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[YMMV] Webull: 3-3.5% IRA Transfer/Contribution Match

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[YMMV] Webull: 3-3.5% IRA Transfer/Contribution Match


Update 8/27/26: Ending September 1, 2026:

Dear Premium Subscriber,

We are writing to inform you of an upcoming change to your Premium IRA Match benefits.

Effective September 1, 2026, the Match rate for qualifying IRA transfers and rollovers initiated on or after that date will change from 3% to 1%. The 1% Match will apply to up to $250,000 in qualifying transferred assets, for a maximum Transfer/Rollover Match of $2,500.

Update 1/16/25: 3% IRA transfer match from WeBull is back/around at this link. There’s also a 3.5% contribution match. These require having WeBull Premium for 12 months and these have a 5 year holding period. 

Update 5/21/24: Available again.

According to the comments you need to have received a push notification about this to be eligible.

The Offer

Direct link to offer

  • Webull is offering an IRA match of up to 4.5%. Broken down as follows:
    • 3.5% over a five year period (1/5 every year)
    • Additional 1% when you complete a referral or receive a referral

The Fine Print

  • Full terms here
  • Offer Eligibility: This promotional offer (the “Offer”) is open only to individuals who have (i) a brokerage account (a “Webull Brokerage Account”) with Webull Financial LLC (“Webull”), and (ii) a self-directed Traditional or Roth IRA account with Webull (each an “IRA Account”; and each such individual, an “Eligible Customer”).
  • Deposit Bonus Requirements: To receive a Deposit Bonus (as defined below), an Eligible Customer, excluding any Referred Customer (as defined below) must, during the Offer Period, (i) complete one or more Qualifying Deposits to their IRA Account, and (ii) maintain an IRA Account balance equal to or greater than the aggregate amount of all Qualifying Deposits (excluding trading losses) until payment of the final installment of any Deposit Bonus that they are entitled to receive (such final payment date, the “Final Bonus Payment Date”).
  • “Qualifying Deposit” means a deposit or transfer by an Eligible Customer to their IRA Account of any amount of funds or assets held by the Eligible Customer at an institution other than Webull or any of its affiliates that settles during the Offer Period. For the avoidance of doubt, if an Eligible Customer withdraws or transfers assets from their Webull Brokerage Account or from any other account that the Eligible Customer holds with Webull or any of its affiliates and deposits or transfers all or any portion of such assets to their IRA Account during the Offer Period, such deposit or transfer will not be deemed a “Qualifying Deposit”.
  • Referring Customer Bonus Requirements: To receive a Referring Customer Bonus (as defined below), an Eligible Customer must, during the Offer Period, (i) satisfy the Deposit Bonus Requirements specified above, and (ii) complete at least one Successful Referral. “Successful Referral” means a referral by an existing Eligible Customer (the “Referring Customer”) of a new customer (the “Referred Customer”) who (i) has not previously applied for or opened for an IRA Account; (ii) during the Offer Period, applies to open an IRA Account using the Referring Customer’s unique referral link and is approved, and makes a single initial Qualifying Deposit of any amount equal to or greater than $5,000.
  • Referred Customer Bonus Requirements: To receive a Referred Customer Bonus (as defined below), an Eligible Customer must, during the Offer Period, (i) apply to open an IRA Account using a Referring Customer’s unique referral link and be approved in connection with a Successful Referral, (ii) make an initial Qualifying Deposit of any amount, and (iii) maintain an IRA Account balance equal to or greater than the amount of their initial Qualifying Deposit (excluding trading losses) until the Final Bonus Payment Date.
  • Deposit Bonus: An Eligible Customer, excluding any Referred Customer, who satisfies the Deposit Bonus Requirements is eligible to receive a “Deposit Bonus” consisting of a cash payment to their Webull Brokerage Account of an amount equal to 3.5% of the aggregate amount of all of the Eligible Customer’s Qualifying Deposits during the Offer Period. Deposit Bonuses will be paid in installments in the amounts and on the dates indicated below.

Our Verdict

This bonus is annoying in that you need to tie up your funds for five years to get the full bonus. We have seen a lot of IRA offers lately, so personally I wouldn’t go for this deal as you’d be locking yourself out of those deals. Others might prefer a set and forget approach. Please do not share referrals in the comments below.

Judge clears UMG and Sony to accuse Suno of pirating YouTube via ‘stream ripping’ to train its AI

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Universal Music Group and Sony Music Entertainment have formally added a claim to their copyright lawsuit against Suno accusing the AI music company of circumventing YouTube’s anti-downloading technology.

The amended complaint – which you can read here – was filed on Tuesday (August 25) in the US District Court for the District of Massachusetts.

It follows an August 18 order in which Judge F. Dennis Saylor IV granted the labels leave to bring the claim, under Section 1201(a) of the Digital Millennium Copyright Act.

However, in a second August 18 order, Saylor denied the labels’ separate bid to add 61,026 recordings to the 560 already in suit.

The labels first moved to add the stream-ripping claim in September 2025, weeks after Anthropic agreed to pay authors $1.5 billion to settle a lawsuit over its downloading of pirated books.

Suno asked the court to throw the claim out in October 2025, arguing that the practice is not prohibited by the DMCA.

Its lawyers argued that the statute bars circumventing controls on access to a copyrighted work, not controls on copying it, and that YouTube videos are freely accessible to anyone.

The amended complaint alleges that Suno obtained recordings by bypassing YouTube’s “rolling cipher,” an encryption measure that the labels say conceals the URL of the underlying media file.

Suno “acquired many (if not all) of the copyrighted sound recordings in its training data by illicitly downloading them from YouTube using a notorious method of music piracy known as ‘stream ripping,’” the complaint states.

The filing names the tools YT-DL and YT-DLP, which it says Suno used “to circumvent YouTube’s encryption and scrape copyrighted recordings from YouTube.”

The footnote supporting that allegation cites Suno’s own supplemental responses to the labels’ interrogatories.

The labels also say they are not currently alleging that Suno’s outputs are themselves infringing, unless discovery shows that they “directly or indirectly recapture portions of the Copyrighted Recordings.”

According to Saylor’s order, Suno told the labels in May 2025 that it had downloaded audio files from YouTube, and that it had used open-source software tools such as YT-DL and YT-DLP to do so.

Saylor wrote: “The ultimate determination of whether Suno circumvented a technological measure that effectively controls access to plaintiffs’ sound recordings will require a developed factual record on how the technological measure and circumvention tools work.

“At this stage, however, the complaint alleges a plausible claim for violation of § 1201(a)(1), and the Court will grant plaintiffs’ motion for leave to amend.”

The judge added that it was not yet clear how the two tools operate, and that they might reach the content through authorized means or bypass YouTube’s measure altogether.

Saylor was less receptive to the labels’ second motion, which sought to add 61,026 recordings identified through audio-fingerprinting service Audible Magic.

Denying that motion, Saylor wrote: “Plaintiffs are of course entitled to pursue valid claims for copyright infringement, and the magnitude of the alleged infringement is not a defense.

“Nonetheless, simply adding claims involving 61,026 additional works to this lawsuit will have obvious consequences of complexity and delay.”

The judge wrote that summary judgment on Suno’s fair use defense “will likely resolve the predominant issue in this case,” and that the company “is entitled to a timely resolution of that question.”

In other words, Saylor wants to get on with deciding the core component of this case – whether Suno’s use of copyrights was fair use or not – and doesn’t want to get delayed by tens of thousands of new recordings entering the docket.

Saylor noted that the labels could assert the additional works in a separate lawsuit, which would likely be assigned to his own session.

The 61,026 works would have carried a theoretical maximum of more than $9 billion in statutory damages, against around $84 million under the 560-work complaint, as previously reported by MBW.

The amended complaint seeks up to $150,000 per work infringed, plus up to $2,500 for each act of circumvention.

It also carries figures that predate Suno’s recent fundraising, describing a $125 million round that valued the company at approximately $500 million.

Suno raised more than $400 million in June 2026 at a $5.4 billion post-money valuation.

Fact discovery in the majors’ case closes on September 30, according to the judge’s order, with both sides expected to move for summary judgment on whether training an AI model on copyrighted recordings without a license is fair use.

Two days after granting leave in the majors’ case, Saylor cited that ruling to keep an equivalent stream-ripping claim alive in a proposed class action brought against Suno by country artist Tony Justice.Music Business Worldwide

The $40 trillion national debt is growing and Boomers are getting $100k in Social Security benefits

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The $40 trillion national debt is growing and Boomers are getting $100k in Social Security benefits



The United States is entering the most expensive phase of retirement. Some of America’s oldest are eligible for more than $100,000 a year in combined Social Security benefits, while remaining as one of the wealthiest generations in the country. The national debt is rising—just passing $40 trillion this month—and Social Security is set to enter insolvency by 2032, meaning it may already be too late for the generations left behind.

