# Enterprise AI Anxiety and SaaS Market Stabilization

**Podcast:** The AI Daily Brief (Formerly The AI Breakdown): Artificial Intelligence News and Analysis
**Published:** 2026-04-10

## Transcript

Today on the AI Daily Brief, the excited anxiety of enterprise AI.
And before that, in the headlines, the SaaSpocalypse is over.
The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
All right, friends, quick announcements before we dive in.
First of all, thank you to today's sponsors, KPMG, Blitzy, Drata, and Zencoder.
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While you're on the site, you can scroll around to find out all the things going on, including, of course, the March AI Usage Pulse Survey, which is closing in a couple of days, and the second cohort of our Enterprise Claw program, which is the facilitated team complement to Claw Camp.
Registration for that closes on Monday, and you can find that at enterpriseclaw.ai.
With that out of the way, let's talk the Sasspocalypse.
We kick off today with a bit of a narrative watch as the SaaSpocalypse narrative seems to be ending as the story of AI disruption fades on Wall Street.
Now, a couple months ago, Wall Street woke up to the massive paradigm shift on the horizon via products like Claude Code and Claude Cowork.
Software indices sold off by 20% in short order, with more vulnerable single stocks taking an even larger hit.
Now the panic is over, and there is far more optimism that SaaS companies can navigate their way through the disruption.
On Tuesday at the HumanX conference in San Francisco, AWS CEO Matt Garman rejected the notion that AI coding would disrupt incumbent SaaS firms.
He said that the idea that companies could use Claude Code to write their own software to replace platforms like Salesforce was overblown.
Now, his view is that AI is enormously disruptive, but that it also represents a huge opportunity for those existing incumbent software companies.
He said they know more about the edges of their software, and so they are in a better position to build the next generation of AI-enabled products.
And yet, Garmin also recognized the risks of not moving with the times, warning that firms that try to protect what they have rather than lean in would be in trouble.
More broadly, Goldman Sachs analyst Peter Oppenheimer believes the worst is over for tech stocks in general.
In a Tuesday note, he wrote that tech's underperformance this year is starting to create opportunities as, quote, its valuation relative to expected consensus growth has fallen below that of global aggregate market.
He noted that past quarter was one of the weakest performances in 50 years for tech stocks relative to global markets.
The weakness has been entirely driven by fears around infrastructure spending, and then of course the shift to fears of AI disruption.
Cybersecurity is also gathering attention as one subsector where the narrative of AI disruption was especially overdone.
Manthan Shah of Westbridge Capital said, right now software investors are selling first and asking questions later, but I think we'll look back and see this as a really interesting time to get into security.
This is one of the top areas where we're excited about the long-term potential.
Shah believes that investors were getting the AI cybersecurity narrative completely wrong.
He said AI is going to massively increase the surface area that can be vulnerable, meaning the need for security is going to compound significantly going forward.
I'm going to slap a big old duh on that one, as the major story of our week was of course Anthropics Mythos, completely freaking everyone out when it comes to, what was it again?
Oh yeah, cybersecurity.
Multiple analysts have upgraded cybersecurity stocks in recent weeks, noting that AI changes the nature of security budgets.
but very likely won't decrease them.
Piper Sandler analyst Rob Owens argued that AI is, quote, an opportunity, not a replacement threat, because it will create the next multi-billion security opportunity as enterprises look to secure a new attack surface.
Even those who believe software is in for a rough year still think security could be an exception.
Ryan Assurewood, the CIO of Significance Capital Management, said, It seems hard to imagine that security stocks will get the premium multiples they've been afforded in the past, but it still looks like the best place to be within software.
We don't want to touch a lot of application software stocks, but within software, cyber looks like the best house in a bad neighborhood.
Now, speaking of market forces, Anthropic has wrapped up their tender offer, but very few employees are cashing out.
Last month, Anthropic gave employees the option to sell their stock into the secondary market.
The stock was valued according to the last venture funding round completed in February, which gave Anthropic a $380 billion valuation.
That mark felt to many already a little cheap by the time the round closed.
But that sense rapidly increased as Claude Code took over the world in the following month.
Some secondary markets saw Anthropic stock trading as high as a $600 billion implied valuation in recent weeks.
According to Bloomberg sources, the tender offer failed to reach its full allocation, leaving some outside investors unable to pick up as much stock as they had hoped.
The total size of the tender offer was not disclosed, but sources indicated it was less than 6 billion investors had lined up to buy.
