# Companies Are Spending 5x More on AI Now — Transcript (2026-11-06)

https://aidailybrief.ai/e/2026-11-06 · Listen: https://pod.link/1680633614

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[00:00:00] 

If you survey just the mainstream media, you might have the impression that business adoption of AI was in some way impaired or slowing down. 

the stories that make the news about enterprise adoption of AI seem primarily right now to be about companies restricting how much their employees can spend on it. Like Apple, for example, cutting the amount that engineers can spend per day, which was a story this week And yet, when you take a step back, not only has AI adoption in the business sector continued unabated, 

The speed at which it is growing is actually increasing, defying all logic and expectation as it starts from a bigger base

The story of enterprise AI adoption this year is very clearly the story of agents, and that's what we're looking at on today's episode

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



All right, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Cdata, Granola, and Blitzy

To get an ad-free version of the show, [00:01:00] Go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts. And if you want to learn more about sponsoring the show, send us a note at sponsors@aidailybrief.ai.

One more quick shout-out before we dive in

our newest premium AI training program is starting soon. It's called the AI Champions Bootcamp. It's three weeks long, and this one has six live sessions led by Nufar Gaspar, frequent contributor to the show and will not just help people use agents more effectively, but have the skills and resources to bring that use to their entire teams. you can get all the information for that program at besuper.ai. 

And the bootcamp begins on November 16th 

Welcome Welcome back to the AI Daily Brief headlines edition, all the daily AI news you need in around five minutes. Well, it looks like the markets got a little bit annoyed at my overly optimistic analysis, because 

yesterday, AI stocks plummeted in their worst drawdown since 2020

Overall, Overall, the Mag Seven lost a combined trillion dollars in market cap, with Nvidia falling nine point three percent and Microsoft dropping [00:02:00] three point six percent. The pain was even greater in the AI supply chain, where Micron lost seventeen point five percent and SanDisk plummeted twenty-two point three percent.

The S&P five hundred fell by four point four percent, and the Nasdaq collapsed by six point four percent in the largest single day drop for the major indices since March of twenty twenty.

Fatalistic takes were all over X Kevin Xu from Alpha AI wrote, "We're effed. Absolutely effed. The AI trade is over. We all top blasted and holding bags now."



Tech investor Steven Sinofsky added, " This is not the AI trade or AI lost the election trade as you might read or see, but rather the what happens when US AI is not going to happen trade." In fact, one of the most striking parts of the drawdown was how broad-based it was.

Completely unrelated stocks like Walmart and ExxonMobil took massive hits alongside tech

Some argued that semiconductors are now cheap, with Nvidia trading at a thirteen x multiple, making it one of the quote unquote cheapest stocks on the market



then then again, investor Jeff Park pointed out that boom and bust semiconductor stocks always look cheap as they head into a [00:03:00] bust, commenting, " Nothing outs a newbie faster than valuing semis on price to earnings."

Now, the curious part is that nothing substantial changed between Wednesday and Thursday that would explain the sudden collapse And whenever that happens, when there's no narrative explanation, we have to look towards market structure and market dynamics as the driver And indeed, Fundstrat's Tom Lee told CNBC, " I think there are stories of some large hedge funds actually being liquidated now."



And according to reports, Walai has blown up and returned their capital. Lone Pine is also rumored to have collapsed, and Millennium is said to have pulled one point five billion in leveraged capital from two outside managers. Bloomberg's Nishant Kumar wrote, " I have covered hedge funds for nearly sixteen years, and I can't remember a day like today."

Nicholas Mugali from TMT Research wrote, " If you're wondering why this sell-off is so aggressive, this is what forced selling looks like. It isn't happening in one sector. It's literally everywhere you look at once, from financials to AI infrastructure, and the best businesses are falling hardest because they're the easiest things to [00:04:00] sell when funds need cash.

The fundamentals are intact, but every chart looks broken right now and has been broken for a couple of months."

In short, rather than investors waking up on Thursday and deciding to sell everything, it looks like Wednesday's drawdown in semiconductors was enough to trigger a broad de-leveraging across the hedge fund industry. That could mark the end of the AI bull market, 

Or it could be a temporary blip like we saw when tariffs were unveiled last year



Now Now with markets in free fall, rumors are swirling that the Anthropic IPO might get pulled. Previous reports said that Anthropic was aiming to get out before Thanksgiving, which means their roadshow would need to begin next week Obviously, a frontier AI lab is going to be a harder sell under these market conditions, and prediction markets are beginning to price in a delay or even a cancellation.



