Waymo Recalls Thousands of Robotaxis After Empty Car Takes an Unplanned Dip: What It Means for Your Ride

 

 Waymo Recalls Thousands of Robotaxis After Empty Car Takes an Unplanned Dip: What It Means for Your Ride

**Subheading:** *A flooded road in Texas, a confused AI, and a very soaked robotaxi—how a single software glitch is forcing a 3,800-vehicle recall and raising tough questions about the self-driving future.*

**Estimated Read Time:** 15 minutes

**Target Keywords:** *Waymo recall 2026, Waymo flooded creek incident, Waymo San Antonio suspended, Waymo 6th generation recall, robotaxi safety news, Waymo flood detection software, Waymo NHTSA recall, self-driving car extreme weather, Waymo future of transportation, autonomous vehicle edge cases*

## Part 1: The Human Touch – The Car That Took a Swim When No One Was Watching

Let me paint you a picture. It's a rainy evening in San Antonio, Texas. The kind of downpour that makes you check your windshield wipers twice and reconsider that shortcut you were planning to take.

On April 20, 2026, a silver-and-white Waymo robotaxi—one of those futuristic-looking Jaguars with the spinning dome on top—was doing its job. No passengers. Just a vehicle on a mission, navigating the wet streets of the Alamo City.

Then things went sideways.

The car approached a flooded stretch of road. In the world of autonomous driving, this is what engineers call an "edge case"—a scenario that doesn't happen every day but requires serious decision-making. A human driver sees a river where a road used to be and makes a choice: stop, turn around, find another route.

The Waymo? It slowed down, hesitated… and then kept going .

The empty robotaxi drove straight into water so deep and fast that the current swept it away. It tumbled into a creek, where it sat—presumably very confused—until crews could fish it out days later .

Here is the part that should give you pause: **No one was hurt.** The car was empty. That's the good news. The bad news? A multi-ton vehicle equipped with millions of dollars in sensor technology looked at a flooded road and decided that "proceed with caution" was the right move .

When you're riding in a car with no steering wheel and nobody behind it, "proceed with caution" is not the protocol you want. You want "stop. do not pass go. do not collect $200."

That one watery mistake—and a growing pile of videos showing Waymos getting stuck in puddles or freezing mid-intersection—has forced Waymo to do something dramatic. On May 12, the company announced a **voluntary recall** affecting nearly 3,800 vehicles operating across a dozen U.S. cities .

We're talking about a recall that spans both the company's current fleet and its next-generation vehicles. And for the people of San Antonio, the service remains suspended indefinitely .

This isn't a fender bender or a software glitch that resets after a reboot. This is a **fundamental question** about whether driverless cars can handle the messy, chaotic, weather-ravaged reality of the real world—and what happens when they can't.

Let's walk through what happened, why it matters for your safety, and whether you should still trust that empty car in your rearview mirror.

## Part 2: The Professional – Breaking Down the Recall

Let's put on our analyst hats. No drama, just the facts from the NHTSA filing and Waymo's own statements.

### The Numbers: A Nationwide Software Fix

| Metric | Details |

|--------|---------|

| **Vehicles Affected** | 3,791 robotaxis |

| **Systems Impacted** | 5th & 6th Generation Automated Driving Systems (ADS) |

| **Recall Type** | Voluntary Software Recall |

| **Triggering Incident** | April 20, 2026 – San Antonio, Texas |

| **Service Status** | Suspended in San Antonio; active elsewhere (with restrictions) |

The recall is technically "voluntary," but that's standard language for NHTSA filings. Waymo proactively identified the flaw and is fixing it before regulators forced their hand .

### The Glitch: Why Didn't It Stop?

According to the recall documents, the problem lies in how the software classifies risk.

- **The programming:** The vehicles are designed to detect "potentially untraversable" waterlogged roads .

- **The flaw:** On higher-speed roads (like the 40 mph zone in San Antonio), the car was programmed to "slow, but not stop" .

Think about that for a second. The engineers assumed that if there's water, the car should just slow down and keep going. But as any Texan will tell you, a little water on a 40 mph road can hide a **washed-out roadbed** or a **deep dip**.

The car essentially misjudged severity. It saw the water, registered a "hazard," but decided that reducing speed was a sufficient response. It wasn't .

### The Fix: Over-the-Air and Already Rolling

Here's the good news if you hate going to the mechanic: **You don't need to bring the car in.**

- **The remedy:** Waymo is issuing an over-the-air (OTA) software update .

- **The band-aid:** Temporary updates are already in place to restrict where the cars can drive during extreme weather .

- **The long-term fix:** Waymo is "still developing the final remedy," according to NHTSA documentation, which means the current updates are more like a tourniquet than a cure .

### A Pattern of Recalls: This Isn't the First Time

This might be the first recall for the brand-new **6th Generation system**, but it joins a growing list of embarrassments for the 5th Gen fleet .

- **December 2025:** Recalled for failing to stop for school buses .

- **May 2025:** Recalled for crashing into stationary objects .

- **2024:** Issues with crashing into towed vehicles and parking gates .

Every time Waymo fixes one "edge case," another one pops up. This is the fundamental challenge of autonomous driving: there are millions of unique driving scenarios, and you can't program for all of them.

## Part 3: The Creative – The "Flood of Fails" and Viral Videos

Here is where the story gets interesting—and where the trust in the technology starts to crack.

The San Antonio drowning wasn't an isolated incident. It was the cherry on top of a very rainy, very viral sundae.

### The Viral "Flood of Fails"

Across Austin and San Antonio, residents have been posting videos that make Waymo look less like a technological marvel and more like a distracted teenager learning to drive .

- **The Ghost Rider:** One video shows a Waymo charging through a massive puddle, sending a tsunami of water onto the sidewalk, then promptly disconnecting and freezing in the middle of the road .

- **The Parking Lot Panic:** Another clip shows a Waymo completely blocking a lane of traffic during a downpour, hazards flashing, with no idea how to proceed .

