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Robinhood Set Records on Revenue, Net Deposits, and Gold Subscribers. The Stock Is Still 44% Below Its High.


Robinhood (HOOD -3.61%) reported second-quarter results on Wednesday that set records nearly everywhere you look. Record revenue. Record net deposits. A record number of Gold subscribers, and record trading volumes in both equities and options.

The stock slipped about 3% during Wednesday’s regular session, closing at $89.84 before the results arrived, then fell another 3.6% on Thursday, to $86.60. That leaves shares roughly 44% beneath the 52-week high of $153.86 they set back in October of last year.

So why won’t the market pay what it used to for a business performing like this? The latest report holds most of the explanation. Here’s a closer look at three things it tells us.

Image source: Getty Images.

The records are broad, not narrow

Robinhood’s revenue reached $1.31 billion in the second quarter, a record, and 32% more than a year earlier. The growth rate more than doubled from the first quarter’s 15%, so the pace is accelerating, too.

Net deposits came in at about $22 billion for the quarter, and total platform assets climbed 32% year over year to $369 billion. Gold subscribers (members of the company’s premium tier, which bundles higher yields, lower margin rates, and other perks) reached a record 4.8 million, up 39%. Funded customers grew 7% to 28.4 million, and retirement assets under custody reached $34.5 billion, 82% higher than a year earlier.

Profitability scaled right along with it. Non-GAAP (adjusted) EBITDA rose 35% year over year to $741 million, a 57% margin.

Net income came in at $573 million, up 48% year over year, and earnings per share climbed at the same rate to $0.62. Both figures got help from about $0.14 per share of one-time gains, though, so the underlying earnings number is closer to $0.48.

The engagement stats were arguably the most impressive part. Equity trading volumes jumped 85% year over year to a record $956 billion, and options contracts traded rose 50% to a record 774 million. On the earnings call, chief financial officer Shiv Verma said the company now counts 13 separate businesses that have each reached $100 million in annualized revenue, two of them added during the quarter.

The mix behind the records changed

Look one layer down, though, and the composition of all that record revenue has shifted meaningfully.

Crypto trading revenue was $252 million as recently as the first quarter of 2025. In this year’s first quarter, it was $134 million. Last quarter, it was $100 million, down 38% year over year — the second quarter in a row of declines at a roughly 40% pace or worse.

What replaced it is younger. Equities revenue nearly doubled year over year to $129 million. Event contracts, the company’s prediction-markets business where customers trade on outcomes like elections and economic data, generated $156 million, up more than tenfold from a year earlier. A business line that barely existed at this scale a year ago now brings in more revenue than crypto does.

This, I’d argue, is what the market is discounting. Robinhood’s transaction revenue has always moved with whatever retail traders are excited about, and the excitement rotates. The records themselves aren’t in dispute. What the market keeps marking down is how repeatable they are, when the fastest-growing line has only recently begun proving itself at scale.

The drawdown hasn’t made the stock cheap

Robinhood Markets Stock Quote

Today’s Change

(-3.61%) $-3.24

Current Price

$86.60

A 44% decline sounds like a bargain. But as of this writing, shares sit at $86.60, or about 38 times earnings — a valuation that assumes plenty of growth ahead. And that’s with earnings flattered by the one-time gains mentioned above.

To be fair, the company is executing well beyond trading. Net interest revenue rose 9% year over year to $389 million, and the margin lending book more than doubled to $21.6 billion. The newer banking and retirement products continue to attract assets, too. Growth like that in platform assets could eventually make trading swings matter less to the overall business.

Is the stock a buy?

Ultimately, I think the market has this one about right. The company is executing about as well as anyone could ask, and the record quarter was broad-based. However, about 38 times earnings already pays for that execution, and the newest revenue lines haven’t yet shown they can hold up across a full market cycle.

If you own the stock, I don’t see anything in this report that argues for selling a business performing like this. I just wouldn’t buy shares on its strength, either. Another quarter or two showing the new revenue mix holding up (crypto stabilizing while event contracts keep growing) could change my mind at a similar price.