On Thursday, odds of the Anthropic IPO before Thanksgiving fell from around 85 to 60%, and the odds they get out by the end of the year have fallen from 95% to 66% over the past week. Thinly traded secondary markets are still pricing the company at around one point six trillion.



But according to The Wall Street Journal, [00:05:00] institutional investors are increasingly skeptical of the price. They wrote that three major investors who plan to participate in the IPO say the valuation will need to come down, with one arguing it needs to be closer to one trillion.

Either that or Anthropic might need to trim their reported hundred billion dollar target for the fundraising

This is obviously a big shift from where they were in August when investors were leaking a two trillion dollar target in the press

Still, that same article in the WSJ reported an eighty billion dollar run rate for October, up from sixty-five billion reported for the end of July



so can Anthropic slough off the market turmoil?



I think we'll have a better sense once the hedge fund blowup stops ricocheting around the market



but for now, something that was idle chatter is getting a lot more attention.

Now, speaking of Anthropic, outside of markets maybe the biggest discussed story right now

is Meter's release of their first independent safety report for Anthropic, which discloses a major acceleration in AI-led research. Last month, Anthropic announced that they'd onboarded Meter as their first independent evaluator with employee-level access.

The idea is, of course, to give the public better visibility into Anthropic [00:06:00] safety practices in the wake of so-called rogue agent incidents. The key finding of the report was that the pace of improvement for Anthropic's models has roughly doubled over the past year. Meter concluded that the acceleration was due to AI being used in research efforts.

They wrote, " We were told that R&D has accelerated primarily via AI assistance rather than fully autonomous contributions, but there are some signs that fully autonomous contributions will play a larger role soon."

If AI systems started to significantly accelerate further AI development, the report stated, this could lead to a sudden emergence of extreme and risky capabilities. Meader noted that they were given access to internal information that surpassed their expectations. This included analysis of three unreleased models trained since July.

The most recent model was completed in September or October and continued the trend of accelerated improvement observed earlier in the year.



Anthropic co-founder and responsible scaling officer Sam McCandlish signed up for X this week to discuss the report. He commented, The The speed of progress in model capabilities is one of the most important questions in AI safety, and Meter's work [00:07:00] here provides the public with an independent external view into how this is happening inside Anthropic Sam acknowledged criticism that Anthropic redacted some of the most important evidence that Meter used to come to their conclusion, But said that Anthropic intends to provide a supplemental report on their own analysis in the coming weeks

Now, in terms of reactions, the AI safety folks were rattled by the report, claiming it accelerated their timelines.

Ryan Greenblatt from Meter wrote, " This report on AI R&D automation is a big update towards ASI within one to two years."

Nathan Calvin, the general counsel for Encode AI, called this a, quote, "extremely unnerving update."

New New York Assemblyman Alex Bores argued that there's no time to wait, writing, " "Congress Congress should ban RSI this year if possible

But others were a little skeptical of the methodology, with open model researcher Nathan Lambert noting, " A lack of good enough benchmarks is a bottleneck, at least for the type of RSI described as an intelligence explosion."



now some rehash the criticism that Meeter is closely affiliated with Anthropic at least in ideology, if not through actual financial and personnel [00:08:00] ties

And whatever you think of those arguments, the report does highlight the issue with presenting METER as an external evaluator. One of the key points of appointing Meador was to address public concerns about cybersecurity breaches from the labs METER's report, though, only mentions cybersecurity in terms of the models getting more advanced and doesn't touch on sandboxing or other safety measures.

It's essentially non-responsive to concerns raised over recent months, instead assuming that RSI is the only risk that matters.

now it's perfectly reasonable for Meter to decide that that is the only concern that they really care about

But if that's the case



having only meter here means a very incomplete independent auditor suite



Now closing out with some feature and product updates. Anthropic has relaunched Claude for personal finance with the ability to connect to your bank account. This is read-only access via Plaid, which is common across the personal finance industry.