The Congressional Budget Office projected in 2023 federal spending on Social Security and medicare will account for 81% of the increase in mandatory spending between 2023 and 2033. In 2026 alone, increases in Social Security and Medicare spending account for nearly half the projected $362 billion increase in mandatory outlays. Interest on the debt is adding even another layer on the stack of debt pancakes. CBO projects net federal interest costs will exceed $1 trillion in 2026 and rise to $2.1 trillion by 2036. That means the government is spending money to simply service the debt accumulated from previous deficits, even as entitlement programs continue growing.

The state of Social Security appears to have contributed to drastically different generational outlooks on the benefit. A December 2025 survey by the Cato Institute found that only 34% of Gen Z respondents expected Social Security to exist when they reached retirement. Cato’s June 2026 analysis also found that 79% of younger respondents expected some type of cut to their own future benefits.

“The survey revealed that young Americans are the least likely to expect Social Security will exist for them,” the study noted, “the most open to reforms, and the least likely to understand how the program works.”

Social Security is a pay-as-you-go program, meaning most payroll taxes collected from today’s workers are used to pay benefits to today’s beneficiaries. In simpler terms, a part of your paycheck subsidizes a boomer’s benefits—and according to the Cato Institute’s 2025 polling, only 45% of Americans correctly understand how the program works. Under current law, employees and employers each pay 6.2% of wages into Social Security up to an annual taxable maximum, which is $184,500 in 2026. Self-employed workers pay the combined 12.4% rate.

That structure worked far smoother when there were many workers for every retiree. But the demographic math changed—baby boomers are now moving through retirement while younger generations deal with record job market difficulty.

And it doesn’t help that Social Security beneficiaries are getting over double their investment into the program back. A median-wage worker retiring in 2027 is expected to receive roughly $730,000 in lifetime Social Security benefits compared with less than $200,000 in combined contributions from the worker and employer. When the employer contribution is excluded, the lifetime benefits amount to roughly 265% of what the worker personally paid into Social Security. The current system is effectively relying on the workers of today—which include millennials and the younger end of Gen X—to finance retirees.

What the government is going to do about it

The federal government has reached a point where arithmetic becomes unavoidable. The 2026 Social Security trustees report projects that the Old-Age and Survivors Insurance trust fund will be depleted in the fourth quarter of 2032. At that point, continuing program income would cover only 78% of scheduled retirement benefits. The theoretically combined Social Security trust funds are projected to be depleted in 2034, when incoming revenue would cover 83% of scheduled benefits. Without congressional action, that would mean an automatic reduction in benefits.

The Committee for a Responsible Federal Budget estimates the retirement program would face an approximately 22% across-the-board reduction when the retirement trust fund is exhausted. The committee has proposed one way to address the issue—putting a ceiling on the benefits paid to its wealthiest retirees. Dubbed the “Six Figure Limit,” the proposal would cap Social Security benefits at $100,000 annually for a married couple retiring at the normal retirement age, with the limit adjusted for marital status and claiming age. A single retiree’s comparable limit would be $50,000. 

The proposal is aimed at an extremely small group. CRFB estimates the cap would only really affect the top 0.05% of couples in its early years, households with average annual retirement income above $2.5 million and average net worth above $65 million. The organization says the cap would become more consequential over time as Social Security’s maximum benefits continue to rise.

CBS News reported in March that roughly one million individual Social Security beneficiaries receive at least $50,000 a year, meaning a married couple with two such beneficiaries could receive more than six-figures.

The Social Security Administration did not immediately respond to a request for comment from Fortune.

Boomers are rich—but no one else will get their wealth

Baby boomers collectively hold roughly $93 trillion in wealth, according to Visa Business and Economic Insights, but only about $36 trillion is expected to pass to millennials and Gen X over the next two decades. The difference is reflected in taxes, debt, spending during retirement and the concentration of wealth among the richest boomers. After the dedication in liabilities, about $88 trillion remain—and the top 1% holds about one-third of that wealth. Boomers are also expected to spend approximately $16 trillion during retirement on housing, food, healthcare, prescriptions and other expenses.

That means the “Great Wealth Transfer” will not move a $93 trillion pile of assets from retirees to younger Americans. A substantial portion of it will never be inherited, and much of what is transferred will be concentrated among the affluent households. But the Social Security program was created as social insurance, not as a means-tested welfare program. So someone who earned more during their career generally receives a larger benefit, subject to the program’s formula and taxable maximum. An affluent retiree can qualify for a fat Social Security check even when that benefit represents only a small portion of their overall income.

According to the Cato Institute, Social Security should focus more heavily on protecting seniors from poverty while giving younger workers greater opportunity to build private retirement savings. Their analysis points to systems in other developed countries across the world that use combinations of basic pensions, targeted benefits, automatic adjustments and private savings mechanisms.

The United States’ earnings-related benefit structure can produce increasingly generous payments for higher earners, based on the Cato Institute’s report. The organization notes that a maximum-earning worker claiming Social Security at age 70 can receive more than $61,000 a year, while arguing policymakers could reduce benefits for higher-income retirees in a restructuring.

“Policymakers should consider fundamentally rethinking the program’s structure and transform it into a system that ensures seniors are protected from poverty when they can no longer work,” the institute wrote, “while also freeing up resources for younger workers to save more on their own.”

Doctor Loans – MortgageDepot

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Doctor Loans – MortgageDepot


Mortgage Solutions Designed for Medical Professionals

We work with many healthcare professionals who face a unique challenge when purchasing a home. Despite having strong income potential and stable careers, doctors, dentists, pharmacists, veterinarians, and other medical professionals often carry significant student loan debt. They may not have accumulated substantial savings for a large down payment. That’s why we’re excited to offer our Doctor Loan Program, a specialized mortgage solution created specifically for medical professionals.

Home Financing For Your Career

Conventional mortgage programs don’t always account for the financial realities of medical professionals. Our Doctor Loan Program recognizes the long-term earning potential and career stability of healthcare providers, offering creative financing options that can make homeownership more accessible.

  • Up to 100% financing available for qualified borrowers
  • No private mortgage insurance (PMI) requirements on eligible transactions
  • Reduced down payment requirements
  • Flexible student loan debt considerations
  • Financing options for newly practicing physicians and medical residents
  • Preserve savings for investments, emergencies, or practice-related expenses

Eligible borrowers

  • Medical Doctors (MD)
  • Doctors of Osteopathy (DO)
  • Doctors of Dental Surgery (DDS)
  • Doctors of Dental Medicine (DMD)
  • Ophthalmologists
  • Psychiatrists
  • Doctors of Pharmacy (PharmD)
  • Doctors of Veterinary Medicine (DVM/VMD)
  • Doctors of Podiatric Medicine (DPM)
  • Certified Registered Nurse Anesthetists (CRNA with DNAP or DNP)
  • Medical Residents
  • Medical Fellows
  • Medical Interns

Doctor Loans

  • Purchase a home sooner rather than waiting to pay down student debt
  • Minimize upfront cash requirements
  • Avoid monthly mortgage insurance expenses
  • Take advantage of underwriting guidelines that consider future earning potential
  • Focus on building wealth through homeownership

Disclosure

Example payment: The principal and interest payment on a $400,000 30-year fixed-rate mortgage at 8.125% with 100% loan-to-value (LTV) is $2,969.99. The Annual Percentage Rate (APR) is 8.525%, with estimated finance charges of $10,000. Payment example does not include taxes, homeowners insurance, HOA dues, or other applicable costs, which will increase the total monthly payment. Rates are subject to change and were current as of 06/16/2026. All loans are subject to credit approval, underwriting guidelines, and program eligibility requirements. Additional restrictions may apply.

If you’re a medical professional looking to purchase a home, refinance an existing mortgage, or explore your options, contact us to learn more about our Doctor Loan Program.

 

Crypto Trading for Beginners | How it works – Trading raj

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Crypto Trading for Beginners | How it works – Trading raj



#cryptotrading #tradingraj #stockmarket #crypto

Delta exchange India :
Telegram :

Understanding the trading can be overwhelming, but not anymore! In this video, I explain the entire trading in the simplest way possible using fun and relatable examples.

You’ll learn:
✅ Best application to trade crypto
✅ Best way to analyse Crypto
✅ Website used to track crypto

😎 Don’t forget to hit the LIKE button & SUBSCRIBE for more such informative videos! 🚀

Disclaimer: This video is for educational purposes only. We’re sharing our knowledge to help you learn about trading, but trading in any financial market can be very risky and you might end up facing huge losses & sometimes even end up losing your entire capital. It’s important to understand the risks before you start any form of trading. We’re not financial advisors, and the information in this video is not financial advice. Always do your own research and talk to a financial professional if needed. We can’t promise any specific results, as trading involves uncertainty and market conditions that are beyond our control. You are responsible for your own trading decisions, and we won’t be liable for any losses or damages you might experience. By subscribing and liking, you get access to all our free trading content. Enjoy the journey and trade wisely!