One source said that the lack of selling reflects optimism among employees that Anthropic's value will continue to skyrocket as revenue booms.
Many are holding onto all of their shares in anticipation of the upcoming IPO.
Now, tech employees holding onto their stock into a hyped IPO is not a unique phenomenon to Anthropic.
OpenAI's most recent tender also failed to fill up, with employees only selling $6.6 billion out of the total $10.3 billion in approved sales.
Still, Anthropic's rapid growth means employees are saying no to a significant amount of money to bet on a big IPO pop.
Employees with more than one year at the company were eligible to sell stock, meaning that even the least tenured stock options of the lot would have been struck at Anthropic's January 2025 valuation of $60 billion.
Speaking of Anthropic, they are also doing well in the talent war.
The company had two big executive poaching announcements.
The first is that they grabbed Eric Boyd, an 18-year veteran of Microsoft, to help run their rapidly scaling infrastructure efforts.
He most recently led the AI hardware and software team for Azure, and now he is joining Anthropic as their new head of infrastructure.
Now, this hire comes, of course, as Anthropic begins to take a more active role in infrastructure management to meet surging demand.
Up until now, they'd largely outsourced that to cloud partners including AWS, and more recently, Google and Microsoft.
Earlier this week, you'll remember, Anthropic announced a massive new deal with Google and Broadcom to stand up 3.5 gigawatts of dedicated inference with the build-out beginning next year.
The information reported that Anthropic is not just hiring Boyd, but an entire team consisting of veterans from other leading cloud enterprises.
In another big get, Anthropic poached Workday this week as well.
Peter Bayless had only joined Workday last May and resigned from the company last month.
The information reports that he will work on reinforcement learning engineering.
For some, though, the question is whether Anthropic is staffing up to take on incumbent SaaS platforms.
Workday's stock lost more than 40% of its value in the now-over, as we just discussed, SaaSpocalypse.
suggesting that the market is pricing them as one of the more vulnerable software firms.
Regardless of what Anthropic is actually working on, the market took this hire very badly, sending Workday down 6.5% on the day.
Over in legal land, Elon Musk's courtroom showdown with Sam Altman is inching closer and tensions are running hot.
In an amended filing on Tuesday, Musk clarified his desired outcome.
He is asking for the judge to unwind OpenAI's for-profit conversion and remove Sam Altman and Greg Brockman from the non-profit board.
Much of the reporting has focused on the $150 billion in damages Musk is seeking alongside corporate reforms.
To set the record straight, however, Musk's filing asks that any monetary damages be awarded to the nonprofit rather than to Musk himself.
Musk's lawyer, Mark Toberoff, said the amendment was filed to make it clear that Musk is, quote, not seeking a single dollar for himself.
Toberoff continued, he is asking the court to return everything that was taken from a public charity and to make sure the people responsible are never in a position to do this again.
OpenAI fired back on X, posting, Today at the 11th hour, Elon lodged a court filing pretending to change his tune about attacking the non-profit OpenAI Foundation.
The truth is that this case has always been about Elon generating more power and more money for what he wants.
Having increasingly realized that his attempt to damage the non-profit OpenAI Foundation rests on a baseless legal case, Elon is once again trying to change the narrative and save face as the trial approaches.
His lawsuit remains nothing more than a harassment campaign that's driven by ego, jealousy, and a desire to slow down a competitor.
The lawsuit will now proceed to a jury trial beginning at the end of the month.
Meanwhile, in more business-y Elon news, Intel has thrown in their lot with Elon, joining his moonshot chipmaking venture.
Intel will partner with Tesla and SpaceX on the TerraFab facility in Austin, Texas, providing design and construction support.
Crucially, Intel will oversee the refactoring step, a manufacturing process that makes the chips more powerful and reliable.
TerraFab is Elon's latest megaproject, designed to produce enough domestic AI chips to power his ambitions to build a, quote, robot army.
Tesla already produces their own AI chips for use in vehicles, but the manufacturing is outsourced to TSMC in Taiwan.
Musk wants to bring the process onshore at an ambitious scale, targeting one terawatt of chips per year.
According to SpaceX, this would make TerraFab the largest fab in the world.
They're framing the project as the, quote, next step to becoming a galactic civilization.
Now, for Intel, this could be a major step in their reclamation project.
They are already planning to build two fabs in Arizona as part of a $20 billion investment in local production.
However, progress has been slow and the company is still struggling to find their feet.
Joining up with Elon, Inc.
could give them a huge boost in credibility if they can complete the project.