The Marketing Channel AI Can’t Commoditize


Catch The Full Episode

Overview

Marketing output is up everywhere, and John Jantsch has good news about where to point it. AI has made it easy for anyone to put out decent marketing content, so the businesses that stand out now are the ones building genuine trust, not churning out more content. Buyers lean on AI engines to help them choose, which puts a premium on being the business people and machines both recommend.

Jantsch explains that the back half of the Marketing Hourglass (retention, repeat business, referrals) is where meaningful advantage lives now. He returns to three ideas from his 2010 book, The Referral Engine, still his bestselling book by the numbers, and shows why they matter more today than when he wrote them. The episode covers why people are wired to refer, how a simple referral system turns that instinct into growth, and why being referable comes down to a value proposition people love passing along.

This one’s for small business owners, agencies, and consultants ready to turn referrals into a growth engine instead of a happy accident. Jantsch walks through partner referrals, an underused channel, using his work with Mike Michalowicz’s Prosper Group as an example, and leaves listeners with two exercises to try this week.

About John Jantsch

John Jantsch is a marketing consultant, speaker, and Wall Street Journal bestselling author known for the Duct Tape Marketing system. He is the author of Duct Tape Marketing, The Referral Engine, and The Ultimate Marketing Engine, and hosts the Duct Tape Marketing Podcast.

Key Takeaways

  • AI has made competent marketing cheap and widely available, making differentiation harder for buyers to spot
  • Buyers increasingly rely on AI search engines and AI models for recommendations rather than doing their own comparison shopping
  • Referrals are the most trusted lead type because they lower perceived risk, making referred customers less price sensitive
  • A referral system requires two components: a process for asking, and a clear, easy to repeat explanation of the value you provide
  • Partner referrals, meaning other businesses that share your ideal client, are a bigger opportunity than most people realize because one strategic partner can refer far more business than a single satisfied customer

Great Moments

  • [00:02] – John opens with the core premise: marketing output is up, results are down, and AI has made his 2010 book more relevant than ever
  • [02:23] – Why getting chosen has become harder, and why doubling down on retention and referrals matters more than chasing new leads
  • [07:08] – The three ideas from The Referral Engine: people are wired to refer, referrals need a system, and referability depends on a clear value proposition
  • [09:28] – Why partner referrals are a bigger opportunity than customer referrals, illustrated through the Mike Michalowicz Prosper Network partnership
  • [12:12] – How AI models are pulling recommendations from customer reviews and social proof rather than traditional authority signals

Memorable Quotes

  • “A referral is the most trusted lead in most cases.” — John Jantsch
  • “No amount of systems or processes or great copy are necessarily going to generate referrals if you are not referable.” — John Jantsch
  • “Machines are talking to machines now to make a referral, if you want to look at it that way.” — John Jantsch
  • “The second half of the hourglass has become probably the most important element.” — John Jantsch

Resources

AI marketing, John Jantsch, Marketing Hourglass, Referral Marketing, Small Business Marketing, The Referral Engine

FCA Shares Update On Stablecoin Sprint


Earlier this year, the UK Financial Conduct Authority (FCA) completed a “Stablecoin Sprint” to better understand the emerging digital currency market. The initiative included about 75 participants, including traditional finance and Fintechs, addressing payments and transfers. This past week, the regulator provided an update on what they learned during the Sprint.

Insights gleaned from the project include:

  • Cross-border and emerging markets are the clearest near-term opportunity. Stablecoins can improve on correspondent banking, especially for corridors involving emerging markets with limited USD access. Advantages are less obvious in major trade corridors already well-served by SWIFT and existing networks.
  • Domestic UK retail payments are already cheap and fast for consumers, so pure programmability is unlikely to drive broad consumer adoption soon. Merchants stand to gain more (lower costs vs card schemes, faster settlement, better liquidity). Possible later or niche consumer use cases include cross-border e-commerce, agentic AI payments, and micropayments.
  • Banks are viewed as essential for trust, scale, and interoperability, but many remain cautious due to AML/CDD concerns and unclear liability across payment chains. There was debate on whether issuers should pay interest or share rewards (potential deposit/credit-creation impacts vs ability to keep end-user costs low).