#trading #bitcoin #bitcoinmining #learning

source

Education Department Appeals Court Rulings That Struck Down PSLF Employer Rule

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Education Department Appeals Court Rulings That Struck Down PSLF Employer Rule


The Department of Education (via the Justice Department) filed notices of appeal on August 27, 2026 in both lawsuits that struck down the Department of Education’s PSLF employer eligibility rule. The government is taking National Council of Nonprofits v. McMahon to the First Circuit Court of Appeals and Robert F. Kennedy Center for Justice and Human Rights v. McMahon to the D.C. Circuit, filing just before the government’s 60-day appeal window closed.

Both appeals target rulings issued June 30, 2026 — one day before the PSLF Employer rule was scheduled to take effect. In Massachusetts, Judge Myong J. Joun’s 68-page decision (PDF File) vacated the rule as contrary to law, arbitrary and capricious, and a violation of the First Amendment. That decision also resolved the companion case brought by 22 states and the District of Columbia. The same day, the D.C. district court granted summary judgment to the RFK Center and co-plaintiff nonprofits in the parallel case.

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Why It Matters

The rule would have let the Education Secretary disqualify employers found to engage in activities with a “substantial illegal purpose”. One of the big focuses of the lawsuits was that the language was based on the administration’s policy priorities about immigration, gender-affirming care, and DEI programs rather than settled criminal law. Teachers, nurses, and nonprofit workers faced losing credit toward forgiveness if their employer was cut from the program.

The Details

For now, nothing changes for borrowers. Both courts vacated the rule rather than merely pausing it, so the existing PSLF employer rules remain in effect while the appeals play out. But the filings confirm the administration intends to keep fighting for the rule rather than rewrite it.

The notices of appeal (PDF File) are two pages each and contain no legal arguments. The government’s case won’t be visible until opening briefs are filed in the respective courts.

The Massachusetts ruling covered a coalition of 22 states, D.C., five cities and counties, five nonprofit employers, and five employee associations. More than 100 amici backed the challengers while none supported the Department of Education.

The Department could ask the courts to pause (stay) the vacatur while the appeals proceed. No stay motion appeared on either docket as of August 27, but it’s important to watch for any changes. The Department previously tried adding a perjury attestation to PSLF employer forms while the litigation was pending, but that was stopped when the court vacated the rule.

How This Connects

The PSLF Employer rule was finalized October 31, 2025, and drew lawsuits within three days — first from cities and counties, then states and nonprofits. It was one piece of the larger student loan overhaul, alongside the RAP plan and Parent PLUS changes reshaping PSLF strategy.

What’s Next

The appeals now get docketed in the First and D.C. Circuits, with briefing likely stretching into late 2026 and decisions possible in 2027. And, of course, any future losses by the Department of Education could be appealed to the Supreme Court. Borrowers also need to watch for any potential stay of the current rulings, though unlikely.

In the meantime, borrowers should keep certifying employment as usual. The vacated rule has no effect unless an appeals court revives it. And even then, the Department would have to take a lot of action very quickly before any borrowers were at risk of losing future qualifying payments.

Editor: Colin Graves

The post Education Department Appeals Court Rulings That Struck Down PSLF Employer Rule appeared first on The College Investor.

Amazon Promo on Select Household Essentials: $15 Off $50+

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Amazon Promo on Select Household Essentials: $15 Off $50+


Amazon Promo on Select Household Essentials

This article contains Amazon affiliate links.

Amazon is running a promotion where you’ll save $15 when spending $50 on qualifying items. The promotion includes more than 1,000 products across categories like household essentials, beauty, grocery, home improvement, office supplies, and more.

Eligible products shown include brands such as Angel Soft, Dude Wipes, Tero, Arm & Hammer, Bounty, Air Wick, and many others. Here’s how it works:

  1. Add items from the products listed in promotion page.
  2. When you’re done shopping, select Go to Cart.
  3. Use promo code STOCKUPSAVE (if not applied already)
  4. The discount will apply at checkout, if eligible.
  5. Complete payment.

PROMO PAGE

You can save even more by using the right credit card. The best option is the U.S. Bank Shopper Cash Rewards Card which earns 6% cash back. The Amazon Prime Visa card will earn 5% cash back on these purchases. You can also get 5% cash back with Chase Freedom Cards this quarter. Another good option is purchasing Amazon gift cards at Staples or Office Depot with a Chase Ink Business Cash card, so you can earn 5X Ultimate Rewards. Also check out these Shop with Points discounts for even more savings.

Keep in mind that Amazon offers free shipping on orders of $35+, or free next-day shipping on all orders with Amazon Prime. Prime members can also share benefits with a Household member. Students and all 18-25 year olds as well as EBT/SNAP/Medicaid cardholders can get a discounted Prime membership.

Offer Terms

  • Offer only applies to products sold by Amazon
  • Products sold by third-party sellers or other Amazon entities will not qualify for this offer, even if “fulfilled by Amazon.com” or “Prime Eligible”.
  • Offer does not apply to digital content.
  • Offer good while supplies last.
  • Items must be purchased in a single order and shipped at the same speed to a single address.

Guru’s Wrap-up

A 30% discount is pretty solid, especially if you’re stocking up on household staples you were planning to buy anyway. Just make sure all items are part of the promotion before checking out.

 

Disclaimer: As an Amazon Associate I earn from qualifying purchases made through this article. Using links on the site for Amazon purchases is the best way you can support the site as you normally can’t earn cash back for these purchases. But, you should still check shopping portals such as Rakuten, TopCashback, RebatesMe, ShopBack and others for possible cashback. Your support is always greatly appreciated!

Our Favorite Management Tips on Learning from Failure

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<p>A curated list from one of HBR&#8217;s most popular newsletters.</p>

How to Set Up ChatGPT, Claude, or Gemini as a Physician (5 Steps)

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How to Set Up ChatGPT, Claude, or Gemini as a Physician (5 Steps)


Tell me if this sounds familiar. You open ChatGPT, ask it something real, and get back an answer that’s technically fine and completely useless.

Meanwhile someone in your call group swears the same tool saves them an hour a day.

Here’s the thing. That person almost certainly isn’t smarter than you and isn’t doing anything technical. They just spent ten minutes setting the thing up before they started typing, and you didn’t.

It’s not a skill gap. It’s a setup gap. And you close it once.

The whole point is to stop re-explaining yourself every single time you open a new chat. Five things, ten minutes, and then you’re done forever.

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.

If you’ve been circling ideas but still feel stuck, you’re not alone.

PIMDCON, the #1 Real Estate & Entrepreneurship Conference for Physicians, is where doctors finally stop spinning their wheels.

Leave with a plan and the confidence to move.

LEARN MORE ABOUT PIMDCON

1. Tell it who you are

Most people skip straight to the question. No context, no introduction, nothing.

That’s like grabbing a new colleague in the hallway and asking about a complicated case without mentioning you’re a physician. You’ll get an answer. It just won’t be for you.

So say who you are up front. Specialty, experience level, and what kind of answer actually helps you.

Something like:

“I’m a family medicine physician. On clinical topics, assume a medical background and skip the basics. On business and investing, I’m a beginner, so explain more.”

Write it once. Then put it somewhere the tool reads automatically, so you never type it again.

ChatGPT has a custom instructions box under personalization. Claude has an account level instructions setting, plus Projects that carry their own context, which is handy if you want your clinical research kept separate from your real estate research. Gemini does it through Gems, little custom assistants you build once and reuse.

2. Tell it how you want to be talked to

Every one of these tools has a default voice. Slightly formal, longer than you need, and weirdly in love with bullet points. If you know, you know.

That default isn’t wrong. It’s just not built for anybody in particular.

So tell it. Short and direct. Conversational. No bullets unless you ask. No summary at the end restating what it just said. However you want it to be.

This is a tiny change that makes the tool feel like a different product. If you want quick, scannable answers between patients, say that. If you’re drafting a patient handout, you want something that reads like a person wrote it, and that’s a different setting entirely.

Claude has a feature built for this called Styles, where you save a tone preset, name it something like Quick Clinical Answers, and turn it on from a menu. ChatGPT folds tone into the same custom instructions box. Gemini bakes it into each Gem.

If your tool lets you save more than one, build two. One for fast answers. One for longer writing.

3. Tell it what it can never do

This is the step almost nobody does, and it’s the one that matters most when the work has real stakes attached to it.

Before you use AI for anything touching clinical or professional work, write down what’s off limits. Not because the tool is dangerous. Because it’s a lot easier to set the boundary now than to explain an awkward situation later.