So theoretically shouldn't be a huge concern to the big banks that are worried about agents making transactions. Claude Claude won't even give investment advice, instead giving users help with budgeting by analyzing their spending data

giving a [00:09:00] sense of the current state of how they talk about AI's potential disruption of professional services, they wrote, " "Personal Personal finance is a first step. For life's bigger money decisions, we think people are best served by Claude working alongside the licensed professionals and regulated firms they trust."

Over in Meta Land, many are rejoicing that Muse now has a voice. Meta's breakout personal agent now has a feature that allows users to communicate with it through voice and video chats. Now, from the start, Muse has leaned into the idea of personalizing your agent by giving it a name and an avatar.



this feature takes that idea to the next level, fully animating the avatars for voice calls and letting users choose from a range of different voice options

AI CEO Alexander Wang showed off the new feature

demonstrating his agent Euler coaching him through problems at work related to posting too many memes

The feature uses Meta's live voice models, which are top-notch, as well as their real-time avatar technology. Meta says the avatars will use subtle expressions and gestures to match the tone of the conversation, but they don't actually have visual inputs to pick up on nonverbal cues The feature is now live for mobile, web, and Mac, and Meta says [00:10:00] they'll use it to power the forthcoming Muse charm device It also works with the Muse Gadget SDK, so you can now talk to your home-brewed AI devices as well

And lastly, OpenAI has rolled out a new version of their image model that now supports animated GIFs. Users can now prompt a short animation, edit the speed and modify individual frames before they share it online

OpenAI says that their users are now creating four billion images per week, up from three billion a couple of months ago. Images 2.6, which is the name of the new version of the model, is fifty percent faster and able to generate an image in as little as six seconds. It can also now accept reference images, meaning users can upload a rough doodle or easily turn it into a high-quality image

And so if you didn't already have plans for the weekend

Maybe it's time to experiment with making your own GIFs. For now, that is gonna do it for the headlines. Next up, the main episode 

Hello everyone

One big change around AI is we've shifted our thinking from how we rank our pages to how do we become the source that AI trusts [00:11:00] enough to answer with?

At KPMG, they're seeing this firsthand. AI-generated results now surface answers directly, often without a single click. that's why they are increasingly focused on generative engine optimization or GEO, structuring content so AI systems can retrieve it, understand it, and cite it as trusted authority this is not just an SEO evolution, but a visibility mandate. And indeed, the GEO mandate from KPMG is simple: If AI is shaping decisions, your expertise needs to show up inside the answer.

Read all about it at slash us/geo. Again, that is kpmg.com/us/geo

This episode is brought to you by Cdata. Companies rolling out AI keep hitting the same wall. Token spend is climbing, security is holding agents back, and no one knows how AI is truly being used. The root cause, AI is cut off from the systems and context your business runs on

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Build what's next at blitzy.com. [00:14:00] That's B-L-I-T-Z-Y.com Welcome back to the AI Welcome back to the AI Daily Brief. Today, we are digging into Menlo's latest enterprise AI report. They've been doing these state of enterprise AI reports for three years now

And they provide a really good snapshot into how businesses are using AI. Now, the data for this survey comes from a survey of around 400 US enterprise decision-makers that was conducted by Morning Consult in mid-September.

So this is pretty fresh data those enterprise decision makers, by the way, are responsible for about $4 billion in AI spend

So it's a pretty meaningful sized group Now in terms of caveats, of course Menlo is not some unbiased observer. They are heavily invested in the AI field including in Anthropic and many other companies

but as we'll see, this data all directionally backs up, I think a lot of common sentiment about business AI spend right now, even if it adds some more color and details as well. What we're gonna do on today's show is not go through every single number in the deck.

[00:15:00] Instead, I've pulled out the 10 most interesting, each of which I think has something else to tell us about the AI market The first set of stats are all about how much business AI spend has grown.

And what's interesting about 2026 is that not only has spending gone up, the rate at which it is growing has also gone up across the four years, excuse me, I think I said three years before, but the four years that Menlo has been doing this survey, their estimates for enterprise AI spending have jumped from one point seven billion annually in twenty twenty-three, to eight point five billion in twenty twenty-four, to thirty-three billion last year, to almost a hundred and seventy-seven billion in twenty twenty-six Now Now from a growth rate perspective that means that the growth rate from twenty-three to twenty-four was five X, but the growth rate from twenty-four to twenty-five was down to three point nine X.