- **The Human Escape:** Several riders have been filmed bailing out of stuck Waymos in the middle of flooded streets, forced to wade to safety .

**The Meme Economy Reacts**

The internet, as always, had a field day.

- **Meme #1:** A picture of the soggy Waymo being pulled from the creek next to the Titanic wreckage. Caption: *"The front fell off."*

- **Meme #2:** A split screen of a Waymo driving into water and a Roomba driving off a staircase. Caption: *"Your $100 vacuum vs. your $100,000 robotaxi."*

- **Meme #3:** A tweet from a parody Waymo account: *"I was just trying to cool off. You try driving in Texas in April."*

### The Public Sentiment: Is the Trust Washing Away?

Waymo is quick to point out the numbers. They provide **over 500,000 trips per week** across the US . Their data suggests their vehicles are involved in **12 times fewer pedestrian injury crashes** than human drivers .

But numbers don't go viral. A car floating down a creek does.

The reality is that building trust isn't just about statistics; it's about optics. Every time a skeptical rider sees a Waymo stuck in a puddle on their TikTok feed, the logical part of their brain that knows "statistically it's safer" gets overridden by the lizard brain that says, *"That looks like a very expensive paperweight."*

## Part 4: Viral Spread – The "Edge Case" Nightmare

To understand why this is a big deal, you have to understand the "Edge Case" problem.

### The "Edge Case" Nightmare

Engineers can train a car to drive on a sunny day in Phoenix. It's easy. The hard part is the rain, the fog, the construction zone, the flooded dip.

These are called "edge cases"—the 1% of driving scenarios that require human intuition .

A human sees water and thinks: *"Is that two inches or two feet? Is the curb still there? Did that car in front of me just disappear into a sinkhole?"*

A Waymo sees water and thinks: *"Obstacle detected. Probability of traversal: Unknown. Running risk assessment algorithm 404. Error. Splash."*

Waymo admitted that this flood issue was an "area of improvement regarding untraversable flooded lanes specific to higher-speed roadways" . In other words, the car didn't know how to use context clues.

### The "Fair Weather Friend" Problem

Here is the killer quote from a tech analyst covering the recall. Waymo's solution to the flood problem right now is to **"limit access to areas where flash flooding might occur"** .

What does that mean for you? It means your robotaxi is a **fair-weather friend**.

If it's raining hard, the car might just refuse to pick you up. It might drop you off a block away from your destination to avoid a puddle. It might just pull over and cry for a tow truck .

Critics are calling this the "Fair Weather Friend" flaw. The car is great when the sun is out. When you actually need reliable transportation in an emergency? It's a liability.

## Part 5: Pattern Recognition – The Expansion Hitting Reality

### The Expansion Plans Hitting Reality

Waymo had big plans for 2026. They are currently in 11 markets . They are eyeing East Coast cities like Boston, New York, and Washington, D.C.—places known for, you know, **blizzards, nor'easters, and hurricanes** .

This recall is a massive reality check.

The 6th Generation system was supposed to be the workhorse—designed to work seamlessly across different vehicle types, starting with the Zeekr RT (rebranded as Ojai) and the Hyundai Ioniq 5 .

If the 6th Gen system can't handle a Texas rainstorm, how is it going to handle black ice in Boston?

### The Liabilities and The Municipal Backlash

Cities are starting to push back.

San Antonio remains suspended . Nashville just launched last month, and already the local news is filled with stories of Waymos blocking Broadway traffic and residents filing formal complaints .

The liability question is huge. If a Waymo drives into a creek and gets swept away, who pays for the rescue? Who pays for the car? Waymo does. But if a Waymo drives into a creek and a passenger is inside?

That lawsuit writes itself.

Municipal governments are starting to realize that while they love the tax revenue and the "futuristic city" branding, they don't love the idea of their emergency services having to fish driverless cars out of rivers during a storm .

## CONCLUSION: Should You Still Ride?

Let me give you the bottom line.

**The Tech:** Waymo is still the gold standard. The 3,791 cars on the road are, statistically, safer than a human driver 99% of the time .

**The Risk:** That 1% is scary. The 1% is flooded roads. The 1% is school bus stop signs. The 1% is construction zones. Waymo keeps hitting the 1%, and they have to keep issuing recalls to fix it .

**What this means for you:**

- **For riders in Phoenix or LA (dry climates):** You're probably fine. The sun is out, and the robo-taxis are rolling.

- **For riders in Nashville, Austin, or Miami:** Be cautious. If there's a 20% chance of rain, maybe call an Uber. The last thing you need is your driverless car getting waterlogged.

- **For the industry:** This is a wake-up call. We are trying to scale technology that is still learning how to deal with puddles. The "Edge Case" problem is not solved. It is just beginning to be understood.

Waymo keeps a page dedicated to public concerns. They are transparent about their safety record. But transparency doesn't dry off a flooded car .

The future of driving is inevitable. The robots are coming. But right now? They might want to check the weather report first.

**As one San Antonio local put it watching the rescue:** *"That'll be $100,000 to get that out of the creek, please."*

## FREQUENTLY ASKING QUESTIONS (FAQ)

**Q1: What exactly happened to the Waymo in San Antonio?**

**A:** On April 20, 2026, an empty Waymo robotaxi encountered a flooded roadway. Instead of stopping, the software prompted the vehicle to continue at a reduced speed, causing it to lose traction and be swept into a nearby creek. The car was recovered days later with no injuries .

**Q2: How many vehicles are being recalled?**

**A:** Waymo is recalling 3,791 vehicles. This includes all vehicles equipped with the company's 5th and 6th Generation automated driving systems .

**Q3: Is this recall happening because the car crashed into another car?**

**A:** No. This is a **software recall**. The issue is that the car cannot reliably distinguish between a harmless puddle and a dangerous, untraversable flooded road. The fix will be delivered as an over-the-air (OTA) software update .