Something like:

“Never present anything you tell me as a diagnosis or a clinical recommendation. Treat it all as background I’ll verify myself. Don’t ask me for patient information, and if I start to share any, stop me.”

Put that in the same permanent spot as your personal context. Actually, put it somewhere even harder to skip. A Claude Project or a Gemini Gem built just for clinical topics, with the boundary written into the instructions, means it’s there every time you open that workspace whether you remember it or not.

One sentence usually does it. Write it before the first real conversation, not after something goes sideways.

This is also very useful when your AI is connected to other tools like your emails or schedule; tell it what it can or cannot access.

One thing worth being really clear about, though. That instruction is a guardrail for you. It isn’t legal protection. The consumer versions of these tools aren’t covered by a business associate agreement, so patient information doesn’t go in them no matter what you’ve told the AI to do.

If you want AI in an actual clinical workflow, that’s a conversation with your organization, not a setting you toggle.

4. Tell it to push back

Most people don’t know this is adjustable. It is, and it might be the most useful setting on this list.

A lot of these tools default to agreeable. They confirm. They validate. They rarely tell you your idea has a hole in it unless you specifically ask.

Which is a problem, right? A second opinion that always agrees with you isn’t a second opinion.

Try this:

“Push back when you see a flaw in my thinking. Don’t just tell me what I want to hear.”

That one line changes the character of every conversation after it. You go from a mirror to something closer to an actual sounding board.

Save it for wherever you do your serious thinking, or create it as a custom project. Reviewing a business plan, stress testing a decision, talking yourself out of a deal you’re too excited about. You probably don’t need it when you’re asking for a packing list.

5. Tell it what to remember

Some of these tools remember you between conversations. Some start from zero every time unless you’re working inside a Project or a Gem.

Knowing which one you’re in matters. Otherwise you end up either repeating your whole background daily or carrying information forward that you’d rather not.

And if you’re handling anything private, patient details most of all, check the memory and data settings before you use that tool for that kind of work. Not after.

ChatGPT keeps saved memories under settings, where you can read them, edit them, or shut the whole thing off. Claude keeps your standing preferences separate from individual Projects, so your clinical work and your business planning don’t bleed into each other. Gemini gives each Gem its own knowledge, so what you tell one doesn’t show up in another.

Then go back every few months and clear it out. Old context sticks around. The job you left, the deal that died, the project you finished. It’s all still in there quietly shaping your answers.

You can even surprise yourself by asking your AI how deep it knows about you. Weird, but you’ll get an idea of how much it learns by what you give it.

Unlock the Full Power of ChatGPT With This Copy-and-Paste Prompt Formula!

Download the Complete ChatGPT Cheat Sheet! Your go-to guide to writing better, faster prompts in seconds. Whether you’re crafting emails, social posts, or presentations, just follow the formula to get results instantly.

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Further Reading



Alibaba (BABA) Q1 2027 Earnings Call Transcript

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Alibaba (BABA) Q1 2027 Earnings Call Transcript


Image source: The Motley Fool.

DATE

Thursday, Aug. 20, 2026 at 7:30 a.m. ET

CALL PARTICIPANTS

  • Chairman – Joe Tsai
  • Chief Executive Officer – Eddie Wu
  • Chief Financial Officer – Toby Xu
  • Chief Executive Officer of Alibaba E-commerce Business Group – Jiang Fan
  • Head of Investor Relations – Lydia Lu

TAKEAWAYS

  • Revenue — RMB 269 billion (US$39.6 billion), an increase of 9% year over year driven by momentum in cloud computing and quick commerce segments.
  • Adjusted EBITA — RMB 27.3 billion (US$4.0 billion), a 30% year-over-year decrease reflecting significant investments in technology infrastructure.
  • GAAP Net Income — RMB 10.4 billion (US$1.5 billion), representing a 75% year-over-year decrease primarily due to reduced gains from investment disposals and mark-to-market changes.
  • Non-GAAP Net Income — RMB 20.7 billion (US$3.1 billion), a decrease of 38% year over year.
  • Alibaba Cloud External Revenue — Grew 45% year over year, representing a 22-quarter high for the segment.
  • AI-Related Product Revenue — Reached an annual revenue run rate of RMB 49.5 billion (US$7.3 billion), maintaining triple-digit growth for 12 consecutive quarters.
  • Cloud Adjusted EBITDA Margin — 11.6% to 12.0%, reflecting improved economies of scale and stronger pricing power in a supply-constrained market.
  • MaaS ARR — Surpassed RMB 16 billion as of Aug. 2026, including revenue from proprietary and third-party models.
  • Capital Expenditures — RMB 67.7 billion (US$10.0 billion), a 75% year-over-year increase focused on expanding AI infrastructure.
  • Free Cash Flow — Outflow of RMB 44.7 billion (US$6.6 billion), compared to an RMB 18.8 billion outflow in the prior-year period due to higher cloud infrastructure spending.
  • Net Cash Position — Approximately $30.7 billion as of June 30, 2026, providing liquidity for continued investment.
  • Share Repurchases — $162 million used to repurchase 13.4 million ordinary shares during the quarter.
  • Alibaba E-commerce Group Revenue — RMB 205.9 billion, an increase of 4% year over year.
  • Customer Management Revenue — RMB 82.5 billion, a 7% year-over-year decrease, though it would have grown 1% excluding contra revenue impacts from new business programs.
  • Quick Commerce Revenue — RMB 53.3 billion, a 45% increase driven by Freshippo and Taobao Instant Commerce.
  • AI Labs and Applications Loss — RMB 13.9 billion, reflecting increased costs for model training and marketing for the Qwen application.
  • 88VIP Membership — Approximately 64 million members, representing double-digit year-over-year growth.
  • Qwen Model Adoption — Downloads exceeded 3 billion globally with more than 300,000 derivative models built on the platform.
  • Zhenwu M890 Processors — Serving more than 650 customers on Alibaba Cloud for both training and inference workloads.
  • MaaS ARR Target — RMB 30 billion by the end of the current fiscal year.
  • Quick Commerce Profitability — Management expects the segment to reach overall profitability in fiscal year 2029.
  • Diluted Earnings per ADS — RMB 3.71 (US$0.55), a 79% year-over-year decrease.

Need a quote from a Motley Fool analyst? Email [email protected]

RISKS

  • Wu warned that international e-commerce growth has been pressured by tariff policies and the geopolitical environment.
  • Xu stated that free cash flow was an outflow of RMB 44.7 billion, “mainly attributed to the investment in cloud infrastructure,” reflecting high upfront hardware costs.
  • Wu noted that the domestic e-commerce landscape faces short-term macroeconomic challenges despite current growth initiatives.

SUMMARY

Management reported a realignment of corporate segments into four groups to integrate full-stack AI capabilities and commerce platforms. The company stated that it is increasing investments in AI infrastructure to address compute demand that currently exceeds available supply. Management indicated that AI-related revenue now represents 35% of external cloud revenue and is contributing to segment margin expansion through higher gross margins and proprietary chip substitution.

  • CEO Wu indicated that the current API-based monetization for large language models is a transitional approach, stating that achieved AGI would enable a model focused on delivering specific products and operational results.
  • Management reported that servers equipped with AI chips typically reach breakeven within three years and generate positive free cash flow for at least two years thereafter.
  • CFO Xu noted that AliExpress achieved operating profit during the quarter, driven by logistics optimization and enhanced cost efficiencies.
  • The company reported that its Zhenwu M890 supernode can support inference for foundation models with more than 2 trillion parameters.
  • Management indicated that Alibaba Cloud has reduced the delivery time for hyperscale AI data centers to 100 days to accelerate global infrastructure expansion.
  • Management expects the transaction volume of quick commerce for non-food categories to exceed that of food categories within the next fiscal year.

INDUSTRY GLOSSARY

  • MaaS (Model as a Service): A cloud-based service providing access to machine learning models without requiring users to manage the underlying infrastructure.
  • ARR (Annual Revenue Run Rate): A projection of annual earnings based on current performance over a shorter period.
  • CMR (Customer Management Revenue): Revenue from services provided to merchants, including advertising and commissions.
  • 88VIP: Alibaba’s premium loyalty membership program for high-spending consumers.
  • T-Head (Pingtouge): Alibaba’s semiconductor division focused on developing proprietary chips for AI and cloud computing.
  • Qwen (Tongyi Qianwen): Alibaba’s family of large language models and AI applications.
  • UE (Unit Economics): The direct revenues and costs associated with a business model expressed on a per-unit basis.
  • Bailian: Alibaba Cloud’s platform for deploying and managing Model as a Service applications.

Full Conference Call Transcript

Operator: Good day, ladies and gentlemen. Thank you for standing by. Welcome to Alibaba Group’s June Quarter 2026 Results Conference Call. [Operator Instructions]. I would now like to turn the call over to Lydia Lu, Head of Investor Relations of Alibaba Group. Please go ahead.

