI but move to some serious industry psychodrama.
A trio of leading AI researchers are returning to OpenAI amid allegations of corporate espionage
On Wednesday night, we had dueling tweets.
Former OpenAI CTO and now CEO of Thinking Machines Labs, Mirror Morati, wrote, �We have
parted ways with Barrett's Oath.
Sumith Gentala will be the new CTO of Thinking Machines.
He�s a brilliant and seasoned leader who has made important contributions to the field
of AI for over a decade, and he�s been a major contributor to our team.
We could not be more excited to have him take on this new responsibility.
Meanwhile, about an hour later, OpenAI CEO of Applications, Fiji Simo tweeted, �excited
to welcome Barrett's Oath, Luke Metz, and Sam Schoenholz back to OpenAI.
This has been in the works for several weeks and we�re thrilled to have them join the
team.
Barrett will report to me, Luke and Sam will report into Barrett, more to come on what
they�ll focus on soon.�
Now by way of background, these three left OpenAI in late 2024 alongside CTO Mirror Morati
as part of a mass exit as of talent.
They were pivotal in the subsequent founding of Morati's Thinking Machines lab.
Zof and Metz were in fact listed as co-founders of TML, with Zof also receiving the CTO title.
This is a significant personnel move that could have implications for the course of both
companies.
CTO first joined OpenAI in 2022 to serve as their VP of research.
Prior to that, he was at Google DeepMind for six years.
At OpenAI, he built the post-training team from scratch with John Schullman, who also
left a co-found TML.
That team yielded the O1 model and helped kickstart the new reasoning paradigm.
Metz and Schoenholz are also leading experts on post-training and reinforcement learning.
Now for TML, it�s very difficult to know how bad a sign this is.
The departure of two co-founders and another senior researcher obviously isn�t a great
indication of how things are going.
Metz Twitter poster signal wrote, �So like Thinking Machines completely imploded today?
Someone DM me the T please?�
And yet at the same time, adding to the intrigue, Kylie Robinson of Core Memory reported
the story with a different twist, writing, �Thinking Machines has terminated its CTO
bear itself due to unethical conduct according to two sources familiar with the matter.
CEO Miramarati announced the news that an all-hands with employees today.
Max F. followed up with this angle for wired, writing that his sources at TML said that Zof
had shared confidential company information with competitors.
The timeline was laid out in a memo written by Fiji Simo on Wednesday and shared with
wired.
Zeph wrote, �According to the memo from Simo, Zof told Thinking Machines CEO Miramarati
on Monday he was considering leaving.
He was then fired on Wednesday.
Simo went on to write that OpenAI doesn�t share the same concerns about Zof as Miramarati.
I don�t know man, obviously from outside it�s hard to tell exactly what�s going on, but
from a sheer talent perspective, you got to think it was a good day for OpenAI.
That however is going to do it for today�s headlines?
Next up, the main episode.
If you�re building anything with voice AI, you need to know about Assembly AI.
They�ve built the best speech to text and speech understanding models in the industry,
the quiet infrastructure behind products like granola, dovetail, ashby, and cluely.
Now, as I�ve said before, voice is one of the most important modalities of AI.
It�s the most natural human interface, and I think it�s a key part of where the next
wave of innovation is going to happen.
Assembly AI�s models lead the field in accuracy and quality so you can actually trust the data
your product is built on.
And their speech understanding models help you go beyond transcription, uncovering insights,
identifying speakers, and surfacing key moments automatically.
Its developer first, no contracts, pay only for what you use, and scales effortlessly.
Go to assemblyai.com, slash brief, grab $50 in free credits, and start building your voice
AI product today.
If you�re listening to this, you already know how fast AI is writing the rules for innovation,
disruption, and value creation.
And this new era demands a new kind of patent law firm.
Landfall IP was built from the ground up to operate differently, orchestrating how human
expertise and AI work together for better patents at founder speed.
Created by world-class patent attorneys who saw better way, Landfall IP lets AI execute
the repeatable while attorneys elevate to create the exceptional.
Landfall isn�t adapting to AI, they were built for it.
Have a new idea?
Try the discovery agent for free.
It�s a confidential tool that helps innovators synthesize their inventions and instantly
see patentable insight.
Visit landfallip.com to learn more, that�s landfallip.com.
If you�re using AI to code, ask yourself, are you building software or are you just
playing prompt roulette?
We know that unstructured prompting works at first, but eventually it leads to AI slop
and technical debt.
Enter Zenflow.
Zenflow takes you from vibe coding to AI first engineering.
It�s the first AI orchestration layer that brings discipline to the chaos.
It transforms free-form prompting into spec-driven workflows and multi-agent verification, where
agents actually cross-check each other to prevent drift.
You can even command a fleet of parallel agents to implement features in fixed bugs simultaneously.
We�ve seen teams accelerate delivery to x to 10x.
Stop gambling with prompts.
Start orchestrating your AI.
Turn raw speed into reliable production-grade output at zenflow.free
Today�s episode is brought to you by super intelligent.