Intel CEO Lip Bhutan is enthusiastic, posting, Elon has a proven track record of reimagining entire industries.
This is exactly what is needed in semiconductor manufacturing today.
TerraFab represents a step change in how silicon logic, memory, and packaging will get built in the future.
Semiconductor analyst Patrick Moorhead believes this is a perfect match, commenting, Intel Foundry needs anchor customers, Musk needs a process partner.
So another interesting chip dynamic to watch, but for now, that is going to do it for the headlines.
Next up, the main episode.
All right, folks, quick pause.
Here's the uncomfortable truth.
If your enterprise AI strategy is we bought some tools, you don't actually have a strategy.
KPMG took the harder route and became their own client zero.
They embedded AI and agents across the enterprise.
how work gets done, how teams collaborate, how decisions move, not as a tech initiative, but as a total operating model shift.
And here's the real unlock.
That shift raised the ceiling on what people could do.
Humans stayed firmly at the center while AI reduced friction, surfaced insight, and accelerated momentum.
The outcome was a more capable, more empowered workforce.
If you want to understand what that actually looks like in the real world, go to www.kpmg.us.ai That's www.kpmg.us.ai.
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These aren't experiments.
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So coding agents are basically solved at this point.
They're incredible at writing code.
But here's the thing nobody talks about.
Coding is maybe a quarter of an engineer's actual day.
The rest is stand-ups, stakeholder updates, meeting prep, chasing context across six different tools.
And it's not just engineers.
Sales spends more time assembling proposals than selling.
Finance is manually chasing subscription requests.
Marketing finds out what shipped two weeks after it merged.
Zen Coder just launched Zenflow Work.
It takes their orchestration engine, the same one already powering coding agents, and connects it to your daily tools.
Jira, Gmail, Google Docs, Linear, Calendar, Notion.
It runs goal-driven workflows that actually finish.
Your stand-up brief is written before you sit down.
Review cycle coming up?
It pulls six months of tickets and writes the prep doc.
Now you might be thinking, didn't OpenClaw try to do this?
It did, but it has come with a whole host of security and functional issues, which can take a huge amount of time to resolve.
Zencoder took a different approach.
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Try it at zenflow.free.
Welcome back to the AI Daily Brief.
We have discussed enterprise AI implicitly or by extension quite a bit recently without necessarily going super deep on what recent numbers are telling us.
I shared the AI maturity maps framework last week, which is a way of looking at AI readiness and AI adoption across six different dimensions, including deployment depth, systems integration, and governance, and shared a bit about what our research had told us about where organizations are right now and why we think in many cases it's behind where they need to be.
But of course, that's different than digging into the actual numbers themselves.
And recently, we've gotten a bunch of different studies.
all with direct sourcing from inside companies that are telling some similar and some different stories about enterprise AI.
So what we're going to do today is talk through what those studies are telling us, where they agree, where they disagree, and what I think the sum total is, and why even all of this still might be missing something.
First up, we have some research from A16Z.
Now, where this data comes from is the aggregation of private data from a number of leading enterprise AI startups who live and work inside many of these big corporations.
Here's a couple of the highlight numbers.
A16Z found that about 19% of the Global 2000 are live-paying customers of a leading AI startup, with that number rising to 29% of the Fortune 500.
That means the enterprises have signed a top-down contract with an AI startup, successfully converted a pilot, and gone live with the product in their organization.
Now, 29% might seem low, but as you heard, that does not include pilot efforts, nor, my guess, is it comprehensive across every tool that companies might be using.
Their next exploration is what is actually working.
And here's their methodology.
A16Z writes, We find that the most indicative way to assess the work the models are inherently better at doing is to overlay revenue momentum across use cases against the theoretical capabilities of models as defined by GDPVal.
They write that to them, these two factors encapsulate both how good models could be as well as how much they're proving to deliver today.
When it comes to use cases and functions, enterprise AI adoption is dominated by coding, support, and search, with coding being the absolute biggest by an order of magnitude.
The tech, legal, and healthcare sectors they found have been the industry's most eager to adopt AI.
Now, we have talked so extensively about coding being the dominant use case for AI that I don't think we need to get into it here.
But their discussion of support, I think, is interesting for reinforcing why it's a comparatively good place for organizations to start when it comes to AI.
First of all, they point out that a lot of the type of work that AI is doing was already outsourced in some way.
Because, as they put it, companies deemed it too tedious and complicated to manage themselves.