Lydia Lu: Thank you. Good day, everyone, and welcome to Alibaba Group’s June Quarter 2026 Earnings Conference Call. Joining the call today are Joe Tsai, Chairman; Eddie Wu, Chief Executive Officer; Toby Xu, Chief Financial Officer; Jiang Fan, Chief Executive Officer of Alibaba E-commerce Business Group. Before we get started, I would like to remind you that today’s discussion may contain forward-looking statements based on management’s current expectations that are subject to risks and uncertainties. We also make reference to non-GAAP financial measures. Reconciliations between GAAP and non-GAAP measures are included in today’s earnings press release and investor presentation. Our comments will be on year-over-year comparisons unless we state otherwise.

A replay of the call will be available on our website later today. With that, I would like to turn the call over to Eddie.

Yongming Wu: Good evening, good morning, and welcome to Alibaba Group’s Earnings Call for the First Quarter of Fiscal Year 2027. Over the past quarter, Alibaba’s strategic AI investments have translated into robust results with a total group revenue growing 9% year-over-year. AI commercialization has also accelerated across the board. Alibaba Cloud’s external revenue grew 45% and EBITDA increased 133% year-over-year, continuing to deliver on our commitment to accelerate growth. Revenue from AI-related products has maintained a triple-digit growth for the 12th consecutive quarter with annual revenue run rate surpassing RMB 49.5 billion around USD 7.3 billion. It is the core engine of Alibaba’s Cloud’s growth acceleration.

I’ll now walk you through 4 key areas: AI and cloud commercialization, full-stack AI capabilities, AI application ecosystem and consumption business. First, AI and cloud commercialization accelerated across the board and is expected to sustain high growth going forward. This quarter, Alibaba Cloud’s external revenue growth accelerated to 45%, a 22 quarter high, while adjusted EBITDA margin reached 11.6%. Notably, this 45% growth was broad-based driven by compute storage Model as a Service, MaaS, and AI applications. We proactively scaled back low-margin business continuing to improve the quality of our growth. This quarter, annual revenue run rate from AI-related products exceeded RMB 49.5 billion and its share of Alibaba Cloud’s external revenue rose to 35%.

AI-related products generate significantly higher gross margins than the average cloud portfolio. Our recurring AI-related product revenue spans multiple layers, AI compute, MaaS and AI applications. This multilayered mix of AI revenue sources and monetization models means growing customer demand at any layer converts directly into commercial opportunity for us. This structural advantage will underpin sustained rapid growth in recurring AI-related product revenue going forward. The surge in AI agents directly drives demand for tokens and GPU compute while also significantly boosting demand for our traditional cloud products across CPU compute, storage, databases and networking. Alibaba Cloud is undergoing a comprehensive upgrade to an agentic cloud.

Based on the latest data, the ARR of our model and application services, including MaaS, has surpassed RMB 16 billion. Based on current market feedback and our contract pipelines, compute demand will continue to outstrip supply. As we continue to ramp up our supply, our AI and cloud revenue growth will accelerate further in the coming quarters, alongside continued improvement in profitability. Second, our full stack AI capabilities continue to strengthen, marked by the scaled commercialization of proprietary chips, faster model iteration and a thriving open source ecosystem. This quarter, deepening synergy between proprietary T-Head chips and proprietary foundation models further improved our AI commercialization efficiency.

T-Head has established a full stack proprietary silicon portfolio, spanning GPU, CPU and networking chips. As of early August, the Zhenwu chips have served more than 650 customers on Alibaba Cloud. The supernode instance powered by T-Head’s next-generation Zhenwu M890 AI processor recently launched on Alibaba Cloud at commercial scale. We expect supply to continue ramping up in the second half of the year to meet strong customer demand. Alibaba Cloud’s Zhenwu M890 supernode can efficiently run inference workload for foundation models with more than 2 trillion parameters, both Kimi K3 and Qwen 3.8 Max are already using it to provide MaaS services to external customers.

At the data center layer, Alibaba Cloud has cut the delivery time for hyperscale AI data centers to 100 days, a world-leading pace that will significantly speed up our global compute infrastructure build-out. At the model layer, our model release cadence has intensified over the past months with major iterations across our large language, image, audio, video and music models, all ranking among the world’s top tier. Last week, we opened the modeled weights of Qwen 3.8 Max with 2.4 trillion parameters and the Qwen 3.8-27B model series. To date, the Qwen model series has been downloaded more than 3 billion times globally with more than 300,000 derivative models built on it.

We believe a thriving open source model ecosystem drives greater demand for our cloud computing services creating a virtual cycle. Third, our AI native applications span both enterprise and consumer use cases driving rapid growth in token consumption. On the enterprise side, we launched QwenWork, a new AI productivity product built for enterprise workforce scenarios, delivering agent capabilities at scale. We expect productivity agents to become another engine of ARR growth. On the consumer side, the Qwen app continued to steadily grow its user base and is expanding the range of its value-added offerings. Through close coordination between Alibaba Token Hub and Alibaba Cloud, we’re running a highly efficient commercial flywheel across compute models, tokens applications and monetization.

Fourth, our e-commerce businesses remained solid this quarter. In quick commerce, we continued to narrow losses substantially while growing business scale by 45% with unit economics improving quarter-over-quarter. Having crossed the AI commercialization inflection point last quarter, we’re now seeing growth accelerate and margins expand this quarter. Our AI businesses own capacity to self-fund and sustain itself is strengthening giving us greater confidence to keep investing. Looking ahead, AI has become Alibaba’s most certain growth engine, we will stay strategically disciplined and drive long-term growth through our full stack AI capabilities. I’ll now hand over to Toby to walk you through our financial results. Thank you.

Toby Xu: Thank you, Eddie. Our strategic priorities in AI + Cloud and consumption businesses backed by disciplined investments delivered strong results this quarter. Cloud segment revenue growth further accelerated to 45% with its EBITDA margin sequentially rising to 12%. AI-related product revenue continued to drive this momentum marking the 12th consecutive quarter of triple-digit growth and accounting for 35% of external cloud revenue. The strong performance demonstrates growing customer adoption of our full stack AI capabilities, spanning AI agents, models, cloud infrastructure and preparatory chips as well as our enhanced scale efficiencies and the robust pricing power in a supply-constrained market. On consumption, Taobao Instant Commerce continued to improve its unit economics while maintaining market share.

Overall e-commerce EBITDA remained relatively stable year-over-year. To realize synergies across our commerce platforms and strengthen our full stack AI capabilities, we have implemented strategic realignment of certain businesses in our financial reporting. Starting from this quarter, our segment reporting will present the following: first, Alibaba E-commerce Group; second, AI Cloud and Compute Services; third, AI Labs and Applications; and number four, all others. Now let’s look at the financial results for this quarter. Total revenue increased 9% year-over-year to RMB 269 billion, driven by the strong momentum in cloud business and quick commerce.

Total adjusted EBITDA decreased 30% to RMB 27.3 billion, primarily attributable to the investment in technology, partly offset by the improved operating results in our cloud business as well as enhanced operating efficiencies across various businesses. Our GAAP net income was RMB 10.4 billion, a decrease of 75%, primarily due to the decrease in income from operations and decrease in net gains from disposal of investments and mark-to-market changes of our equity investments. Operating cash flow this quarter increased by 11% to RMB 22.9 billion compared to RMB 20.7 billion in the same quarter last year. Free cash flow was an outflow of RMB 44.7 billion compared to an outflow of RMB 18.8 billion in the same quarter last year.

The decrease was mainly attributed to the investment in cloud infrastructure. CapEx was RMB 67.7 billion this quarter, reflecting our continued investments in AI infrastructure to meet strong and growing customer demand. The significant year-over-year increase is due to several reasons, including fluctuations in procurement cycles, increase in CPU compute capacity driven by anticipated growing customer adoption of AI agents and higher pricing of a broad range of chip components. As of June 30, 2026, we held approximately USD 30.7 billion in net cash, excluding debt with maturities beyond 5 years, our net cash position stands at approximately [ USD 46.5 billion ]. This balance sheet strength gives us confidence to invest for robust growth.

Our AI + Cloud investment has a clear path to attractive ROIC. Our servers equipped with chips typically reach breakeven within 3 years. With a 5-year useful life, we expect them to get positive free cash flow, at least in the 2 years following breakeven. For the quarter ended June 30, 2026, we repurchased shares of an aggregate consideration of USD 162 billion. We remain committed to maximizing long-term shareholder returns through disciplined capital allocation across investments for AI + Cloud business growth, share buybacks and dividends. We will adjust our priorities as market conditions and the strategic needs evolve. Now let’s first look at our e-commerce businesses.