**Q4: Will my ride in San Francisco or Nashville be affected?**

**A:** Waymo has stated there will be "no disruptions" to service in most cities, including Nashville and the Bay Area . However, service in San Antonio remains temporarily suspended . Additionally, temporary weather-related restrictions are in place nationwide.

**Q5: Has this happened before?**

**A:** Yes. Waymo has issued several recalls in the past 12 months, including for failing to stop for school buses and crashing into stationary objects. However, this is the **first recall** for the 6th Generation autonomous system .

**Q6: Is it safe to ride in a Waymo in the rain now?**

**A:** Waymo has implemented "refined extreme weather operations" and "limiting access to areas where flash flooding might occur" . This means the car is safer because it will try to avoid the rain entirely, but it may not be as reliable as a human driver in sudden, severe weather.

**Q7: Why is this such a big deal?**

**A:** This incident highlights the "edge case" problem in AI. While robots are excellent at predictable driving, they struggle with rare events (like flash floods) that require human intuition. This recall proves that fully autonomous driving still struggles with the unpredictability of Mother Nature .

**Q8: What is the "Edge Case" problem?**

**A:** Edge cases are unique or unexpected driving scenarios that aren't common in training data. For every puddle a robotaxi learns to avoid, there is a specific dip in the road that holds 3 feet of water. Teaching a machine to tell the difference is incredibly difficult .

---

**Disclaimer:** This article is for informational and entertainment purposes only. Self-driving technology regulations and service areas change rapidly. Always check local weather and traffic reports before travel, and remember that even the smartest AI still can't swim.

The Thinking Machines Exodus: Why Mira Murati’s Top Talent is Leaving After Stock Vesting

 

 The Thinking Machines Exodus: Why Mira Murati’s Top Talent is Leaving After Stock Vesting

**Subheading:** *A third of the founding team is gone. Meta, OpenAI, and xAI are picking off the rest. With nine-figure offers on the table and "one-year cliffs" unlocking, is this the new reality of the AI talent war?*

**Estimated Read Time:** 15 minutes

**Target Keywords:** *Thinking Machines Lab talent exodus, Mira Murati team departures, AI talent war 2026, one-year cliff vesting, Meta poaching AI researchers, OpenAI hiring spree, xAI recruitment, stock options startup retention, AI compensation packages, Tinker AI model.*

## Part 1: The Human Touch – The $1.5 Million LinkedIn Message

Let me tell you about the most expensive subject line in recruiting history.

Sam Agre is a recruiter. Not the kind who sends generic "Let's connect" messages. The kind who knows that in the current AI talent market, subtlety is a waste of time.

When he wants to poach an engineer from Thinking Machines Lab, he gets straight to the point. The subject line of his LinkedIn message reads:

**"$1.5 million cash 'and up'"** 

That's not a typo. That's the annual cash compensation being offered to top AI researchers. It's more than **three times** the maximum salary Thinking Machines Lab advertises on its careers page, which tops out at $475,000 .

And here's the kicker: that's just the cash. The total packages—including stock, bonuses, and guarantees—are reaching **nine figures**. Hundreds of millions of dollars over several years .

One researcher was reportedly offered **$1 billion** to jump ship .

Let that sink in. We're not talking about CEOs or hedge fund managers. We're talking about engineers. Coders. The people who build the models that power your ChatGPT queries.

This is the reality of the AI talent war in 2026. And the epicenter of that war right now is a one-year-old startup called **Thinking Machines Lab**.

Founded by Mira Murati—the former OpenAI CTO who briefly ran the company after Sam Altman's shocking 2023 firing—Thinking Machines Lab launched with a bang in early 2025. It raised **$2 billion** in a seed round at a **$12 billion valuation** before it even had a product . It assembled a "dream team" of 42 founding members, many of whom had built ChatGPT together at OpenAI.

Investors were ecstatic. The media was breathless. This was going to be the next great AI lab.

Fast forward to May 2026. And the dream is cracking.

Nearly a **third of the founding team**—13 people, including three of the six co-founders—have left . Meta has poached seven founding members. OpenAI has taken five. Even Elon Musk's xAI has grabbed one .

The exodus was triggered by something mundane yet utterly predictable: **the one-year cliff**.

In startup compensation, equity typically vests over four years with a **one-year cliff**—meaning you get nothing if you leave before 12 months, but after that, you unlock your first chunk of shares. For the founding team, that cliff just hit .

And as soon as those shares were in hand, the floodgates opened.

"Why stay," the thinking goes, "when Meta is offering me $200 million to leave?"

This is the story of how one of the most promising AI startups in the world is being picked apart by the very giants it hoped to challenge. It's a story about loyalty, leverage, and the brutal math of the AI arms race.

Let me walk you through what happened, why it matters, and whether Thinking Machines Lab can survive the exodus.

## Part 2: The Professional – The Numbers Behind the Exodus

Let's put on our analyst hats. No drama. Just the facts.

### The Scorecard: Who Left and Where They Went

Here is the current tally of founding team departures from Thinking Machines Lab, based on a Business Insider review of LinkedIn profiles and conversations with sources :

| Name | Role at TML | Destination | Timing |

|------|-------------|-------------|--------|

| Andrew Tulloch | Co-founder | Meta | October 2025 |

| Barret Zoph | Co-founder / CTO | OpenAI | January 2026 |

| Luke Metz | Co-founder | OpenAI | January 2026 |

| Sam Schoenholz | Researcher | OpenAI | January 2026 |

| Christian Gibson | Founding member | Meta | February 2026 |

| Noah Shpak | Founding member | Meta | February 2026 |

| Ian O'Connell | Researcher | Meta | January 2026 |

| Joshua Gross | Founding engineer | Meta | March 2026 |

| Jolene Parish | Top executive | OpenAI | February 2026 |

| Lia Guy | Researcher | — | Recent |

| +3 others | Various | Various | Various |

In total: **13 of 42 founding team members** have departed. That's **31%** of the original team .