Now, this is kind of what you'd expect. As a market gets more mature, more saturated, and the total size of the numbers go up

While the sheer amount of spend can continue to increase The multiple of the previous year's spend at some point has to start coming down. That's sort of what it looked like happened between '24 [00:16:00] and '25. Except now in '26, we've had a jump not only ahead of the 25 number, but a faster growth rate than even the 23 to 24 number

And obviously the explanation is agents. This year, the big story of 2026 from Opus 45 in November of last year on



is that AI spend stopped being about seats and started being about tokens and everything that intelligence could do, and that has totally transformed our expectations of how big the market can get

This number also shows up in our next stat where breadth of AI spend was up only incrementally, but depth of AI spend was up significantly. So the share of enterprises using AI jumped from fifty-five to sixty-five percent between '25 and '26, which is insane that that still means that thirty-five percent of enterprises report not using AI.



But here we are. If you've ever doubted the inertia of the business world, there you go However, the average AI spend per enterprise using 2025 as 1X is up almost 5X, 4.7 times [00:17:00] more per enterprise on average this year Again, the answer is agents. Agents that can work independently, that can run in parallel, that can keep working after people step away.

Spending then scales with work, not with headcount

Now, one thing that we've been debating on this show, and in markets frankly, is whether the concentration and top heaviness of AI spend is a bearish or bullish sign. The bearish sign, at least from a markets perspective, is that when big chunks of revenue are concentrated in a small handful of customers

If any one of those customers stops spending or changes their spending habits significantly, it can have an outsize effect on the entire market, which could then have knock-on effects that drag the whole market down This is why concentration is a risk. The other way to look at it, however, is that the concentration is not a natural state of the market where there are only a handful of companies that can use that much AI, but instead that those handful of companies that are using that much AI are leading indicators for where the rest of the market will eventually [00:18:00] go.



that is of course the bullish assessment



that takes what those companies at the very top are spending and multiplies that across all the other companies that could possibly spend that much as well. Menlo certainly reads this 5X growth in average spend per enterprise as a sign of future demand

they believe that the long tail basically is barely getting started

And one more on growth, and this is something that certainly we've been seeing companies reconcile with all year. Enterprises spent about 2.5 times what they had budgeted this year Basically, the average company planned to roughly double spend between '25 and '26. they had planned to grow spend about 1.9X.

so if you spent $10 million in 2025, you were anticipating that you'd spend $19 million in 2026 Those numbers were, of course, calculated by most firms on the basis of increased adoption on a per-seat basis across more parts of their companies. In actuality, spend has jumped four point seven X

Around two point five times more growth than they had budgeted for

and the challenge of course for CFOs is that this [00:19:00] isn't necessarily some out of control spend. It's people flexing and understanding what they can actually do with all this raw intelligence power

The whiplash that companies have had between token maxing on the one hand and token restrictions on the other are all symptomatic of the fact that we planned for one paradigm, a per seat paradigm, and instead got a per token paradigm, which needs a totally different type of strategy

budgeting for AI is starting to look more like budgeting for cloud compute, but even that analogy falls down as well. It It really is this new category of thing that doesn't cleanly fit in past buckets

And so needs to be considered on its own new terms

For our next section, we'll look at what Menlo found About the competition and where those enterprise dollars were going

One story that kind of flies in the face of some of the conventional wisdom, or at least some of the narratives for the past few months, is that overall, buyers moved to more expensive models. The most popular models a year ago were Claude Sonnet 4.5, which cost $3 per million input tokens and $15 per million output [00:20:00] tokens, and GPT-5, which was $125 and $10 respectively.

The most popular models today are, unsurprisingly, the latest generation from each of those companies as well. Claude Opus 5.5, which is $4 input and $20 output, and GPT-6 Astro, which is 10 and 60



The frontier model spend of share went from thirty-five percent to sixty-three percent

Meanwhile, every other category, mid-tier, small, and older, went down as a percentage of total spend.

Now, part of this is also small model price wars. Labs cut small model prices by 80% plus

But the story that this survey is finding is that buyers still, despite all of the narrative about cost and cost efficiency, are choosing capability over cost. So is the narrative that we've had about cost wrong? or is there room for both of these stories simultaneously?