The new Alibaba e-commerce group reflects our strategic focus on unlocking significant synergies across our domestic and cross-border e-commerce businesses. Starting from this quarter, we will present Alibaba E-commerce Group’s revenue as the following: first, China e-commerce; second, China quick commerce; third, international e-commerce; and fourth, global wholesale. Revenue for Alibaba e-commerce group was RMB 205.9 billion, an increase of 4%. Customer management revenue decreased by 7%. Excluding the contra revenue impact from the new business development program, customer management revenue would have grown by 1% year-over-year. Revenue from China quick commerce business was RMB 53.3 billion, an increase of 45%, driven by Freshippo and Taobao instant commerce.

Alibaba E-commerce Group’s adjusted EBITDA remained relatively stable year-over-year at RMB 39.7 billion, underscoring our cost discipline against the backdrop of increased investments in user experiences and technology. Taobao instant commerce continued to improve its unit economics quarter-over-quarter while maintaining market share, driven by higher average order value and enhanced fulfillment logistics efficiency. In addition, AliExpress achieved our pre-profit this quarter. We aim to maintain steady profit in our conventional e-commerce business while continuing to drive profitability improvement in our quick commerce business. Now let’s review the business updates and results of AI Cloud and Compute Services, which comprises the Cloud Intelligence Group and T-Head. The year-over-year growth of total revenue and revenue from external customers both accelerated to 45%.

Revenue from Alibaba Cloud also accelerated growing 45% year-over-year. We are confident the growth rate will further accelerate in the coming quarters. This quarter’s AI-related product revenue was RMB 12.4 billion, implying an annual — revenue run rate of RMB 49.5 billion. It delivered a 12th consecutive quarter of triple-digit growth and accounted for 35% of external cloud revenue. The adjusted EBITDA margin expanded to 12%, driven by improved economies of scale and a stronger pricing power of AI-related products amid tight market supply.

We expect EBITDA margin to further expand steadily in the coming quarters by improving resource utilization, optimizing model portfolio and innovating new scenarios, we are accelerating the growth of AI + Cloud business and driving greater benefits of scale. AI Lab and Applications comprises AI model labs Qwen Consumer Business Group and QwenWork. Its adjusted EBITDA was a loss of RMB 13.9 billion, primarily due to our increased investment in AI capabilities and higher inference costs related to Qwen app. The loss significantly narrowed quarter-over-quarter due to the reduction in marketing expenses for Qwen app. We expect the segment loss to narrow over the coming quarters driven by improving efficiency in both model training and marketing spend on Qwen app.

We have launched our frontier language coding, video, audio, image and music models or delivering top-tier performance. 250 million have had their first AI-driven shopping experience through Qwen app’s agentic features across an expanding range of e-commerce and other services since the launch of Qwen app. All other segment revenue remained stable at RMB 28.8 billion. All other adjusted EBITDA was a loss of RMB 3.3 billion primarily due to our increased investment in technology.

AI has progressed from incubation to commercialization at scale as we expand our market share, strengthen AI leadership and improving operating efficiency, we are gaining greater strategic and financial flexibility to make disciplined and sustained investments in both full stack AI capabilities and consumption opportunities driving secular growth and greater value for our shareholders. Thank you. That’s the end of our prepared remarks. We can open up for Q&A.

Lydia Lu: Thank you, Toby. We will now begin the Q&A session. You’re welcome to ask questions in Chinese or English. A third-party translator will provide consecutive interpretation. In the case of any discrepancy, our management statements in the original language will prevail. Operator, please start the Q&A session. Thank you.

Operator: [Operator Instructions] Your first question comes from Alicia Yap with Citigroup.

Alicis a Yap: Also congrats on your solid cloud performance. Could management please comment on the reasons and the drivers for the significant increase in the CapEx this quarter? And also, what is the expected CapEx trend for the coming quarters? And are these — are there any updates to the existing 3-year CapEx budget that you have of this RMB 380 billion that you mentioned before? And also, we would appreciate if management can also provide a breakdown of CapEx allocation across the different services like the training costs and all that? And then also, what is management expected return on the invested capital for these investments?

Unknown Executive: [Interpreted] Thank you very much for the question. It’s an important question, and I’d like to take the opportunity perhaps to explain generally what our business model is for AI and our expectations around CapEx going forward. So indeed, last February, we announced a 3-year capital investment plan with total investment of RMB 380 billion as of the end of the June quarter this year, we had already spent RMB 190 billion with progress broadly in line with our expectations. While this quarter spending of RMB 67.1 billion is somewhat higher, hardware deliveries follow different procurement cycles. There can be fluctuations in the cadence and pace of hardware deliveries. So it’s not evenly distributed across different quarters.

So the increase primarily reflects volatility in those equipment delivery schedules. At the same time, we increased procurement of CPUs this quarter as we are witnessing a substantial surge in demand driven by the agent-centric era. Of course, rising prices for semiconductor components have also contributed to this trend. So I don’t think we should take the spending for this quarter and multiply it by 4 to come up with an annualized figure for the year or to expect that there’ll be a steady linear progression. The build-out has been progressing at a steady pace, but that is the overall situation. [Interpreted] Next, let me expand on our full stack AI business model. This is an asset-heavy business model.

If you think about all of the different ways that AI is monetized and can be monetized, be it through software subscriptions, be it through API calls, through Models as a Service, through training, inference. In all of these different respects, you need compute centers to run and to monetize. So it’s only possible to monetize when you have that compute capacity in place. So what that means is that we need to be investing upfront in order to be able to grow this business model and monetize across all of those different areas. So that’s why beginning in 2025, we began a heavy investment cycle in hardware.

And this is really a function of that asset-heavy business model, as I explained, in order to be able to capture that future growth. We first need to make these CapEx investments to build out the necessary compute capacity. [Interpreted] Next, let me explain why we see return on invested capital in AI-related CapEx as highly certain. There’s consensus across the industry that the current shortage in AI compute will not be resolved until at least 2030. So industry-wide then, it makes sense that there should be high certainty in our investments in AI compute. Based on average gross margins today, roughly, we can breakeven on AI-related CapEx in 3 years.

And of course, average gross margin continues to rise, and we expect to be able to shorten that payback period, say, to 2.5 years. [Interpreted] Following that 3-year payback period, these AI assets that we’ve invested in can achieve very positive and robust cash flow. So to give you some direct examples A100 purchased in 2020 or A100 purchased in 20 — sorry, V100 purchased in 2018, even today are still running at full capacity. [Interpreted] Additionally, we have 3 means that we can leverage to further enhance gross margin and return on invested capital.

First is we can continue to develop state-of-the-art models and enhanced gross margin on AI products themselves and continue to expand a higher-margin Model as a Service, MaaS, businesses, and we can adopt our product mix across IaaS and across software to achieve higher gross margin on the portfolio as a whole. And as a result of improving gross margin, you’ve already seen an overall increase of 4.4 percentage points in Alibaba Cloud’s overall segment profitability, bringing it this quarter to 11.6%. So that represents initial validation of that thesis. [Interpreted] A very important piece of this is our ability to deploy our own proprietary chips.

As you know, our own T-Head proprietary chip span GPUs, CPUs and networking chips, which are the critical chipsets for AI. And in AI data centers, the most expensive components are, of course, chips and storage. So we have a very significant advantage in being able to deploy our own proprietary chips. As we ramp up deployment of our own proprietary chips in our data centers as they account for an increasing proportion of total chips and replace commercially procured chips, we can expect to see substantially higher gross margin as well as profitability. [Interpreted] Third and also very importantly, we have means to monetize and get better efficiency of utilization of our own cash flow.

These include, for example, co-building data centers with partners as well as pre-charging and receiving pre-payments for compute-based services. So these are important ways in which we can further enhance ROIC. [Interpreted] So through these 3 different methods, we can shorten the payback period for AI CapEx, for example, to 2.5 years or even 2 years. And we can apply a simple framework to understand this. At our current level of gross margin for AI products and under the assumption of a 3-year payback period on CapEx. Theoretically, keeping our growth rate below 33% would already enable positive cash flow. However, that is not our strategic choice at this time.

Given that AI remains in a very early stage, we’re committed to aggressively investing in CapEx and proactively scaling up to drive our rapid business expansion. As our product gross margin improves and our proprietary chip substitution rate increases, our payback period will shorten to 2.5 years or even less. And so under those circumstances, while pursuing growth of over 40%, we’ll also be able to maintain positive cash flow. So that is our long-term strategic direction.

Operator: Your next question comes from Charlene Liu with HSBC.