In regard to regulation, there is an expectation that stablecoins be treated just like money or cash equivalents, but adaptations to rules will be needed to address the new technology. At the same time, over-regulation can stymie innovation.

The FCA and other government entities continue to investigate updates to payment services rules and tokenized assets, including stablecoins.

Laurent Descout, CEO at Neo, shared his thoughts on the stablecoin sprint as these digital assets “continue to attract regulatory attention due to the benefits of fast and low-cost transactions, eliminating the need for ‘clunky bank transfers’ and currency conversions.

“Despite the benefits, corporates have historically been deterred from adopting stablecoins by the operational and regulatory complexity that comes with managing external wallets. To become a core part of operations for businesses in emerging markets, stablecoins need to be able to co-exist alongside traditional currencies, rather functioning as an entirely separate ecosystem that needs to be managed.”



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You Have 500 Leads. Now What? How to Prioritize Your List So You’re Not Wasting Marketing Dollars.


Sponsored by PropStream

As a real estate investor, you already know that finding deals is the cornerstone of building a successful business. Building a list of potential sellers is pretty straightforward these days—there are software tools that can easily generate a list of 500 potential leads. So, not knowing how or where to find leads is definitely not an issue. 

The issue is that you diligently mailed and called everyone on your huge list, got a very low response rate, and concluded that “marketing doesn’t work.” Maybe these data tool-generated lists are overhyped? Maybe you should go back to old-school ways of looking for motivated sellers, e.g., direct outreach?

Let’s pause at the phrase “motivated seller.” That’s your real issue right there—not using a tool that “doesn’t work” but treating every name as equally likely to sell. To succeed, you must learn how to prioritize, and that involves reframing your goals from “How do I find leads?” to “How do I know which leads are worth spending money on first?” 

Here’s how you do that.

What “Motivated” Actually Looks Like in Data 

The idea of a “motivated seller” can seem like a vague one. Traditionally, investors using direct outreach to find motivated sellers had to almost act as detectives, trying to suss out the property owner’s financial position and goals and then pitch them an attractive opportunity. Essentially, you would be trying to build a complex picture of where the potential seller is from a puzzle piece here and there. 

Identifying motivated sellers can be a much more efficient and less labor-intensive process. All you need are concrete, visible data metrics that will give you a clear sign that the seller is ready for an offer. It’s not a guessing game anymore when you have access to these key data fields. They are: 

  • Equity position: How much of the property’s value the owner owns outright vs. still owes on a mortgage. High equity = more room to accept a lower offer and still walk away with cash.
  • Absentee owner: The owner doesn’t live at the property (examples include that it’s a rental or inherited home). These owners are statistically more open to selling than owner-occupants.
  • Length of ownership/tenure: How many years the owner has held the property. Longer tenure often means more built-up equity and less emotional attachment tied to a recent purchase.
  • Tax delinquency: The owner is behind on property taxes, a sign of financial strain.
  • Pre-foreclosure status: The owner has received a default notice from their lender, a strong signal of urgency to sell.

Your work doesn’t quite stop at identifying these key areas, however. Just one of these doesn’t necessarily mean that you have yourself a high-quality lead. High equity, for example, doesn’t automatically translate into a motivation to sell—some people would rather keep their home no matter what. 

Once you have access to the right data, you need to filter your potential sellers and prioritize them based on how likely they are to want to sell. Again, it’s not a guessing game: There’s a simple formula for sifting through your list and prioritizing the highest-quality leads.

Building a Simple Scoring System 

Remember: Outreach costs you money, eating into your business budget. Therefore, you must treat it as a finite resource. 

That’s why you need to prioritize efficiency instead of treating your lead list as a simple numbers game (contact everyone and hope for the best). Systematizing your outreach process is crucial if you want to pour the majority of your effort and money into the leads that will actually pay off. 