I actually think that these two things are not mutually exclusive

I believe that the continued emphasis on frontier models reflects the fact that starting with Opus 4.5, we really have had a differentiated set of capabilities that came online. And [00:21:00] since the entire year has been businesses racing to catch up with those capabilities and implement new agentic coding, they've sort of been priced in, at least initially, to taking advantage of the frontier models



Opus 5.5 feels like another one of those jumps where we're uncovering a bunch of use cases that really are significantly different with Opus 5.5 and aren't as easily translated to other models I don't think that that changes the fact that there is still going to be fierce and extremely important competition for those cheaper and more efficient models

I think we're still in an early adoption phase where adoption is necessarily going to be more heavily concentrated in the state-of-the-art. but as that adoption matures and the stack matures along with it, you will see those smaller, more efficient models start to come in and form a more complex stack.



if anything can be said, it's that perhaps our narratives and storytelling are getting out ahead of where businesses actually are And in that way, this is a good reminder that the things that we're discussing on the AI Daily Brief and within heavily enfranchised AI circles don't necessarily reflect the lived reality right [00:22:00] now of the average business buying and using AI

Now, Now, when it comes to the competition, Anthropic has continued its ascendancy. Back in 2023, Anthropic had about 12% of business API spend. In '24, it jumped to 24%, and by last year it was up to 40%. Now it has crested over the halfway mark at 55%.



and if you are once again seeing the fingerprints of agents here, you are not wrong. The Opus models ever since Opus 45 have been perhaps the default for long horizon agentic encoding tasks, and that's now showing up in the numbers. Now, OpenAI has throughout the same time, in general

seen a decline. They went from fifty percent of spend in '23 to thirty-four percent in '24 to twenty-seven percent in '25. Last year, in other words, was the first year that Anthropic actually flipped OpenAI. This year, though, we did see a slight recovery. OpenAI is now up to thirty-one percent of enterprise API spend And Menlo's read is that this represents a strong second half of the year recovery.

The GPT 5.6 series of models, including Sol, Terra, and Luna coming in [00:23:00] July, as well as GPT 6 in September. And just in general, OpenAI's emphasis throughout 2026 on enterprise and business use cases is clearly bearing fruit. Now, the main company that suffered and allowed Anthropic and OpenAI to both win more market share is Google.

Google was down from 21% of enterprise spend last year to just 9% this year And again, this is just the story of companies flowing to where the best model capabilities are. While Gemini 3 Pro was at the frontier in November before Opus 4.5 came out and agentic coding took over everything That was the last time Google could reasonably be considered

in a three-way race for the top of the heap when it comes to model capability. Perhaps that is changing now with Gemini 4 Carbon Early indications look good, although it remains to be seen and needs a little bit more battle testing for us to claim that

But at the moment, when it comes to enterprise AI spend, this is a completely two-horse race between Anthropic and OpenAI

The near frontier models still haven't really broken through for as much attention as they get. Grok 4.7, [00:24:00] Kimi K3, DeepSeek V4, GLM 5.3 all together still account for under 5% of API spend. Although as we'll see when we get to open source

When it comes to understanding open and cheaper models, it's worth looking not only at percentage of spend, but percentage of tokens

Because it turns out that while open source only gets 4% of spending, it represents about 19% of tokens

interestingly enough, as a percentage of spend, open source is actually down. it was at six percent in 2025 and is at four percent this year. However, as a percentage of tokens, it has jumped from eleven to nineteen percent

I would argue that in the medium to long term, The token share numbers are more significant than the spending numbers. Like I argued before, I think right now we're still in an early adoption phase where companies are concentrating around the most expensive models to bring these new capabilities online.

But the fact that you're starting to see open source take more share is directionally where things will head



and I don't think that that's a trend that's reached its peak

Now, what this says for the total [00:25:00] addressable market value of all tokens is an interesting debate and one that happens on Wall Street all the time. There are plenty of smart people who think that in the long run, open source models represent the vast majority of tokens used, but still a relatively small percentage of overall spend, which is concentrated among the most important and elite use cases

driven by the state-of-the-art closed models

By the way, when it comes to open source spend, Qemu was the dominant force this year representing 61% of open source spending

Next up, we move to different categories of usage within the enterprise

One of the most staggering numbers, not unexpected but still staggering in its magnitude, was the revenue growth of the lab coding agents, Codex and Claude Code

That revenue jumped from $1 billion at the end of 2025 to 24 billion at the end of 2026 Like I said, not surprising given how much Claude Code and Codex have just defined the experience of AI users throughout the year

But still, [00:26:00] 24X growth in revenue in a single year is just astronomical. Now, what's interesting is that this didn't necessarily come at the cost of other coding platforms like Cursor, Cognition, Factory, Lovable, etc. Their revenue still grew almost three X this year as well, from two point six billion last year to seven point eight billion now.

