Intelligence – Earlybirds Invest

Colton Masi checked off every box in his quest to land a good job in the computer science industry after college.

The 23-year-old attended Drexel University, a Philadelphia school distinguished by its focus on real-life job experience. And he majored in software engineering, a discipline he had been hearing his whole life was synonymous with stable, high-paying work. It was all part of his plan to avoid the fate that befell so many millennials after the Great Recession.

“When I was 13, I was online all the time.” Colton told Today, Explained co-host Noel King. “I was on Tumblr, and I was seeing a lot of these currently graduating young adults kind of talk about their struggles with the job market and getting themselves established…I was always like, ‘Oh no, I need to do something that’s going to get me a job.’”

So Masi took the advice offered by everyone from Joe Biden to Chris Bosh to Ashton Kutcher in that era: he learned to code.

But Masi graduated from Drexel this past June into a historically bad job market for entry-level computer science positions. Since then he’s applied to about 100 jobs — none have even offered an interview.

“It’s like, you do everything right. You follow the instructions, but the field changes,” Colton said. “There’s nothing you can do about it. It’s just: keep it pushing until you find something.”

Masi’s situation is increasingly common for recent college graduates and others seeking to break into white-collar industries like computer science and marketing.

“I hear about a lot of rejection from job seekers,” Lindsay Ellis, a reporter for the Wall Street Journal who has been crunching the numbers on the entry-level job decline, told Noel King. “[The] market feels kind of stuck to a lot of people.”

Ellis talked to King about why big companies are planning on a future with far fewer entry-level employees, the wild lengths people are going to to find a job, and what career advice executives are giving their own kids.

Below is an excerpt of their conversation, edited for length and clarity. There’s much more in the full podcast, so listen to Today, Explained wherever you get podcasts, including Apple Podcasts, Pandora, and Spotify.

If I were to guess at what’s going on, I would say this must have something to do with AI. Is that it?

That’s a factor, and I think is layered on top of a bunch of other factors that have caused the white-collar market to slow considerably over the last few years.

You know, starting in maybe late 2022, early 2023, companies and hiring managers were really pumping the brakes in a lot of sectors. There were the tons of tech layoffs that started in ’23, but from inflation [and] geopolitical conflict, then the looming election and a lot of uncertainty — in terms of policy — [about] which way things were going to go. If a hiring manager is saying, “Hey, can we hold off on making this hire and maybe have a little bit more buffer in terms of headcount, in terms of payroll costs,” they might see how long they can last without making that hire.

And then you add in AI as a layer on top of all of this, and the calculation is totally different. I talked to James Hornick, who’s the chief growth officer at the Chicago-based recruiting firm Hirewell. And he told me that clients have all but stopped requesting entry-level staff. Those young grads were once in high demand, but their work is now a home run for AI.

We’re always trying to figure out what is data and what is anecdata. You can hear one story about someone who applied for three or four jobs a day for a month and got nothing, and that will be the thing that sticks in your brain forever.

But the unemployment rate in the US right now is around 4.2 percent, which is super low, right? Is there a tension between the one extreme story and the actual trend?

Behind that number, I think you’ll see a couple of other trends that suggest that the picture is a little bit more complicated.

Number one is sort of labor data on the time it takes to find a job. And there are two things that my colleagues and I have been looking at. One is for unemployed Americans, it now takes them on average 24 weeks to find a job after losing one, and that’s nearly a month longer than a year prior.

And the number of long-term unemployed Americans — that’s people who are unemployed for at least 27 weeks — that figure is now 1.8 million people a year. Prior, it was like 1.5 [million]. So that’s an uptick too.

The other factor here is you think about which sectors are hiring at the moment, [and] much of the jobs growth is coming from state and local government, or sectors like health care, social assistance, leisure and hospitality, construction. A white-collar project manager probably wouldn’t be qualified for a role in health care or might not be looking for a local government job in a different state. So I think it’s also a question of matching opportunity to skillset and how that goes.

The job application process for a long time has been: There’s maybe a portal and you submit your resume, or you send an email to a hiring manager. Is AI changing the way we apply for jobs?

Oh my god, you have no idea.

This has been a total fascination of mine. The job application process now in many ways can in my mind be described as a robot-versus-robot arms race, basically.

What you hear from applicants is that they are super frustrated with corporate hiring software, which for many years will scan an applicant’s resume and cover letter and basic details and sort of rank them based on their qualifications. And they feel like that artificial intelligence basically forces good people to slip through the cracks.