There is no single “correct” framework for prioritizing your leads, but it could look like this:

  • Tier 1 (contact first, higher effort): High equity + absentee owner + long tenure. These owners have both the ability and likely motivation to sell.
  • Tier 2 (contact second, medium effort): Meets one or two of these criteria.
  • Tier 3 (lower-cost outreach): Meets none of the above but still fits the general buy box (e.g., right property type/area).

Real estate is not an exact science, which is why sometimes, if you still have the budget and time for it, you might try one or two people in tier 3 in case your hunch they might want to sell turns out to be correct. 

The tiered framework just builds an element of discipline into how you approach your leads overall. So, rather than all your outreach efforts being just stab-in-the-dark hunches, most of them will have an evidence-backed assumption of motivation behind them.

Sequencing Outreach and Budget Around Tiers 

So, what does prioritization look like in practice? Once again, there’s no guesswork involved. There’s a simple formula that makes outreach yield better, more predictable results: You pour more resources and effort into the top tier of your leads, the tier the most likely to generate deals. 

Tier 1 leads should get a phone call or personal letter, which costs more but also has a higher chance to get them to sell. By comparison, tier 3 leads can be covered with a cheap postcard drip campaign. The juice is simply not worth the squeeze of a higher effort when your chances are low to begin with. 

This tiered approach to budgeting your outreach directly ties list prioritization to marketing ROI—spending more money on the leads most likely to convert first.

Easy Data Access With PropStream

At this point, you might be asking yourself, “Well, how do I get access to all this key data?” That’s where PropStream comes in. 

PropStream supplies the underlying data fields (equity, absentee status, ownership length, and tax and foreclosure records) needed to actually build this scoring system. It’s not just another list-generation tool but the source of the details that make prioritization possible. Try it for yourself and see how your marketing efforts are transformed from haphazard to efficient and high-yield.  

Trump Says Warsh Wants Lower Interest Rates, But Has a Political Board


As expected, the Fed left rates unchanged this week, though there was an outside chance they were going to raise 25 basis points.

It turned out to be a holding steady situation as most envisioned, though three board members did dissent.

That included Dallas Fed President Lorie Logan, Cleveland Fed President Beth Hammack, and Minneapolis Fed President Neel Kashkari.

They all wanted a 25-bp hike, with Logan saying she thought rates should be “modestly higher.”

However, President Trump said new Fed chair Kevin Warsh wants lower rates and is essentially hamstrung by a “political board.”

Does Kevin Warsh Want Lower Mortgage Rates?

As always, I need to point out that the Fed doesn’t set mortgage rates, though monetary policy does play a role.

And the reason we had record low mortgage rates for much of the past decade was because of the Fed’s Quantitative Easing (QE) program.

While that’s likely not coming back anytime soon, the Fed is at a crossroads with inflation rising again and the economy under threat from increased layoffs, AI, and a protracted war in the Middle East.

For now, it appears they can see through the rise in oil prices, which have driven inflation higher.

That means they can keep rates as they are, with neither a hike necessary nor a cut justified.

It’s pretty much no different than when Jerome Powell was the chair, except now we have the unknowns of a serious war to consider.

If you recall, Trump chose new chair Kevin Warsh because he was fed up (no pun intended) with Powell for not cutting rates fast enough.

But now Warsh is in the same boat as Powell, though Trump pointed out that it’s not his fault (unlike Powell).

After the Fed announced that it had held steady, Trump told the press that “Kevin’s fantastic, but he’s got a board.”

Adding that “I know he’d love to see lower interest rates, but he’s got a board, and it’s a political board, and they want to keep rates up.”

Trump Has the Power to Get Mortgage Rates Lower!

Now the ironic part. While Trump is quasi-complaining again that the Fed isn’t doing what he wants, he in fact might have more power than them.

Ultimately, the Fed is simply following the economic data, which is driven to some degree by government policy.

Remember Trump’s tariffs? And his new ones. Those are said to increase inflation, which would require either a rate hike or at minimum no rate cuts.

What about the war with Iran? Again, that has led to the closure of the Strait of Hormuz, a key channel for energy transport.