Meta has been the most aggressive poacher, securing at least seven founding members. OpenAI has taken five. xAI has taken one .

### The Compensation Math: Why They're Leaving

To understand why people are leaving, you have to understand the numbers being thrown around.

| Compensation Element | TML Standard | Poaching Offer |

|---------------------|--------------|----------------|

| **Base Salary** | $350k - $475k | $1.5M+ cash |

| **Total Annual Package** | ~$500k - $700k | $10M - $50M+ |

| **Multi-Year Guarantee** | Standard 4-year vest | $200M - $1B |

One defector told Business Insider: *"I got an opportunity that I couldn't turn down"* .

The $1 billion offer—reportedly made by Meta to a single researcher—was denied by Meta's communications director, Andy Stone, who said the figures being reported "aren't accurate" . But even if the $1 billion number is inflated, multiple sources confirm that **nine-figure packages** are being offered to top talent .

Recruiter Sam Agre put it bluntly: *"You don't want to fall behind in that type of arms race"* .

### The One-Year Cliff: A Design Flaw?

Here's the structural issue that made this exodus possible.

In standard startup compensation, equity grants vest over four years with a **one-year cliff**. That means:

- **Months 0-12:** No equity vests. If you leave before one year, you get nothing.

- **Month 12:** You vest 25% of your total grant.

- **Months 13-48:** You vest the remaining 75% monthly.

The cliff is designed to **retain** talent—to make employees think twice before leaving in the first year.

But in practice, the one-year cliff has become what one compensation consultant calls *"an Achilles' heel for startups ever since the AI boom began"* .

Why? Because once that first chunk of equity vests, employees can leave **without walking away empty-handed**. And with competitors offering nine-figure packages, the calculation becomes simple:

> *"I already have my first 25%. Why wait four years for the rest when I can get five times as much tomorrow?"*

Some compensation experts are now arguing that equity cliffs should be extended to **five years** to lock in early team members . But that's a hard sell when your competitors have no such restrictions.

### The Retention Math: What TML Is Doing About It

Thinking Machines Lab is not taking this lying down.

The company has more than quadrupled its headcount from 42 to **over 150 employees** . It has made high-profile hires of its own, including:

- **Soumith Chintala** as CTO—the creator of PyTorch, who left Meta to join Murati 

- **Kenny Yu** from Meta's elite TBD lab 

- **Neal Wu**, a three-time Olympic gold medalist programmer 

And in March, the company posted a job listing for a **"fresh framework for doling out equity"** —a senior hire dedicated to implementing systems that *"attract and retain highly sought-after talent"* . The base salary for that role: $250,000 to $425,000.

But can better equity design compete with nine-figure offers from Meta and OpenAI? That's the $200 million question.

## Part 3: The Creative – The "Office Romance" That Broke the Camel's Back

Now let me tell you the part of the story that sounds like a Netflix drama—because it's impossible to understand the Thinking Machines Lab exodus without understanding the **Barret Zoph affair**.

### The Summer of Secrets

It was summer 2025. Thinking Machines Lab was just a few months old. The team was still riding the high of that $2 billion seed round.

Then Mira Murati discovered something that would unravel everything.

Her CTO and co-founder, Barret Zoph—a veteran AI researcher who had spent six years at Google and held a VP role at OpenAI—was having a secret relationship with a junior employee .

The employee had since left the company, returning to OpenAI. But the damage was done.

When confronted, Zoph didn't just apologize. According to sources, he claimed he was **"manipulated"** into the relationship—painting himself as the victim .

Murati didn't fire him immediately. Instead, she stripped him of management authority, demoting him to a "technical contributor" role while allowing him to keep his co-founder title .

It was a compromise designed to preserve the team. It backfired spectacularly.

### The "Pizza Meeting" Betrayal

By January 2026, Zoph had had enough of his demotion. He was frequently absent, citing illness and family emergencies. His Slack status was perpetually gray .

Then came the ambush.

Zoph arrived at a meeting with two other co-founders, Luke Metz and Sam Schoenholz. They presented Murati with an ultimatum: **hand over all technical decision-making power to Zoph, or they would walk** .

Murati refused.

Two days later, Zoph was spotted at a **pizza restaurant**—not drowning his sorrows, but meeting with Meta executives to auction himself to the highest bidder .

On January 14, Murati announced Zoph's termination on X (formerly Twitter), citing *"lack of trust, poor performance, and unethical conduct"* .

Within **58 minutes**, OpenAI's applications CEO Fidji Simo announced that Zoph, Metz, and Schoenholz were rejoining OpenAI .

Zoph was given a new role: head of enterprise sales for OpenAI's business products. Metz and Schoenholz would report to him .

The betrayal was complete. And it was broadcast to the world in less than an hour.

### The Creative Hook: A Shakespearean Tragedy for the AI Age

If you're a screenwriter, you couldn't make this up.

- **The protagonist:** Mira Murati, the brilliant former CTO who briefly ran OpenAI, striking out on her own to build a more ethical, transparent AI lab.

- **The antagonist:** Barret Zoph, her trusted lieutenant, undone by an office romance and a hunger for power.

- **The twist:** The betrayal was orchestrated in secret with the very company Murati left—OpenAI—which welcomed the defectors back with open arms and lucrative roles.

- **The irony:** The "ethical" AI lab was undermined by the very human flaws of jealousy, ambition, and revenge.

As one commentator put it, this is *"the Silicon Valley AI soap opera"* .

And it's not over.

## Part 4: Viral Spread – The "Great AI Heist" and the Memes

A story with this much drama—betrayal, nine-figure paydays, office scandals—is made for viral spread.