So in response, [applicants are] using AI of their own to craft cover letters and resumes, using the job description and their own stuff to basically incorporate all of the keywords, [to] show how they’re responding to specific job responsibilities. There are even tools, though, that scan the entire internet for potential jobs and then just spray out a candidate’s application in seconds.

The whole thing has left applicants and employers super irritated, because employers are totally — all of their portals are getting clogged up, and it’s really hard to tell who is actually interested versus who is using really good prompts or keywords. Applicants are really frustrated because they will look at a job posting on LinkedIn, and it’ll say how many people have applied, and it’s like, Shoot, I have no chance here. Should I even still do this? Then if they do put time into their application, they might get a rejection hours later or at 2 in the morning on a Sunday. It just feels super impersonal, and both sides of the table are really frustrated.

What are young people being told to do now? What are the options?

I’ve been asking executives the same question. I mean both from a [perspective of], what are you talking to universities about — because there’s a lot of correspondence between business and higher ed — but also, what are you telling your own kids?

I talked to the chief executive of a consulting firm in Ohio, and he basically said, I’m telling my kids to really focus on jobs that really require in-person or client-facing communication. One of his children is becoming a police officer, and he said, while AI will affect the way he does his job, nothing replaces those relationships that are forged face-to-face in a community.

And now, chief executives are talking openly about AI’s immense capabilities, and how those might lead to job cuts, even more so than [just] at the entry levels. I mean, you had executives at Amazon, JPMorgan in recent weeks saying that they expect their workforces to shrink considerably. The CEO of Ford said he expects AI will replace half of the white-collar workforce in the US. Those are figures that suggest that people in various roles, various experience levels, should expect significant disruption.

You have spent a lot of time, all over the country, talking to people who are really struggling. What do you think about how these folks — many of them young people — are going to deal with all this?

Many people feel quite low. It’s a really hard stretch, and it’s a hard time to be on the market, and I don’t want to sugarcoat that.

I talked to some people who say, what’s really helped me is to get outside, do some gardening, go for a run, go swimming. Swimming is great. You can’t really have your phone in your hand. I will say, though: A lot of them are spending a lot of money to be able to hopefully speed up this process and stand out to employers and potential employers.

I talked to one guy who said he spent $10,000 on basically a marketing firm that’s treating him as the product, to basically get his resume out there, make him a website, try and introduce him to hiring managers and people who might know of jobs that aren’t posted publicly.
So I think for some people, it helps when they can funnel their frustration into, I’m going to do this; I’m going to really push myself hard. Other people have been telling me, look, this is a marathon, not a sprint. I need to make sure I’m taking time outside of this hunt to really keep my mental health steady.

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Period	Active customer accounts	Year-over-year growth (in %)	Dollar-based net expansion rate (in %)
Q1 2024	313,000	4%	102%
Q2 2024	316,000	4%	102%
Q3 2024	320,000	5%	105%
Q4 2024	325,000	7%	106%
Q1 2025	335,000	7%	107%
Q2 2025	349,000	10%	108%

Investors may not realize it, but today (Aug. 14) is one of the most important days of the entire quarter. While earnings season is critical in helping investors learn about the operating health of America’s leading businesses, Form 13F filings, which are due today, are equally invaluable.

A 13F is a required filing due no later than 45 calendar days following the end to a quarter for institutional investors with at least $100 million in assets under management. It allows investors to track which stocks Wall Street’s smartest money managers purchased and sold in the latest quarter (in this instance, the June-ended quarter), as well as identify which trends have the attention of successful fund managers.

Silver dice that read, buy and sell, being rolled across a digital screen displaying stock charts and volume data.

Image source: Getty Images.

Although Warren Buffett is the stock market’s most followed billionaire investor, he’s far from the only billionaire known for their outsized investment returns. For instance, Coatue Management’s Philippe Laffont, who’s been a big investor of the artificial intelligence (AI) revolution, is known for spotting phenomenal deals hiding in plain sight.

Laffont’s approach to the evolution of AI has been particularly interesting. Specifically, he’s pared down his fund’s stake in the face of the AI movement, Nvidia (NVDA -0.83%), for eight straight quarters, and has been buying shares of another historically cheap AI stock hand over fist.