Super intelligent is a platform that very simply put is all about helping your company figure
out how to use AI better.
We deploy voice agents to interview people across your company, combine that with proprietary
intelligence about what�s working for other companies, and give you a set of recommendations
around use cases, change management initiatives, that add up to an AI roadmap that can help
you get value out of AI for your company.
But now we want to empower the folks inside your team who are responsible for that transformation
with an even more direct platform.
Our forthcoming AI strategy compass tool is ready to start to be tested.
This is a power tool for anyone who is responsible for AI adoption or AI transformation inside
their companies.
It�s going to allow you to do a lot of the things that we do at super intelligent, but
in a much more automated, self-managed way, and with a totally different cost structure.
If you are interested in checking it out, go to AIDailyBrief.ai slash compass, fill out
the form and we will be in touch soon.
Welcome back to the AIDailyBrief.
Today we are talking about Google Gemini�s big upgrade that they are calling personal
intelligence.
Yesterday, they announced the very obvious and yet very useful ability to connect Gemini
with all the information from other Google apps that you interact with, like Gmail,
photos, search, YouTube, all of course in an effort to help make Gemini more personalized
for the individual user.
What�s interesting is that I believe that at Core, you can view almost every single
move being made in and around consumer AI as in some way a battle for personal context.
So let�s look at what I mean.
Big news from earlier this week was the announcement of Clawed Co-Work.
It�s basically Clawed Code, but simplified in a way that it�s designed for non-technical
users.
You don�t have to deal with the terminal anymore, it lives right inside your Clawed Desktop
app, and it allows you to do the types of things that people have been using Clawed
Code for outside of coding.
Now the big thing that has made Clawed Code and now Clawed Co-Work powerful is that it
has access to a unique set of context, which is the stuff on your desktop.
What makes it different than just the Clawed Chat window or the ChatGPT window or the
Gemini window is that instead of having to upload the context that�s relevant for
any particular thing you�re trying to do, you just point it at the relevant part of
the computer.
Now of course, in addition to having that better context, Clawed Code can also do things
and interact with your desktop making it more agentic, but that power comes from its
ability to access everything on your computer.
And yet, even with that, a lot of the issues that people have discussed when it comes
to Clawed Code work over the last few days, which admittedly are more likely having to
do with the fact that it was built in the 10 days previous, isn�t around connecting
other types of context.
While Clawed Code and Clawed Code have access to what�s on your machine, if you live
in the modern world, there�s going to be lots of other data sources in places where
your data lives that are not just on your desktop.
And for that, Clawed gives you access to things via what they call connectors.
Connectors are ways to link things like Google Drive, obviously powered by the Model Context
Protocol, and in the first couple of days after Clawed Code work went live, a lot of people�s
challenges have been in and around making those connectors work.
The point being in some ways that we are so hungry for personal context that just having
access to our full computers isn�t enough, we still need access to everything that exists
on the web as well.
So okay, we�ve repositioned Clawed Code work in Clawed Code as powerful because of the
way they give you unique access to your desktop context.
How are the other things that AI companies are launching right now, also in some way about
this battle for personal context?
I would argue that when it comes to chat GPT, a huge anchor to their strategy has always
been to try to leverage the fact that because chat GPT was many people�s default, it has
a huge amount of personal context in the form of past chats.
And when you view everything in the battle for personal context, all of a sudden open
AI strategy to add more and more applications all of the time with an incredible shipping
velocity starts to make a little more sense.
They are trying with each new app release to get more personal context, which makes the
switching costs of leaving and going to another AI service more and more costly in terms
of that lost context.
For months now, folks have been talking about how memory is the next big moat and I think
that that�s dead on.
Now bringing it back to things that have been released recently, so far from open AI,
the biggest product that we�ve got in January is the introduction of chat GPT health.
It�s a dedicated health experience inside the app whose entire purpose is to collect
a huge amount of personal health context and organize it in a single place that makes
it accessible to the AI.
And their announcement post they wrote, today health information is often scattered across
portals, apps, wearables, PDFs and medical notes, so it�s hard to see the full picture,
and people are left to navigate a complex health care system on their own.
Now as they point out, people are already using chat GPT to help navigate all this, but
now they are allowing you to port all of that context in.
And they are really trying to pull that health context from everywhere it lives.
Just a few days later, we got Anthropics answered to that in their Cloud for Healthcare.
A big part of that Cloud for Healthcare announcement was about connecting personal health data.
The announcement came with a bunch of new connectors rolling out specifically for that
type of personal context.
I would even argue that Grox big play, outside of having Elon for fundraising, and for building
the biggest supercomputers in the world, is once again around personal context.
The unique personal context that Grox has access to is everything that happens in and around
X-slash Twitter.
Which for those of you who aren�t on X-slash Twitter might not seem like it matters,
but for those of us who are and who have been for a very long time is a very significant
part of personal context.
Okay, so now you are starting to see all of these different moves through the lens of
personal context, but Google�s latest announcement isn�t in some ways the clearest yet.