It's just that the lab coding apps

were by far the more significant

Now, when you start to go category by category

You might expect then that coding

grew faster than any other category when it came to app spending, and you'd be almost right. agentic coding apps grew at a 7.7X rate. Ahead of everything except financial services, which grew at an 8X rate Now, the total spend on financial services apps was a tiny fraction of coding apps.

Overall, Menlo estimates financial services apps saw about eight hundred million dollars in spending compared to the thirty-four billion for coding apps

But still their speed of growth is an interesting trend. some of the other notable areas of growth include customer success, which grew four point three X, sales [00:27:00] apps which grew three point six X, legal which grew two point eight X

And down at the bottom, marketing, which grew just one point eight X

It's tempting once again, I think, to read this chart as at least in part emphasizing the fact that applications in verifiable domains where the power of agentic coding could be brought to bear, even if it wasn't for building apps, were the ones that benefited the most from the developments of 2026

Now, Now, when it comes to which companies are being chosen to deliver the AI, there is a really strong divide between AI native companies, by which of course I mean companies like Anthropic and OpenAI

And the incumbents who are fast bringing AI into their services, such as ServiceNow.

when it when it comes to those coding applications, they are dominated by AI-native companies. AI-native companies, in fact, represent 96% of that spend. finance and operations is also up there where AI-native companies represent 95% of spend And AI natives also rule the design field representing 81% of spend Sales is nearly split in the middle with [00:28:00] fifty-four percent of the spend going to AI natives versus forty-six percent to incumbents.

But then every other area, marketing, customer success, HR, data science, and IT all see the incumbents getting more than fifty percent of market share

Basically, big chunks of enterprise spend has wanted to flow towards the solutions they already use and already have embedded and already have connected to their systems of record or which are their systems of record, rather than ripping that out and building something completely new It makes sense then on the other end of the spectrum that agentic coding, which is a net new capability, doesn't yet have an incumbent solution that's plugged into all the systems, so it has room for a new solution to come in.

Compare that to IT, for example, where many companies have been using the same vendor or partner for years, if not decades. And it makes sense perhaps then that only 9% of AI app spend for IT is coming from AI-native companies, compared to 91% that's going to incumbents.

So what does this all mean for software? Another question that lurks, particularly in the minds of investors who are trying to figure out how AI plays out, [00:29:00] is whether AI just eats SaaS. one of the big stories of 2026 was, of course, the SaaS-pocalypse, where for a while at the beginning of the year as agentic coding came online, SaaS stocks were just absolutely decimated as people operated on the idea that the ability to roll your own apps or spin your own apps was going to fundamentally undermine the use of those SaaS tools That's not exactly the story that Menlo found, however



While Menlo did find some diminished growth of the SaaS application market from 13% growth in 2025 to 11% growth in 2026



they found that the majority of new AI spending actually came from net new budgets, not existing software budgets



of the AI spending that could be accounted for by Menlo, 21% came from existing software budgets. That compared to 55%, which was net new budgets

Now that's obviously positive news for SaaS investors and SaaS companies. Although I do think that we've barely gotten started on the next narrative which threatens SaaS, which is the rise of agents using software and headless software [00:30:00] and all it takes is reading some comments from Salesforce to understand how significant that disruption could be as well.

For now, however, it looks like a lot of AI spend is net new, Not just cannibalizing spend that already existed



So that So that is Menlo's State of Enterprise AI. Like I said at the top, I think that this broadly reflects a lot of what we've been discussing and seeing throughout the show. Although it is a good reminder that there remains a gap between our narratives that we explore every day and the trends that we see on the horizon, and the lived experience that companies operate within every day.

For now, that is For now, that is gonna do it for today's AI Daily Brief. Appreciate you listening or watching, as always. And until next time, peace. 

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