As a result, the price of oil has skyrocketed, leading to another unwanted bout of inflation.

Simply put, mortgage rates don’t like inflation because it erodes the value of the underlying bonds.

That means mortgage-backed securities (MBS) investors require a higher yield (interest rate) in order to buy them. So mortgage rates go up.

Perhaps if Trump didn’t keep threatening tariffs, and didn’t get us into another war, mortgage rates would be doing what he wanted.

And the Fed could also keep cutting, making government debt cheaper to repay at the same time.

Instead, policies that drive up interest rates continue to get unleashed, making it impossible for Trump to reach his goal of bringing back those 3% mortgage rates he promised us.

A 25-bp rate hike or cut wouldn’t move 30-year fixed mortgage rates much.

But ending the war, or tariffs, or avoiding other policies that don’t drive up government spending and inflation could be a huge tailwind for mortgage rates.

That would also allow Warsh (and the rest of the Fed board) to play ball accordingly.

Colin Robertson
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Diagnostic AI Is Now the Top Patient Safety Concern: What Physicians Need to Know



Have you ever used a tool at work that you didn’t fully understand, but used anyway because everyone else was? That’s one of the biggest threats with AI in medicine right now.

For a while, the conversation about AI and patient safety was mostly hypothetical. What might go wrong someday. What we should probably watch for eventually.

That changed in March 2026.

ECRI, a nonprofit patient safety organization, released its annual report with the Institute for Safe Medication Practices. They ranked the ten biggest threats to safe care in the U.S. this year.

Above rural hospital closures. Above the return of vaccine-preventable diseases. Above federal funding cuts.

Number one was diagnostic AI.

Not future AI. Not some theoretical version down the road. The tools being used in clinics and hospitals right now, today, on real patients.


Disclaimer: While these are general suggestions, it’s important to conduct thorough research and due diligence when selecting AI tools. We do not endorse or promote any specific AI tools mentioned here. This article is for educational and informational purposes only. It is not intended to provide legal, financial, or clinical advice. Always comply with HIPAA and institutional policies. For any decisions that impact patient care or finances, consult a qualified professional.

With so much noise out there, it’s hard to know who’s actually done what you’re trying to do.

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What the problem is

The report isn’t saying these tools are bad. That’s not the argument.

The real issue is the gap between how fast we’re using them and how well we actually understand them. A survey of nearly 1,200 physicians found that 66% reported using AI in clinical practice in 2024. That’s up from 38% the year before. Almost double, in twelve months.

The tools spread faster than the training did. Faster than the policies. Faster than anyone figured out who’s accountable when something goes wrong.

And the performance numbers are where it gets real. Tested machine learning models failed to recognize 66% of critical or deteriorating conditions in synthesized cases. Popular generative AI tools saw their accuracy drop when the prompts came from open-ended patient conversations instead of clean, textbook descriptions.

Sit with that one for a second.

A tool that performs well on a tidy, structured input but struggles with the messy, incomplete, sometimes contradictory way patients actually talk is a tool worth understanding closely before leaning on it. Performing well on a test and performing well in the room aren’t always the same thing.

ECRI itself frames the risk in three parts: more diagnostic errors, more bias, and an erosion of our own critical thinking over time. Three different problems. All three are real.

Who takes the liability?

Richard Anderson, CEO of The Doctors Company, put it plainly to Medical Economics:

“If AI makes a recommendation that’s different than the standard of care, and the doctor follows it, and the outcome is actually adverse, then, by definition, the doctor has violated the standard of care.”

Read that again.

That’s the paradox Anderson is describing: the tool makes the call, but under this reasoning, the liability can still land on the physician who followed it. Worth knowing, whatever your specialty.

Anderson said more than 1,000 AI tools have already gotten FDA validation, but most of us have no real way to evaluate which ones are actually reliable, or what happens when one gets it wrong. His words, not mine: “It’s 100% certain that the technology that is integral to the practice of medicine today, which includes AI, the legal system will not keep up with that technology.”