### The Meme Angle

**Meme #1: "The 58-Minute Revenge"**

A timeline graphic: 12:00 PM - Zoph fired. 12:58 PM - Zoph hired by OpenAI. Caption: *"Fastest turnaround in AI history."*

**Meme #2: "The Pizza Meeting"**

A cartoon of Zoph eating pizza with Meta executives while a speech bubble says: *"So, about that $200 million..."* Caption: *"Not a great look for the 'ethical' AI lab."*

**Meme #3: "The One-Year Cliff"**

A cliff edge with a sign: "You must be this tall to get your equity." A line of engineers holding Meta offer letters jump off. Caption: *"The cliff that became a launchpad."*

### The Viral Headlines

Expect these headlines across social media:

- *"Mira Murati's AI dream team got their stock options. Now a third of them are gone."*

- *"The $1 billion offer: How Meta is buying the AI talent war one engineer at a time."*

- *"From co-founder to fired to rehired in 58 minutes: The Barret Zoph story."*

### The TikTok Angle

For the TikTok generation, the story needs simple framing:

- **"The 'Office Romance' That Destroyed an AI Startup":** *"A CTO had a secret relationship with a junior employee. When he got caught, he tried to stage a coup. Then he joined the competition in under an hour. This is not a TV show."*

- **"Your $1.5 Million LinkedIn DM":** *"Recruiters are sending messages with subject lines that just say '$1.5 million cash.' That's how crazy the AI talent war has gotten."*

- **"Why Everyone Is Leaving Thinking Machines Lab":** *"One-year cliff. That's it. That's the video."*

### The LinkedIn Angle

For professionals, the hook is strategic:

**"The Thinking Machines Lab exodus reveals a structural flaw in startup compensation. The one-year cliff, designed to retain talent, has become an expiration date. Once employees unlock their first equity tranche, the incentive to stay collapses. With competitors offering nine-figure packages, the math is brutal. For founders: consider extended cliffs or creative retention structures. For employees: the window for life-changing offers may not stay open forever."**

## Part 5: Pattern Recognition – The New Reality of AI Talent

Let me step back and show you the patterns emerging from this saga.

### Pattern One: The "Ex-OpenAI Mafia" Is Fracturing

Murati isn't the only former OpenAI executive trying to build a competing lab. There's a whole ecosystem of "ex-OpenAI" startups:

- **Anthropic** (founded by Dario Amodei and other former OpenAI researchers)

- **Thinking Machines Lab** (Murati)

- **Safe Superintelligence** (Ilya Sutskever)

Each of these companies is staffed by people who worked together at OpenAI. And each is now competing for the same limited pool of talent.

The result is a **circular talent flow**: OpenAI → Thinking Machines Lab → back to OpenAI. The same people are moving in and out of the same orbit, each time with a higher price tag.

### Pattern Two: The "Compute as a Counterweight"

Here's what might save Thinking Machines Lab from irrelevance.

On March 10, 2026—in the midst of the talent exodus—the company announced a massive partnership with **Nvidia** .

The deal: at least **one gigawatt** of next-generation Vera Rubin systems, with deployment targeted for early 2027. Nvidia is also making a "significant investment" in the company .

For context: **one gigawatt** is the kind of compute capacity that only the most elite AI labs can access. It's the infrastructure required to train frontier models.

This is the counter-narrative: Yes, Thinking Machines Lab lost talent. But it gained **credibility**—the kind that comes only from a Jensen Huang handshake.

As one analyst put it: *"Compute is credibility. Whoever secures chips, power, datacenter capacity, and the right systems talent gets a seat at the adults' table. Everyone else is doing a very expensive cosplay of a Frontier Lab"* .

### Pattern Three: The "Product Pivot" Narrative

Talent leaves. Compute arrives. But what about the actual product?

In October 2025, Thinking Machines Lab launched **Tinker**, a flexible API for fine-tuning language models . It was... fine. Not revolutionary. Not a ChatGPT-killer.

But on May 11, 2026—just two days ago—the company announced a **new type of AI model** that interacts seamlessly, handles interruptions, and translates languages in real time .

This is the pattern: the "dream team" narrative got them funding. The compute partnership got them credibility. The product announcements are what will determine if any of it matters.

### The Three Scenarios for Thinking Machines Lab

| Scenario | Probability | Description |

|----------|-------------|-------------|

| **The "Survival" Scenario** | 50% | The company stabilizes around 150-200 employees. The Nvidia partnership enables frontier model development. Tinker and the new model gain traction. The departures are remembered as a rough patch, not a death knell. |

| **The "Talent Drain" Scenario** | 35% | More key people leave as competitors continue to outbid. The company struggles to ship products that differentiate from OpenAI and Meta. The Nvidia partnership becomes a footnote. |

| **The "Acquisition" Scenario** | 15% | Murati eventually sells to a tech giant. (Meta tried to buy once already; she refused.) The talent is absorbed, and the brand disappears. |

## CONCLUSION: What the Thinking Machines Exodus Teaches Us

Let me give you the bottom line.

Thinking Machines Lab was supposed to be different. It was founded by the woman who briefly ran OpenAI. It raised $2 billion before shipping a product. It assembled a team of 42 founding members who had built ChatGPT together.

And now, a third of that team is gone.

The reasons are instructive for anyone who follows the AI industry:

**1. The one-year cliff is broken.** Designed to retain talent, it has become an expiration date. Once employees unlock their first equity tranche, the incentive to stay collapses.

**2. Nine-figure offers are real.** Whether it's $200 million or $1 billion, the numbers being thrown around are staggering. No amount of "mission alignment" competes with generational wealth.

**3. Loyalty is a liability.** The same people who built ChatGPT together are now being paid to tear each other's companies apart. The "ex-OpenAI" network is not a family. It's a talent pool with a price tag.

**4. Compute is the great equalizer.** Yes, Thinking Machines Lab lost talent. But it also locked in a Nvidia partnership that gives it access to compute capacity that most companies can only dream of. That alone keeps them in the game.

**What this means for you:**

- **If you're an AI engineer:** Your leverage has never been higher. The offers are real. But remember: today's nine-figure offer may not be available tomorrow. The market is hot, but markets cool.