Charlene Liu: I come from HSBC. First, when we get an update on the latest developments in quick commerce and under the reclassification of multiple business lines, which are regrouped under the Alibaba E-commerce Group. Can you talk about the future strategic focuses of these lines of businesses. Let me quickly translate the question myself. [Foreign Language]

Unknown Executive: [Interpreted] Okay. Thank you very much for the question as well as for the translation. In the new fiscal year, indeed, we’ve realigned our e-commerce business segments. And moving forward, we’ll be updating progress on 4 core areas: China e-commerce, quick commerce, international e-commerce and global — B2B global wholesale. Let me then briefly share the strategic priorities and key considerations for each of these 4 segments in the period ahead. So starting with China e-commerce. While the domestic e-commerce landscape faces short-term macroeconomic challenges, our long-term strategy centers on strengthening core supply capabilities and at the same time, we aim to leverage AI to enhance the overall shopping experience and improve operational efficiency across the board.

So first, regarding supply, since last year, Taobao and Tmall have focused on supporting original merchants, including branded sellers, while simultaneously unlocking the potential of high-quality white label suppliers from key industrial clusters.

Unknown Executive: [Interpreted] We will continue to strengthen our partnerships with leading brand merchants, helping them achieve stable and sustainable business growth. Tmall remains the most critical operational hub for both major brands and many original merchants. At the same time, we are diving deeper into industrial clusters to source high-quality products directly from their origins. We are supporting more manufacturing factories and operating directly on our platform and leveraging our platform AI capabilities to enable white-label merchants to adopt a simpler and more efficient managed operation model. And the share of transactions being generated through that industrial cluster managed model continues to rise steadily.

In the past quarter, during the recent 618 shopping festival despite certain macroeconomic challenges, the outcomes were aligned with our expectations and notably, core merchants achieved solid growth. [Interpreted] At the same time, we see significant opportunities for AI across both the supply and demand sides of e-commerce. On the consumer side, we will continue to launch new experiences and scenarios powered by AI, such as multimodal search and virtual try-ons. Our goal is twofold: first, to use AI technology to enhance the experience and efficiency of existing shopping scenarios, and we’ve already observed that AI has driven significant efficiency gains in our product recommendations; and secondly, to drive new kinds of AI-driven interaction.

On the merchant side, we observed that merchants are already widely adopting AI in their operations. We’re exploring ways to leverage AI across various operational links to boost merchant capabilities, particularly in data analytics, advertising and marketing and customer service where merchants can derive clear benefits. And going forward, we’ll also collaborate with Qwen Office to launch AI agents that are specifically tailored for e-commerce scenarios. [Interpreted] Next, on quick commerce. After more than a year of investment and development, Taobao Instant Commerce has undergone substantial changes in scale and in market share with significant improvements across user mind share, supply diversity, logistics experience and order volume.

Last quarter, while maintaining growth in both users and orders unit economics, UE, substantially improved and losses significantly reduced. On that basis, we will accelerate the integration of businesses such as Freshippo and Tmall supermarket to develop the nonfood categories growth within the Quick Commerce business, and we’ll place a particular focus on expanding our front warehouses. Over the past year, Freshippo has accelerated the development of front warehouses leading to a year-over-year increase in GMV. [Interpreted] Meanwhile, Quick Commerce will continue to expand its category coverage and innovate in key areas to enhance the consumer experience.

We expect the transaction volume of quick commerce for nonfood categories to surpass that of food categories within the next fiscal year, driving growth in many different physical goods categories across the overall e-commerce business. The Quick Commerce business is expected to achieve overall profitability in FY ’29. In the long term, we believe it has the potential to contribute 30% of the platform’s total GMV, becoming the second growth curve for our e-commerce business. [Interpreted] Third is international e-commerce. In the short term, our international e-commerce business has indeed been affected by tariff policies and the geopolitical environment pressuring growth.

That said, despite the complex market environment, our cross-border business has delivered significant improvement in profitability while maintaining growth in transaction volume. In terms of both transaction scale and profitability, we believe the cross-border business holds long-term growth potential. In addition, our local e-commerce platforms in international markets such as Turkey and the Middle East are growing rapidly and operating efficiency in markets such as Southeast Asia continues to improve. [Interpreted] Fourth is global B2B. Our B2B businesses, including the 1688 and alibaba.com platforms have grown consistently over the past 2 decades and we see that AI technology will bring profound changes to our B2B platforms and may even fundamentally reshape existing business models.

In particular, the agentic model will play an increasingly important role in B2B transactions. We’ve launched Accio Work, which is an AI agent for cross-border merchants and it had already attracted over 50,000 paying merchants shortly after its launch. AI is comprehensively transforming the way that B2B merchants do business, especially cross-border merchants. We believe that building on our 2 years of know-how in this — 2 decades of know-how in this field, we have the opportunity to create entirely new business models and commercial opportunities in B2B and in cross-border trade in the AI era. [Interpreted] Overall, over the past few years, we have completed a new strategic positioning for our e-commerce businesses across several key areas.

And going forward, we aim to continue leveraging our strengths from supply chain synergies to AI technology to unlock greater growth potential for the e-commerce segment in the AI era, while building a more diversified revenue and profit structure to drive steadier development of the overall segment.

Operator: Your next question comes from Yang Bai with CICC.

Yang Bai: [Interpreted] My question is about the cloud and AI business. We’ve seen that Alibaba Cloud’s revenue growth has been accelerating quarter-by-quarter reaching 45% this quarter. We know the company has previously set a long-term goal of exceeding USD 100 billion in external cloud revenue over the next 5 years. And you’ve also now indicated that growth will remain on an accelerated trajectory in the quarters ahead. So I’d like to ask 2 questions. First, looking ahead to the coming quarters, what do you anticipate being the pace of growth in the cloud business. What are the core drivers underpinning the continued acceleration of cloud computing growth?

And then secondly, as you mentioned, the industry is now in a phase of relatively tight capacity in terms of supply of compute. And you just mentioned that, that supply demand dynamic may shift around 2030. So I’d like to ask from an even longer-term perspective, what are the fundamental growth drivers for the cloud business? And do they differ from those in the short term?

Unknown Executive: [Interpreted] Thank you for the question. And I think I can expand on this in 3 different areas. I can start by looking at our current business and the relevant data. Secondly, I can discuss the drivers for growth. And then thirdly, I can share with you our long-term perspective based on that analysis. So let me begin with the first part, covering our current business and the key metrics. So as you’ve seen, external revenue for the AI and Cloud segment has been accelerating now for 9 consecutive quarters. And in this last quarter, growth has already accelerated to 45%.

We’re seeing very strong customer demand and our offerings boast a distinct competitive advantage compared to those of other cloud providers. As a result, we expect revenue growth to continue accelerating over the coming quarters. We’ve observed that AI-related products generated RMB 12.4 billion in revenue this quarter. And so if we convert that into an annualized U.S. dollar figure, that works out to USD 7.3 billion in annual revenue. Looking ahead to the next quarter, our own forecast is that, that same annualized revenue for AI quarters — next quarter will approach USD 10 billion. So our growth rate remains exceptionally strong.

At the same time, we also expect our EBITDA margin to improve quarter by quarter sequentially over the next few quarters. Additionally, something very important in respect to the cloud business is growth in demand for MaaS. We’ve seen very significant growth in demand for MaaS this quarter, coupled with ongoing improvement in inference efficiencies. So the ARR of our MaaS business has now surpassed RMB 16 billion. And actually, let me clarify. That’s the latest data as of August, it’s already surpassed RMB 16 billion. [Interpreted] Next, let me expand on the growth drivers within our business model. So it’s important to understand that Alibaba’s investment model for AI is fundamentally different from that pure-play AI companies.

We are pursuing an intensive strategy across the full stack including chips, including AI cloud infrastructure and including models. And we maintain a leading position in the industry across all 3 of those most critical domains. Moreover, we believe that the development of AI and technology across the industry is still in its early stages. Looking forward, different stages of technological development, the core commercial value within the AI industry may shift across different layers, including chips, cloud computing models and applications.

Our full stack investments ensure that we can deliver optimal service capabilities and the best value for money positioning us favorably in the industry going forward and ensuring that within each stage of technological development, it’s possible for us to maintain competitiveness and sustained growth momentum. [Interpreted] Next, let me look ahead to what we think is going to be the most important growth driver over the next 1 to 2 years in the short term. So we’ve seen exponential demand for commercial insurance services as of the end of 2025. This exponential growth in demand for inference has marked a fundamental shift in the model, whereby compute has now become the core asset driving AI revenue.

And today, all AI-related revenue models are centered on AI compute. And at the same time, there’s a consensus across the industry, as I mentioned, that compute will remain in a shortage of supply for some time to come. At the same time, the higher gross margins of MaaS inference services have also made a major difference if compute was once a cost center, traditionally, compute has now been transformed into a core productive asset whose value generation is positively correlated with revenue. So high-priced computing power remains in short supply across the industry precisely at a time where you have widespread adoption of GPUs across diverse use cases.