So that gap isn’t closing anytime soon. Which is part of why ECRI’s guidance points toward understanding a tool’s limitations at the individual level, not just assuming it’s been sorted out somewhere upstream, whether it came from your hospital, your EHR, or a vendor’s pitch deck.

What’s being recommended

A few things, and they may apply to you individually, not just to the hospital system.

First, clear AI usage policies are lagging behind adoption. The AMA’s 2026 survey found that 81% of physicians now use AI professionally (more than double the 2023 rate) while physicians consistently rank data privacy assurances and validated safety/efficacy as the top prerequisites for trusting a tool. In practice, that gap between fast adoption and slower institutional guardrails means you’re often the one evaluating a tool’s reliability yourself, without a formal policy to lean on.

Second, training on the specific tools you’re actually using. Not AI in general. Knowing a tool has FDA clearance tells you almost nothing about where it degrades or which patients fall outside what it was validated for.

Third, documentation. Write down when AI influenced a clinical decision. ECRI frames this as good practice on two fronts: it creates a record, and over time it gives your practice real data on whether a tool is actually performing the way it was advertised.

Fourth, and this one matters most: treat AI as a supplement, not a replacement. That’s not a policy line. That’s just a habit you build.


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Here’s the part I don’t think gets said enough

It would be easy to walk away from all this thinking AI is just risky and we should all proceed with our arms crossed.

That’s not the full picture. The same report that ranked diagnostic AI as the top safety concern also said, clearly, that AI has real potential to improve how we work and who gets access to good care. Both things are true at once. A tool can be genuinely useful and still carry risk that deserves real attention.

I think the physicians who come out ahead here are the ones willing to hold both of those truths. Leaning on AI without understanding its limits carries real risk. But so does avoiding it entirely just because the oversight isn’t perfect yet, since colleagues who learn to use these tools well may end up with real efficiency gains.

ECRI put this at the top of a ten-item list this year, which suggests it reflects a pattern their team is seeing show up across the health care system, not a one-off concern.

Taking it seriously doesn’t mean walking away from AI. It means applying the same rigor you’d apply to any other clinical tool. Know what it’s good at. Know where it breaks. Write it down when it influences a call. Keep your own judgment in the loop, always.

That’s not some new, higher bar we have to clear.

It’s just medicine. Same as it’s always been, applied to a new kind of tool.

So, how are you handling AI in your own practice right now? Are you using it, avoiding it, or somewhere in between? Let us know in the comments.


At Passive Income MD, we cover the tools, strategies, and practical AI workflow tips helping physicians build more time and financial freedom. We’ll keep tracking where AI goes from here.


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Want more tips to sharpen your AI skills? Subscribe to our newsletter for exclusive insights and practical advice. You’ll also get access to our free AI resource page, packed with AI tools and tutorials to help you have more in life outside of medicine. Let’s make life easier, one prompt at a time. Make it happen!


Disclaimer: This article is for general informational and educational purposes only. It does not constitute medical, legal, compliance, or professional advice. The information provided here is based on available public data and may not be entirely accurate or up-to-date. It’s recommended to contact the respective companies/individuals for detailed information on features, pricing, and availability. All screenshots, if any, are used under the principles of fair use for editorial, educational, or commentary purposes. All trademarks and copyrights belong to their respective owners.

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Further Reading



Tim Cook’s final Apple earnings call amid ‘hundred year flood’ in memory chip pricing



Apple said it is facing severe supply constraints that will affect sales of iPhones and Macs in the months ahead, underscoring the challenges looming over the company as Tim Cook prepares to hand over the CEO reins.

In his final earnings call as CEO, Cook said he has never been more optimistic about the opportunities ahead for Apple. “I am beyond excited,” said Cook, who has led the company for 15 years and will pass the CEO baton to John Ternus in September. But Cook’s confidence in the future stood in contrast to the picture that he and other executives painted of the current business conditions. 

“We’re seeing some very significant constraints currently, with limited flexibility in the supply chain,” Cook said. “There’s a quarter where we’re going to be scrambling on the supply side,” he acknowledged at another point. 