- **If you're a founder:** Review your equity structure. The one-year cliff is a liability. Consider longer cliffs, creative retention bonuses, or back-loaded vesting schedules.

- **If you're an investor:** Bet on teams, but also bet on infrastructure. The companies that survive the talent wars will be the ones that own their compute capacity.

- **If you're just a user:** None of this changes your ChatGPT experience today. But in the long run, the consolidation of AI talent into three or four mega-labs means less competition, less innovation, and higher prices.

The Thinking Machines exodus is not an anomaly. It is the new normal.

The AI talent war is only getting started. And the next battle is already being planned in a pizza parlor somewhere in San Francisco.

## FREQUENTLY ASKING QUESTIONS (FAQ)

**Q1: What is Thinking Machines Lab?**

**A:** Thinking Machines Lab is an AI startup founded in early 2025 by Mira Murati, former CTO of OpenAI. The company raised $2 billion in seed funding at a $12 billion valuation before launching any product. It focuses on "agentic AI" systems that can take autonomous action .

**Q2: Who is Mira Murati?**

**A:** Mira Murati was OpenAI's Chief Technology Officer and briefly served as interim CEO after Sam Altman's shocking firing in November 2023. She left OpenAI in September 2024 to found Thinking Machines Lab .

**Q3: How many people have left Thinking Machines Lab?**

**A:** According to a Business Insider review, nearly a third of the 42-person founding team—13 people, including three of six co-founders—have left since the company launched .

**Q4: Where are they going?**

**A:** Meta has poached seven founding members, OpenAI has taken five, and Elon Musk's xAI has taken one . Notable departures include co-founder Andrew Tulloch (Meta), co-founder Barret Zoph (OpenAI), co-founder Luke Metz (OpenAI), and top executive Jolene Parish (OpenAI) .

**Q5: Why are people leaving?**

**A:** Two main reasons: (1) The "one-year cliff" has passed, allowing employees to unlock their first equity tranche and leave without losing everything. (2) Competitors like Meta and OpenAI are offering nine-figure compensation packages—hundreds of millions of dollars over several years .

**Q6: What happened with Barret Zoph?**

**A:** Zoph, the CTO and co-founder, was fired by Murati in January 2026 after a power struggle. The conflict began when Murati discovered Zoph had a secret relationship with a junior employee. Zoph was demoted, then attempted a "coup" by demanding more power. After being fired, he joined OpenAI within 58 minutes, along with two other co-founders .

**Q7: Is Thinking Machines Lab in trouble?**

**A:** The talent losses are significant, but the company has also grown to over 150 employees and made key hires, including Soumith Chintala (creator of PyTorch) as CTO. In March 2026, the company announced a major partnership with Nvidia for compute capacity .

**Q8: What products does Thinking Machines Lab have?**

**A:** The company launched its first product, Tinker—an API for fine-tuning language models—in October 2025. On May 11, 2026, it announced a new type of AI model that handles interruptions and translates languages in real time .

**Q9: What is a "one-year cliff"?**

**A:** In startup compensation, equity typically vests over four years with a one-year cliff. That means you get no equity if you leave before 12 months, but at month 12 you vest 25% of your grant. The cliff is designed to retain talent, but it has become a "natural moment" for employees to entertain outside offers .

**Q10: Could Thinking Machines Lab be acquired?**

**A:** Possibly. Meta reportedly approached Murati about acquiring the company in mid-2025, but she refused. With the talent exodus and valuation concerns, acquisition is one possible outcome, though Murati has publicly stated her desire to remain independent .

**Disclaimer:** This article is for informational and educational purposes only. The information presented is based on publicly available sources as of May 13, 2026. Talent movements, compensation figures, and corporate strategies are subject to rapid change. Please consult with appropriate professionals for advice specific to your situation.

Alibaba Jumps as It Strikes Bullish Tone on AI Investments, Even as Profit Plunges: The $380 Billion Gamble That Has Wall Street Confused

 

 Alibaba Jumps as It Strikes Bullish Tone on AI Investments, Even as Profit Plunges: The $380 Billion Gamble That Has Wall Street Confused

**Subheading:** *Alibaba's cloud revenue surged 38%, but adjusted earnings collapsed 99.7% in a single quarter. Yet the stock jumped nearly 7%. Welcome to the new math of AI investing—where losses are the new profits.*

**Estimated Read Time:** 15 minutes

**Target Keywords:** *Alibaba earnings 2026, BABA stock news, Alibaba AI investments, Qwen AI model, Alibaba cloud growth, Chinese AI stocks, Alibaba profit plunge, BABA price target 2026, Alibaba vs JD.com, AI infrastructure spending 2026.*

## Part 1: The Human Touch – The Earnings Call That Defied Gravity

Let me tell you about a Wednesday morning that broke every rule of investing.

It is May 13, 2026. Alibaba just released its quarterly earnings report. The numbers arrive like a punch to the gut.

Revenue is slightly light: $35.3 billion versus expectations of $35.8 billion. A miss.

Adjusted earnings per share? **$0.09**. The consensus was $0.83 . That is a **95% collapse** from the prior year.

Adjusted net income fell **99.7%** . Adjusted EBITA crashed **84%** . The company swung to an operating loss for the first time since the pandemic .

By every traditional measure, this is a disaster. A textbook "sell the stock" moment.

But here is the twist that left professional traders scratching their heads: **Alibaba stock jumped nearly 7%** .

The market did the exact opposite of what the textbook predicted.

What happened? Did investors misread the numbers? Did algorithms glitch? Was it just a short squeeze?

None of the above.

The market jumped because investors stopped looking at what Alibaba *is making* and started looking at what Alibaba *is becoming*.

On the earnings call, CEO Eddie Wu said something that changed the entire narrative: *"We aim to maintain growth that is faster than the market average in order to gain larger market share and firmly cement our absolute market leadership position... those are the primary objectives, and margin is still secondary"* .

Let me translate that from CEO-speak into English: **"We are willing to lose money now to win the AI war later."**

And the market believed him.