Yesterday�s CEO Sundar Bachai tweeted, answering a top request from our users were introducing
personal intelligence in the Gemini app.
You can now securely connect to Google Apps for an even more helpful experience.
Personal intelligence combines two core strengths, reasoning across complex sources and retrieving
specific details, e.g. from an email or photo to provide uniquely tailored answers.
It�s built with privacy at the center and you choose exactly which apps to connect
with the connected app settings off by default.
Some of the examples that Google gives about how this might be useful are really concentrated
on day-to-day life.
This is not about work.
In their announcement thread they wrote, �ever need to buy parts for your car but don�t
have the info handy, ask Gemini to recommend tires for my car.
By referencing connected apps like Gmail and Photos, it can understand your car�s making
model and even the types of trips you take to give recommendations of tires and info like
your license plate number to make your visit to the auto shop go more smoothly.
When a user asks for recommendations around travel, instead of it being generic lists,
the specific travel dates that can be found in Gmail, plus other evidence like, in their
example, your love for nature photography found in Google Photos, lead to more personalized
recommendations.
People's first instinct was that this was a big deal and that in many ways it was inevitable
but kind of a killing blowplay from Google, AI YouTuber Matthew Berman writes, �Gemin
I will now be my daily driver AI for the next few weeks all because of personal intelligence.
Google would have never allowed this kind of feature to release just 18 months ago.
It would have been too nervous, too much red tape, but now they got out of their own
way and allowed users to choose.
Google is so well positioned to win AI.
Apple, where you add.
A gosh group to writes, Google just revealed the AI mode nobody can replicate.
Every AI company is racing to build memory and personalization.
Google connects to a decade of your Gmail threads, every photo you�ve ever taken, your
complete YouTube watch history, and every search query you�ve made since 2005.
The question for every other AI company, how do you compete on personalization when your
competitor has the user�s entire digital life and you�re starting from a blank conversation?
I think there are a couple answers to this.
First of all, I do think it�s important to note that while it does seem obvious that
this would make AI better for a variety of use cases, I don�t think we yet have enough
evidence to note exactly the full complexity of the way that AI gets used over time.
To be clear, I am far from the average consumer and user of AI, and yet I do represent a type
of user of AI and I couldn�t care less about this if I tried.
For my work-related use cases, I care about the quality of AI strategic thinking, its
ability to process and articulate multiple angles around the same decisions, how good it
is at accessing other types of data, how good it is at analyzing types of data I give
it access to, how good it is at building the things that I need.
There�s not a universe in which I�m switching models because I can get better travel
recommendations or need a shortcut way to figure out what my license plate is.
And to be clear, this is not at all a knock on these new features from Google, nor an
argument that I�m anywhere near the normal consumer.
My point is solely that when it comes to these big bold claims that Gemini is killing
everyone because of this, I think there�s going to be a lot of types of different AI
users, all of whom have different types of priorities.
Still, let�s assume that this type of personalization is really valuable for many, if not
most consumers.
While another path is to ask who else has access to that data, which brings us back to Matthew
Berman�s question, Apple, where are you at?
When Apple announced Apple Intelligence way back when, it was all with this same argument.
The pitch was simple, helpful, day-to-day use cases that took advantage of the context
that Apple had about you because it powers all of your devices.
Now, obviously, it has not delivered on that promise.
One of the big takeaways after Google I.O. last year, in fact, was that Google had basically
shipped everything that Apple�s AI wanted to do, and now, of course, Gemini is going
to actually power Apple Intelligence.
And yet, Apple still does have an enormous amount of personal context that others don�t
have.
But for example, does not have your iMessages, and for iPhone users, iMessages tend to represent
dozens of gigabytes of personal context that is extraordinarily valuable, and frankly,
when it comes to a personal level, more valuable for many use cases than the stuff that�s
in your Gmail.
Apple also has something else, ownership of devices that operate in the physical world.
And I think when you start to view everything through the lens of this battle for personal
context, open AI�s hardware decisions start to make a little more sense.
Software allows them to go after a very specific type of personal context, which is the personal
context of how you interact in the physical world.
One thing that Apple did last year that did capture people�s attention was the new
live translation feature they announced for Apple AirPods.
Unlike many other form factors for AI devices, AirPods are something we already interact
with.
It�s not at all weird or abnormal to talk to someone who has AirPods in.
And so, to the extent that AirPods can become a starting point for AI to interact with
your physical experience, it could unlock a whole additional set of that personal context.
This is why it wasn�t all that surprising when we found out that it seems like at least
one of the form factors that open AI and Johnny Iver are exploring is something at least
tangentially related to an AirPods.
Make no mistake about it.
Google giving Gemini access to all of this information is a major inflection point and
a major upgrade in their positioning when it comes to the consumer AI race.
But it�s still early innings that a lot of battles yet to be fought, and a lot of
personal context still needs to be organized.
For now that is going to do it for today�s Aideally Brief, appreciate you listening or
watching as always, and until next time, peace�
The AI Daily Brief: Artificial Intelligence News and Analysis