Second, they argued that its discreteness really matters, i.e.
the nature of most support interactions is time-bound with a constrained intent that outputs into a well-defined problem for an agent to tackle.
It's got an easy ROI profile, because support operates on quantifiable metrics like number of tickets answered, satisfaction scores of customers and resolution rates.
And, and I think importantly, they point out that support doesn't require 100% accuracy to be useful.
since it has natural off-ramps to a human, e.g.
the I'm escalating you to a manager.
Now, when it comes to the industries, again, technology is not a surprise, but legal, they write, was primed for adoption of AI because it had actually been left behind by traditional enterprise software.
They write, static workflow tools didn't accelerate the unstructured nuance work that lawyers typically did, but AI has made the value prop of technology to lawyers much clearer.
AI is excellent at parsing dense text, reasoning over large amounts of text, and summarizing drafting responses, all work that lawyers regularly do.
Healthcare, they argue, is another market that's responding to AI in a way that it didn't for traditional software.
They write, With AI, however, they write, e.g.
medical scribes, or augmenting higher-value work doctors were doing.
The work is distinct enough, then, not to require a rip-and-replace of the EHR.
Now, moving out to a more longitudinal view, we have KPMG's most recent quarterly pulse survey.
This data comes from a set of executives from companies primarily with more than a billion dollars in revenue and is their recurring tracker so we get some amount of quarter-over-quarter data.
A couple of big things stand out.
Despite ROI still being hard to quantify in really clear ways, The average anticipated spend on AI just continues to go up.
In Q1 of last year, organizations reported to KPMG that they anticipated spending about $114 million on average over the next 12 months.
That has now jumped to $207 million.
And part of the reason might be that agents are now, to put it bluntly, very real.
In Q1 of 2025, only 11% of organizations had agents in deployment.
Many more were experimenting or piloting, but that was the number where agents were in full production.
In Q2, that jumped meaningfully to 33%.
And yet in Q1 of this year, that number is now over 50% for the first time at 54%.
Within that 54%, 40% are scaling or deploying, 6% are developing multi-agent systems, and 9% are orchestrating.
Piloting is down to 30%, and experimenting is down to 14%.
And in many ways, this agentic adoption kind of defines everything else on the survey.
A lot of the considerations are around how to manage new risk from agents.
cyber and employee misuse is up from 32% to 44% when asked about the most difficult society-wide challenge with AI between now and 2030.
It's also coloring challenges around employees.
While 55% of organizations are seeing slight or significant employee adoption of agents, i.e.
employees beginning to accept and integrate agents into their work, they're also finding resistance.
Interestingly, the resistance appears to be more about skills gaps than concerns about job security, although both rate very highly at 76% and 71% respectively.
Agents are also shaping what companies expect from their talent.
57% said that they expect humans to primarily manage and direct AI agents in the next two to three years.
64% said agents had already changed their approach to entry-level hiring, which is interestingly lower than the percentage who said that agents had changed their approach to experienced hires, which was at 71%.
And for the carrot in this equation, 45% of leaders said that they're willing to pay 11 to 15% more for strong AI skills.
When it comes to how they get these skills, most leaders are looking internally first.
87% said that they are focused on upskilling or reskilling their current workforce.
68% said that they're hiring for new roles like AI architects.
55% said that they're redesigning existing roles.
all of which is much higher than the percentage that are turning to managed services at 39% or acquihires at 17% to get the AI skills they need.
Interestingly, when it comes to what leaders value in their talent, while 71% said technical or programming abilities, and this is specifically for skills related to entry-level employees that need to work with AI agents, 83% said that it's about adaptability and continuous learning.
Now, there are still tons of challenges and barriers to demonstrating ROI.
58% point to risk considerations such as data privacy and cyber.
59% said that they have difficulty quantifying indirect or long-term benefit.
62% see skills gaps.
And 65% are having difficulty scaling use cases.
And many of these are the stories and themes that a recent study from Writer, in collaboration with Workplace Intelligence, found.
Writer CEO May Habib sums up the difference between last year's version of the study and this year's like this.
When we looked at the data last year, the defining theme was tension.
Budgets were climbing and pilots were multiplying, but the reality on the ground was messy.
Ownership was murky, IT and the C-suite were locked in a constant tug-of-war, and frustration grew as that massive investments hit a wall.
Only 12 months later, the tension has evolved into something much more consequential.
It's now cultural, organizational, and deeply structural.