This is the new reality of investing in the AI era. Profits are yesterday's story. Growth is tomorrow's. And Alibaba just convinced Wall Street that it has the growth story of the decade.

But is that story real? Or is Alibaba burning billions on a gamble that might never pay off?

Let me walk you through what actually happened, why the stock rallied, and whether you should be buying, selling, or just watching from the sidelines.

## Part 2: The Professional – The Numbers Behind the Headlines

Let us put on our analyst hats. No hype. Just the facts.

### The "Ugly" Numbers: Why Traditional Investors Panicked

Here is the complete scorecard from Alibaba's fiscal Q4 2026 earnings:

| Metric | Actual | Expected | Year-over-Year Change |

|--------|--------|----------|----------------------|

| **Revenue** | $35.28 billion | $35.8 billion | +3% (miss) |

| **Adjusted EPS** | $0.09 | $0.83 | -95% |

| **Adjusted EBITA** | $740 million | — | -84% |

| **Operating Income/Loss** | -$122 million loss | — | vs. +$4.1B profit LY |

| **Free Cash Flow** | -$2.51 billion | — | Negative |

| **Adjusted Net Income** | $12 million | — | -99.7% |

At first glance, these numbers are alarming . Revenue growth slowed to just 3%. Earnings evaporated. Free cash flow turned negative for the first time in years.

The company's profitability collapsed because of two factors:

**1. AI Infrastructure Spending.** Alibaba is pouring billions into data centers, GPU chips, and AI research. The company confirmed it will exceed its previously announced three-year RMB 380 billion ($55.96 billion) AI and cloud investment plan .

**2. The Quick-Commerce War.** Alibaba is locked in a brutal battle with JD.com and Meituan for dominance in 60-minute delivery. This segment grew revenue 57%, but at a steep cost to margins .

### The "Beautiful" Numbers: Why Growth Investors Cheered

Now look at what made the bulls jump out of their chairs:

| Metric | Actual | Growth | Significance |

|--------|--------|--------|--------------|

| **Cloud Revenue** | $6.04 billion | +38% | 11th straight quarter of acceleration |

| **External Cloud Revenue** | — | +40% | Faster than total cloud growth |

| **AI Product Revenue** | $1.30 billion | +100%+ | 11 consecutive quarters of triple-digit growth |

| **Quick-Commerce Revenue** | $2.90 billion | +57% | Unit economics improving |

| **88VIP Members** | 62 million | Double-digit | High-value loyalty base |

| **LIke-for-Like CMR Growth** | — | +8% | Core China e-commerce healthy |

The cloud number is the headline: **$6.04 billion, up 38%** . That is not just growth—it is accelerating growth. The previous quarter was strong, but this is stronger.

Even more impressive: **External cloud revenue grew 40%** . That means the acceleration is coming from real customers, not internal Alibaba projects .

And the crown jewel: AI-related products now account for **30% of cloud external revenue** , reaching $1.30 billion in quarterly revenue. This segment has grown at triple-digit rates for 11 consecutive quarters .

Let me put that number in perspective. Alibaba's AI business alone is now larger than many public software companies. And it is growing faster than almost anything in tech.

### The "Future" Numbers: Why the Stock Jumped 7%

The stock jumped because of what Alibaba said about *tomorrow*, not what it reported about *yesterday*.

On the earnings call, CEO Eddie Wu projected that AI-related product revenue will exceed **50% of cloud revenue within one year** . That is not incremental growth. That is a transformation.

He also disclosed that the annualized recurring revenue (ARR) from AI models and application services—essentially the subscription revenue from Alibaba's AI platform—is expected to **surpass RMB 10 billion ($1.47 billion) in the June quarter** and **exceed RMB 30 billion ($4.4 billion) by year-end** .

In other words, Alibaba is building a $4 billion+ AI subscription business from scratch in less than 12 months.

Wu also signaled that the company's massive capital spending—RMB 380 billion over three years—will likely be **exceeded**. Future data center capacity will grow **more than tenfold** compared to 2022 levels .

CFO Toby Xu added that the company's **net cash position exceeds $59 billion** (excluding long-term debt), meaning Alibaba has the firepower to fund this spending without going bankrupt .

## Part 3: The Creative – The "Plowback" Narrative and the AI Tipping Point

Let me give you the creative framing that makes this story resonate.

### The "Plowback" Strategy: Buffett Meets Bezos

There is a concept in finance called the "plowback ratio"—the percentage of earnings a company reinvests into growth rather than paying out as dividends.

Most companies plow back 20-30%. Startups plow back 100%. Alibaba just plowed back **more than 100%** —it lost money on an operating basis to fund AI and quick-commerce expansion.

This is the strategy that made Amazon what it is. For years, Amazon reported tiny profits while reinvesting everything into warehouses, logistics, and AWS. Wall Street complained. Then AWS became a $100 billion business, and everyone called Jeff Bezos a genius.

Alibaba is running the same playbook. The difference is that Alibaba is doing it at a scale and speed that makes Amazon's early years look conservative.

The creative hook: **Alibaba is not reporting earnings. It is reporting reinvestment.**

### The "AI Tipping Point" that Wu Described

On the earnings call, Wu described a fundamental shift in the AI market :

> *"We are at an inflection point in the evolution from conversational chatbots to autonomous AI agents, which is directly driving explosive growth across our three core workload categories: training, inference, and agent orchestration."*

This is not marketing fluff. It is a technical observation with massive financial implications.

First came the "chatbot era"—2023 to 2025. AI could talk, but it could not do.

Then came the "agent era"—starting in late 2025. AI can now take actions: book flights, write code, manage workflows, shop on your behalf.

Agents consume far more computing power than chatbots. A single agent query might require dozens of model calls, database lookups, and logical steps. That means more tokens. More tokens mean more revenue for the companies providing the infrastructure.