The supply crunch is making it more difficult for Apple to obtain the advanced processors it needs for its phones and computers. And that translates into lower revenue. 

Sales of the iPhone, which accounts for roughly half of Apple’s business, will grow at a “mid-teens” percentage rate in the current quarter, Apple said, forecasting a significant deceleration from the 22% growth the iPhone business posted in the recently ended quarter. Total revenue in the current quarter will grow between 9% and 10% year-over-year, which was below the 12% rate expected by analysts.

Gross profit margins, which came in at 48% of revenue (excluding the benefit of tariff refunds) in the most recent quarter, will come under pressure in the current quarter, Apple said. 

Shares of Apple fell as much as 8% in after hours trading on Thursday following the earnings results, before regaining some ground, with the stock later trading down roughly 6% from its closing price of $333.85. 

Apple, the world’s most valuable company (market cap $4.9 trillion!), has been one of the best performers among Big Tech stocks this year, with its shares up 23% in 2026. While Apple has been late to the AI game and struggled to develop its own AI models, investors have come to appreciate that the company is not locked in the AI infrastructure arms race that has swelled capital expenditures at Meta, Google, Microsoft, and Amazon. 

Last week, Google-parent company Alphabet’s stock plunged 7% after it raised its capex forecast for the year to above $200 billion and reported its first ever negative free cash flow. Meta’s stock took a similar drubbing this week. 

While Apple is not spending hundreds of billions in capex, its business is still feeling the effects of the AI arms race, specifically when it comes to memory chips. The demand for memory chips in the data centers being built to power AI is causing a shortage and sending prices skyrocketing. 

In June, Apple was forced to raise prices of its Macs and iPads to account for the inrecased cost of memory chips. “We did it because we’re in what I would characterize as a hundred year flood on the memory pricing,” Cook said on Thursday’s earnings call.

He lamented that the DRAM memory chip market is essentially owned by three companies, Micron, SK Hynix, and Samsung.  “If there were more suppliers that would be good. It would help us on  the supply side, and perhaps the pricing side,” Cook said, adding somewhat cryptically that Apple is “evaluating all options.”

Asked about Apple’s AI offerings, including the upcoming Siri AI, Cook said the company saw an opportunity in running more AI directly on users’ devices, an approach that Apple believes can appeal to privacy-minded users. “The ability to run some percentage of requests on-device is also very strategic, sort of a competitive weapon if you will,” Cook said. 

But he also noted that AI capabilities could lead to an increase in users of Apple’s iCloud offering–a potential boost to Apple’s Services business, its second largest business unit, with $30 billion in revenue last quarter.

Apple’s overall revenue in the three months ended June 30 totalled $109.4 billon, up 16% year over year, and roughly in line with analyst estimates. The company posted net income of $29.8 billion, or $2.02 per share, versus the $1.89 EPS expected by analysts. Apple said that roughly 11 cents of the EPS was attributed to a refund from President Trump’s tariffs. 

As for incoming CEO John Ternus, he was present for the earnings call, but did deliver prepared remarks and was not made available as an executive available for the Q&A portion. That didn’t stop one analyst from trying. 

The analyst wanted to know: How does Ternus view the competitive landscape, especially at a time when new competitors like SpaceX and OpenAI could be preparing to offer AI-powered hardware devices that would compete with Apple?

“I would just reiterate what Tim said,” Ternus said. “There is so much opportunity for us, with everything that’s happening in the space. We’re just really focused on our plans, and really excited about it.”

Andy Jassy said Amazon will spend $220 billion this year—and still won’t have enough capacity



Amazon’s stock price jumped more than 9% in after-hours trading on Thursday after the retail-and-AI giant reported second-quarter results buoyed by its Amazon Web Services cloud business, which is racing faster ahead than it has in more than four years.

The cloud unit posted $42.2 billion in revenue in Q2, up 37% from $30.9 billion a year ago, marking AWS’ fastest growth in 18 quarters, and what Amazon CEO Andy Jassy called its fifth consecutive quarter of accelerating growth. AWS added more than $4.6 billion in revenue quarter over quarter, and AWS operating income hit $16.6 billion, up 64% from $10.2 billion a year ago, on a 39.4% margin, up from 32.9% in the same period a year ago. AWS’s backlog—customer agreements representing future revenue—grew to $496 billion. 