So pricing models are tending to converge on the most high margin, the most margin generative monetization approaches. So this is driving the pricing models for nearly all GPU-related products. Moreover, Alibaba, both comprehensive multimodal model capabilities. Our models are state-of-the-art level within the industry, giving us a distinct advantage in realizing the value of that compute power and providing a robust anchor for our pricing strategy. When it comes time to price for new customers or to sign — reassign contracts with existing customers as they renew, we can adopt more healthy pricing models. And so we expect to see this as a very positive short-term driver for improving margin in the coming year plus.

[Interpreted] Next, let me talk about the scale effects and networking effects, which are very important long-term growth drivers in AI cloud. For the past couple of years, a lot of people have asked what is the super app for AI. And the answer to that is that the real super application is compute, cloud-based AI compute because all of these different workloads need to run on a full stack of AI cloud compute, including training, inferencing, AI software and agents requiring GPUs, CPUs, storage, databases, virtualization as well as harness tools among others.

So AI cloud is like a super city in which workload is the residents and continually iterating full stack AI cloud services or the urban infrastructure, which in turn attracts more new residents and enhances the stickiness of the existing residents. So this is where you see an extremely powerful network effect and scale effect. [Interpreted] Given that we operate the largest number of data centers across any Asian cloud provider, we benefit from the strongest economies of scale. At the same time, the large-scale deployment of our proprietary T-Head AI chips allows us to avoid the high price premiums associated with procuring expensive commercial GPUs thus avoiding erosion of our gross margins.

And with our state-of-the-art performance in our proprietary models, we possess strong pricing power for our compute resources. So looking ahead from the perspective of industry development trends and our own product strength, the long-term revenue growth trend and margin expansion trend are exceptionally strong. And as a result, we’re highly confident in our ability to achieve our goal of RMB 100 billion in external cloud revenue by 2030. And we have good visibility into achieving gross margin of 20%.

Operator: Your next question comes from Yuan Liao with CITICS.

Yuan Liao: [Interpreted] Congratulations on the strong quarterly results and especially the progress made in the AI sector. So I have a follow-up question on the MaaS business. As Eddie mentioned earlier, ARR as of August has exceeded RMB 16 billion in last quarter, I believe you stated that the target for year-end is to surpass RMB 30 billion in MaaS ARR. So I’m wondering, given the progress to date, do you anticipate making any adjustments to that year-end goal? And then additionally, within the MaaS business, what are the respective shares of our own proprietary models versus third-party models.

And as model-related competition intensifies and more open source models emerge how all these factors possibly affect gross margin and profitability in the MaaS business?

Unknown Executive: [Interpreted] Thank you for the question. Yes, indeed, growth in Bailian’s MaaS business is very rapid. And in — as of August, we reached RMB 16 billion or surpassed RMB 16 billion in ARR. So given the current growth momentum as well as the pipeline of new models slated for launch, we remain confident that we will achieve our year-end target of RMB 30 billion ARR by the end of the year. [Interpreted] So on our MaaS platform, our own proprietary model still account for the majority of the revenue. But having said that, revenue from third-party models is also not small. And having said that, perhaps let me talk a little bit about how we see different model capabilities.

A lot of customers tend to need to use or want to use multiple different models in their own AI applications because those different models they can draw and have different characteristics or different capabilities. So having more open source models on platforms like ours like Bailian to provide inferencing is a good thing for us and for Bailian. When it comes to gross margin, the level of gross margin that we can achieve on a platform like Bailian from hosting our own proprietary models versus third-party models is actually very similar. It’s highly comparable.

We’re really developing those proprietary models on the one hand in order to keep creating higher levels of model intelligence and also as part of our ultimate drive to achieve AGI. But simply from the perspective of the MaaS business, the level of gross margin from those 2 kinds of models is actually very comparable. But overall, having a prosperous and flourishing open ecosystem with many of these open source models on it is highly favorable for a cloud provider like Alibaba Cloud.

Operator: Your final question comes from Alex Yao with JPMorgan.

Alex Yao: [Interpreted] I’d like to come back to Eddie’s earlier remarks, he spoke at length about how Alibaba is developing a full stack AI ecosystem. My question really is in which layer of that full stack ecosystem, do you think value will accrete and monetization will be concentrated. We saw just after it has been released for 3 months that you open sourced the weight of your flagship model, Qwen 3.8 Max. At the same time, your proprietary chips are also proving successful, now serving over 600 external customers.

I’m wondering if this means that the future value will accrete mainly in the compute layer or perhaps in the orchestration layer and not necessarily in the model layer or do you think that the value will accrete to different layers in different stages of development of the industry. And in the long term, if you think that value and monetization will largely be concentrated in the hardware and compute layers then how should we think about competition going forward, given that it will be a government-led process for allocating a lot of that hardware and compute capacity?

Unknown Executive: [Interpreted] Thanks. That’s a very professional question, and really it’s a matter of long-term judgment. So I think it’s inherently associated with a high level of uncertainty. But what I can say is that we are investing in the full stack. And what that means is that whichever layer represents the greatest value and no matter how that may shift across layers in different periods of time. All of those layers are part of our ecosystem. I guess I can share with you my own short-term view namely in the short-term perspective, I think that most of the value will be in chips and in AI cloud infrastructure.

It’s a pattern that we can see not just in China but globally across a lot of different companies when a technology is in its early stages and especially when there’s a shortage of supply. Lots of the value tends to be concentrated in the infrastructure and in the core hardware. In this case, chips and storage. So in Alibaba’s case, we’ve integrated our compute power, our cloud infrastructure and our AI inference into one core business segment. [Interpreted] Let me turn next to where the ultimate commercial value will be realized from these AI models. It’s a question around which there’s a lot of debate within the industry and indeed, there are different views even inside our own company.

So here, I’m just sharing my own personal opinion. But in my personal view, I think that the current monetization model for large language models through APIs is just a short-term approach, a short-term transitional approach and is certainly not the ultimate business model. Our company has invested a tremendous amount of compute across our entire platform, but the objective is not simply to be able to generate that kind of short-term API revenue. I think when we get to the stage where we’ve accomplished AGI or we’re close to achieving AGI at that point, the ultimate business model will be delivering actual products, delivering actual results that clients are looking for.

It will be conducting the actual R&D that delivers products and that delivers operations. So the reason that all these different AI model companies are investing so heavily and engaging in an arms race today is not simply to be able to compete to provide that API-based service. It’s because they have to eyes on that ultimate end game, where I think that the monetization level will be significantly higher, be much higher than what you see today selling the service through API calls. [Interpreted] In terms of hardware, I’d like to add a few thoughts regarding our T-Head proprietary chips.

I know it’s a topic about which we haven’t communicated a lot with investors in the past, but the last generation of T-Head chips we’ve already manufactured over 500,000 of them and shipped. And then the latest generation in August has already been deployed on AI — Alibaba’s AI cloud as supernodes. And I think we’re one of the only companies that’s able to deploy such proprietary chips, domestic chips at scale. One thing that’s really unique about our T-Head chips, domestically manufactured chips, is that they are designed with GPU architecture as their core technical foundation, and they can very well support both training and inference workloads.

So there are now already several hundreds of companies that are leveraging these chips via Alibaba Cloud for both inference as well as for model training and these span companies across Embodied AI, autonomous driving as well as large model companies. So in terms of our generation 2 of chips, we are going to start developing them in the second half of this year. And we expect them to boast exceptionally high compute power as well as extremely robust interconnection bandwidth, making them fully capable of serving as a direct replacement for existing chips. So I think we’re in a really, really unique position in the chip sector, especially when it comes to large scale model training.

So I don’t think that there’s any government-led compute supply allocation scheme that could produce chips with such truly strong competitiveness. So I think that the T-Head’s future is highly certain as a very key and core component of Alibaba Cloud, and we remain highly confident in our core competitive strength in this area. I’ve interacted with a lot of different engineers across China. And I can tell you that these chips have a very broad audience with engineers across a wide range of different engineering domains. [Interpreted] So to sum up, I think that our T-Head ships are definitely the best among domestic Chinese chips for supporting both training and inference across a wide range of different industries.

So we really are #1 in the industry. And then I think in terms of future production capacity and deployment, we can confidently claim to be at least 1 of the top 2. But in terms of our ability to actually reach customers with AI chips, Alibaba Cloud is the largest player by market share in China’s cloud and AI market. So I think we have a very strong edge when it comes to channel distribution. So from this perspective, I am highly confident in the long-term commercial value of T-Head chips.

Lydia Lu: Thank you very much. We appreciate your support, and we look forward to updating you on our progress next quarter. Thank you.

Operator: Thank you. That does conclude our conference for today. Thank you for participating. You may now disconnect. [Portions of this transcript that are marked [Interpreted] were spoken by an interpreter present on the live call.]

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