Coatue Management’s billionaire chief has sold 83% of his fund’s Nvidia stake

While some billionaire money managers bid adieu to AI-graphics processing unit (GPU) colossus Nvidia many quarters ago, Coatue Management billionaire boss has been paring down his fund’s stake with some degree of consistency for two full years. Accounting for Nvidia’s historic 10-for-1 stock split in June 2024, Laffont has overseen an 83% reduction in his fund’s position in this AI powerhouse:

Q1 2023: 49,802,020 shares of Nvidia
Q2 2023: 46,449,700 shares
Q3 2023: 45,410,400 shares
Q4 2023: 43,222,010 shares
Q1 2024: 13,851,410 shares
Q2 2024: 13,754,447 shares
Q3 2024: 10,138,161 shares
Q4 2024: 10,006,488 shares
Q1 2025: 8,545,835 shares

With Coatue’s average top-20 position held for roughly 21 months, as of the end of March, it demonstrates that Laffont and his top advisors aren’t shy about locking in gains when presented with the opportunity. Nvidia shares catapulting more than twelvefold since the start of 2023 has given Coatue’s brightest investor plenty of reason to cash in his chips.

The concern with Philippe Laffont’s persistent selling spanning eight quarters is there may be more than profit-taking on his mind.

For instance, while the addressable opportunity for AI is sky-high, historical precedent shows that every next-big-thing trend for three decades has endured a bubble-bursting event early in its expansion. Investors have a tendency to overhype the utility and early stage adoption rates of new technologies, which eventually leads to these lofty expectations not being met. No company has been a more direct beneficiary of the evolution of AI than Nvidia, which suggests it would potentially be the hardest hit if the AI bubble were to burst.

Another possible consideration for Philippe Laffont is growing competition in the AI-GPU space. Make no mistake about it, Nvidia’s Hopper (H100) and Blackwell GPUs are at the top of the pedestal, in terms of compute ability. But this doesn’t mean Hopper and Blackwell won’t endure headwinds in the coming quarters and years.

Specifically, internal competition could prove to be a thorn in Nvidia’s side. Many of its leading customers by net sales are developing AI-GPUs and solutions for their data centers. Even though these chips are slower than Nvidia’s and they pose no external competitive threat, they’re considerably cheaper, more readily accessible, and capable of taking up valuable data-center real estate. In short, these chips could crush Nvidia’s pristine pricing power and gross margin.

Nvidia’s valuation is worrisome, as well. Historically, megacap companies have peaked with price-to-sales (P/S) ratios of roughly 30 to 40. Nvidia is tipping the scales at a P/S ratio of more than 30, as of the closing bell on Aug. 11.

Two engineers checking wires and switches on an enterprise data center server tower.

Image source: Getty Images.

Billionaire Philippe Laffont can’t stop buying this cash-rich AI stock

On the other end of the spectrum is a historically cheap and cash-rich artificial intelligence stock that Coatue Management’s billionaire chief can’t stop buying. I’m talking about China-based Alibaba Group (BABA 3.73%).

When 2024 came to a close, Alibaba was a relatively forgettable holding in Coatue’s portfolio, with just 192,728 shares held. But during the first quarter, Laffont came close to 20Xing this stake to 3,801,703 shares, based on the filed 13F.

While Alibaba’s growth ambitions very much rely on AI, this isn’t the company’s foundational operating segment responsible for most of its cash flow. Alibaba laid its roots through its e-commerce operations in China.

Whereas online retail sales have matured in the U.S., a burgeoning middle class in the world’s No. 2 economy by gross domestic product can generate high-octane e-commerce sales growth for the foreseeable future. Based on an analysis from DBS Treasures, Taobao and Tmall combine to account for a 41% share of China’s e-commerce space. These platforms should have little issue continuing to generate bountiful cash flow that Alibaba can redirect to faster-growing and/or higher-margin initiatives.

However, e-commerce isn’t the only arena that Alibaba Group is leading. According to estimates from tech analysis firm Canalys, Alibaba Cloud reined in 33% of Mainland China’s cloud infrastructure service spending during the first quarter, which was nearly double the 18% share Huawei Cloud earned as the No. 2 cloud infrastructure services provider.

Alibaba is aggressively incorporating generative AI solutions into its cloud platform and giving its clients access to the tools needed to build and train large language models. The expectation is that these AI solutions will enhance demand (and margins) for Alibaba Cloud.

Something else that’s likely attracted Laffont to Alibaba is the company’s capital-return program. It closed out fiscal 2025 (ended March 31) with $51.6 billion in cash, cash equivalents, and short-term investments, along with $7.4 billion in equity securities and $6 billion in restricted cash. When combined with the cash flow being generated from Alibaba’s numerous operating segments, there’s more than enough capital available for share repurchases and dividends.

The proverbial cherry on the sundae is that Alibaba Group stock is historically inexpensive at an estimated 11 times forward-year earnings. This is modestly lower than its average forward price-to-earnings (P/E) ratio over the past half-decade, and it stands out amid a historically pricey stock market.