“AWS is now a $169 billion dollar annualized revenue run rate business, which, for perspective, would place it 24th on the Fortune 500 list if it was a standalone company,” Jassy said during Thursday’s earnings call.

Across all of Amazon businesses including stores, advertising, Prime, devices, and cloud, net sales rose 20% to $200.6 billion, compared with $167.7 billion a year earlier. Operating income surged to $27.5 billion, from $19.2 billion. Net income hit $62.6 billion, or $5.75 per diluted share, compared with $18.2 billion, or $1.68 per share, a year ago—with a caveat that the net-income figure includes $53.4 billion in non-operating income primarily from Amazon’s investments in Anthropic. Advertising, one of the unsung heroes of Amazon’s business, grew 26% year-over-year, up from 22% growth a year ago when the segment hit $15.7 billion.  

Meanwhile, free cash flow, a metric that has caused some angst among investors as hyperscalers and cloud providers have committed to plowing more than $800 billion into building out data centers and AI infrastructure, flipped to negative $7.6 billion, compared with an inflow of $18.2 billion a year ago. The flip is driven by Amazon’s $66.1 billion year-over-year increase in equipment purchases, which Amazon said reflects AI investments. 

During the call, Jassy told investors that Amazon now expects to spend $220 billion in capital expenditures in 2026, up from its prior estimate of $200 billion, due to higher memory costs. Even at the elevated level, however, Jassy said Amazon still won’t “have enough capacity to meet all the demand we have in 2026. And I believe this dynamic will also be true in 2027 too.”

Undergirding much of the growth is AWS, which “is booming,” said Jassy in his remarks. 

The acceleration of AWS has been a steady build that began its most recent ascent in the third quarter of 2025 when growth hit 20% and accelerated each quarter until 37% in Q2. During the same span, AWS’ operating margins expanded from 32.9% a year ago to 39.4% while the company has been spending heavily on data center infrastructure. AWS property and equipment grew to $223 billion in Q1 up from $190 billion the quarter before. (Comparable Q2 figures haven’t been published yet.)

On Thursday’s call, Jassy said the growth acceleration is being driven by capacity additions plus other factors. Customers are gravitating toward AWS because it has “the broadest functionality across both cloud core and AI” and “the strongest operational performance and security.”

“As more and more companies are bringing their inference workloads to production, they want it to live near the rest of their workloads and data, and so much more of it lives in AWS than anywhere else,” said Jassy.

And as for Amazon’s core cloud business, which has seen its own boost from post-training reinforcement learning and agent tool use, Jassy noted that 85% of global IT spending is still on-premises. Meaning, plenty of companies are still running their own hardware in their own facilities. 

“That equation is going to flip in the next 10 to 20 years,” he said, adding that AWS is “winning the lion’s share” of enterprise cloud migration plans. 

Customer adoption of Bedrock—Amazon’s platform for accessing AI models from Anthropic, Meta, and OpenAI—has seen solid performance and customers spent more on the service in Q2 than in all prior quarters combined, an analyst noted on Thursday. Jassy’s view is that AWS and Amazon can “have a wildly successful business” without its own frontier model because there won’t be one model “to rule the world.”

“It’s not just Anthropic; it’s not just OpenAI,” Jassy said. “You see increasingly more and more companies being interested in the open models as well, and we have all of them in Bedrock.”

Meanwhile, AWS remains on pace to double its power capacity by the end of 2027, compared with 2025, Jassy said. 

The Pros and Cons of Going on a Cruise



Cruises have become one of my favourite ways to travel. I have been on six of them and planning to go on way more as I get older. I used to think that cruises was for “old” people (i.e. above 65 years old) and growing up in the 90s, I had the “Titanic” fear. However, much of my previous impression of cruises has changed over the years. Below are my personal top pros and cons of cruising.

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