Now, importantly, Ryder is actually dealing with the new reality of agentic.
May continues, the shift towards agentic AI has moved at a pace that's hard to overstate.
AI isn't rolling out at the edges anymore.
Instead, organizations are embedding agents directly into their mission-critical workflows, where they make autonomous decisions and fundamentally change how work gets done.
On the one hand, you can feel the ambition.
There's an entire cohort of AI-native leaders and employees who are compounding their advantage in real time.
They're working faster, more independently, and more creatively than we could have imagined just a year ago.
But all of that enthusiasm is running headlong into chaos.
Agendic AI is exposing a deep structural gap that most enterprises just aren't prepared for.
It's showing up in misaligned incentives, siloed teams, and outdated operating models that are reaching a breaking point.
So this study surveyed 2,400 knowledge workers across the US and parts of Europe and was split half and half between C-suite executives and employees.
All employees in the study were required to be actively using generative AI tools at work, and executives were required to be working at a company that permits the use of AI.
Basically, this is a voice of AI adopters study.
A couple of the highlights that I thought were interesting.
One is that on the one hand, leaders are in many ways out ahead of their employees when it comes to AI adoption.
with 64% of those surveyed spending at least two hours a day using these tools.
75% of executives believed that AI agents would be part of their company's C-suite within the next five years.
And yet, a full 73% of CEOs said that their company's AI strategy was causing them stress or anxiety, with 38% reporting a high or crippling amount of stress.
61% of executives fear they could lose their job if they fail to lead their organization through the AI transition.
And when you dig into the numbers, it's not hard to see why the strategy is successful.
Basically, the strategy is kind of not clear.
39% don't have a formal strategy in place to drive revenue from AI, and a full 75% said that their company's AI strategy was more for show than actual internal guidance, which is obviously just a recipe for disaster.
56% said that AI had created power struggles and disruption at their organization, which is a big jump from 42% last year.
On the flip side, there's continuing employee sabotage of the efforts.
29% of employees, including 44% of Gen Z, admit to sabotaging their company's AI strategy.
And 76% of the C-suite said employee sabotage poses a serious threat to their company's future.
35% of employees said that they'd entered proprietary, confidential, or sensitive information into a public AI tool.
And two-thirds of executives said that they believed their company had already suffered a data leak or security breach because of an employee using an unapproved AI tool.
And if you want to get a sense of why, I think you have to point to a gap in leadership.
Just 35% of employees said that their manager is an AI champion.
75% said that they trust AI more than their manager for certain work tasks.
That is just an incredibly damning statistic that is showing up downstream, I think, in everything else.
And increasingly, what you're getting, then, is effectively two tiers in the workplace.
92% of the C-suite said that they're actively cultivating a new class of AI elite employees, with 60% planning to lay off employees who can't or won't use AI.
AI super users are about 3x more likely to have gotten both a promotion and a pay raise in 2025 compared to those who aren't using AI.
In its recent State of Digital Adoption report, SAP subsidiary WalkMe surveyed 3,750 executives and employees across 14 countries and found something similar.
Their report found that 33% of employees hadn't used AI at all and another 54% had in some cases bypassed their company's AI tools to complete work manually instead.
And once again, the story here is a difference between employees and leadership, which is, to put it bluntly, a leadership problem.
For example, 61% of executives trust AI for complex business-critical decisions, but only 9% of workers surveyed in this survey did.
That is a 52-point trust gap.
88% of executives said that their employees have adequate tools, but only 21% of workers agree.
That is a 67-point gap.
This reflects something that we found when we were aggregating surveys.
for the maturity maps, that something like 93% of all AI spending goes to infrastructure and models and compute and tools, compared to just 7% invested in the humans using those things.
That is a recipe for disaster, and the disaster is showing up in the data.
Which brings us back to a theme which is going to come up in a different way on Monday's episode about harness engineering.
The quintessential lesson of the last year of AI adoption in the enterprise is that picking the tools and getting access to the models is not enough.
The companies that are seeing results and getting value out of AI are designing systems and structures that support its use and support the people using it.
I call this episode the excited anxiety of enterprise AI because the people who have gone all in, using Claude Code or OpenClaw or Cowork or Codex or any of these other tools, genuinely go to sleep and wake up feeling like they have superpowers.
And yet for everyone else, they feel increasingly adrift, at risk of obsolescence.
Simply put, there is a leadership crisis when it comes to AI, and the companies that don't solve it are going to fail.
This is a theme I'm sure we will continue to watch, but 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.