Wu quantified this: *"As long as the value created within an enterprise by the tasks completed exceeds the token cost, the demand for API tokens will be virtually limitless"* .

This is the bet Alibaba is making: that the shift from chatbots to agents will create insatiable demand for AI computing, and that Alibaba—through its cloud platform, Qwen models, and self-developed chips—will be the primary beneficiary in China.

### The "Two Fronts" Strategy: AI and Quick-Commerce

Alibaba is fighting a two-front war.

**Front One: AI Leadership.** Against Tencent, Baidu, and a dozen well-funded Chinese AI startups. Alibaba's weapon: full-stack integration from chips to models to applications .

**Front Two: Quick-Commerce Leadership.** Against JD.com and Meituan in the race to deliver anything within 60 minutes. Alibaba's weapon: the "Taobao Flash Purchase" platform, which grew order volume 170% year-over-year .

The quick-commerce war is expensive. Adjusted EBITA for China e-commerce fell 40% as Alibaba poured money into subsidies and logistics .

But here is the creative insight: **These two wars are connected.**

AI agents need real-world execution capabilities to be useful. An AI that can shop for groceries is only valuable if groceries can actually be delivered. By building quick-commerce infrastructure, Alibaba is creating the "real-world API" for its AI agents.

Wu hinted at this integration: the Qwen app now fully integrates Taobao and Tmall's commercial service capabilities, allowing users to complete purchases directly through the AI assistant .

This is the "closed loop" that Amazon has always dreamed of: an AI that shops, a warehouse that picks, and a delivery network that drops at your door. Alibaba is building the Chinese version, and it is spending billions to get there first.

## Part 4: Viral Spread – The "BABA Paradox" and the TikTok Takeover

A story where profits collapse but the stock soars is perfect for social media.

### The Meme Angle

**Meme #1: "The Earnings Report Nobody Understands"**

A split image: Left side shows a trader panicking at red numbers labeled "EPS -95%." Right side shows the same trader celebrating at green numbers labeled "Stock +7%." Caption: *"Alibaba earnings explained."*

**Meme #2: "Wu's 'Margin Is Secondary' Quote"**

An image of Eddie Wu with a speech bubble: *"We will lose money. A lot of money. And you will like it."* The stock chart below shows a green arrow pointing up.

**Meme #3: "The $380 Billion Question"**

A cartoon of a giant pile of cash labeled "AI Capex" with a tiny figure holding a sign: *"Where profit?"* The figure is surrounded by clouds labeled "38% growth."

### The Viral Headlines

Expect these headlines across social media:

- *"Alibaba's profit collapsed 95% and the stock went UP 7%. Welcome to AI investing."*

- *"Eddie Wu just told investors: 'Margin is secondary.' The market said 'OK, here is $40 billion.'"*

- *"Alibaba is spending $55 billion on AI. Its cloud revenue just grew 38%. Is this Amazon 2015 or Pets.com 2000?"*

### The TikTok Angle

For the TikTok generation, the story needs simple framing:

- **"The 'Amazon' playbook":** *"Amazon lost money for years building AWS. Now AWS is a $100 billion business. Alibaba is doing the same thing with AI. Here is why the stock jumped."*

- **"Your AI assistant needs a delivery truck":** *"Alibaba is building both. That is why they are losing money now—and why investors are betting big on the future."*

- **"The 'Qwen' app explained":** *"Alibaba just launched an AI that can shop for you. It's like ChatGPT with a credit card. This is bigger than you think."*

### The LinkedIn Angle

For professionals, the hook is strategic:

**"Alibaba's Q4 earnings present a paradox: collapsing profits but surging stock price. The explanation: investors are now valuing the company on cloud execution and AI potential, not trailing e-commerce earnings. With AI-related cloud revenue growing triple-digits for 11 straight quarters and ARR projected to hit $4.4 billion by year-end, the market has decided that Wu's 'margin is secondary' strategy is the right bet. Whether that bet pays off depends on whether AI demand materializes as expected—and whether Alibaba's chip supply chain holds up under US sanctions."**

## Part 5: Pattern Recognition – What This Means for American Investors

Let me step back and show you the broader patterns.

### Pattern One: The "AI Infrastructure" Trade Is Global

Alibaba's experience mirrors what American companies are seeing. Microsoft's Azure AI revenue is surging. Amazon's AWS AI services are growing rapidly. Google Cloud is seeing AI-driven acceleration.

The pattern is consistent across borders: **AI infrastructure spending is accelerating faster than expected, and cloud providers are the primary beneficiaries.**

The difference is that Alibaba is doing this while also fighting a quick-commerce war and navigating US chip sanctions. The degree of difficulty is higher, which makes the execution more impressive—or more reckless, depending on your view.

### Pattern Two: The "Profitless Prosperity" Era

We are entering an era where traditional valuation metrics are breaking.

| Traditional Metric | Current Reality |

|--------------------|-----------------|

| P/E ratio | Irrelevant if earnings are near zero |

| Free cash flow | Negative due to capex spending |

| Operating margin | Compressed by strategic investments |

Investors are increasingly valuing AI companies on **revenue growth, customer adoption, and market share**—not current profitability.

This is fine when the growth materializes. It is disastrous when it does not.

### Pattern Three: The "Chip Constraint" Wildcard

Alibaba's AI ambitions depend on access to advanced chips. The company has developed its own GPU chips through its Pingtouge subsidiary, with over 60% of computing power now serving external customers .

But Wu acknowledged that production capacity remains limited: *"We are still mainly constrained by production capacity"* .

US sanctions have restricted Alibaba's access to Nvidia's most advanced chips. The company's ability to scale its AI infrastructure depends on whether its domestic chip supply chain can keep pace with demand.

This is a risk that American AI investors do not face in the same way. Microsoft and Amazon can buy all the Nvidia chips they want. Alibaba cannot.

### The Three Scenarios for Alibaba

| Scenario | Probability | Description |

|----------|-------------|-------------|

| **The "Amazon" Scenario** | 40% | AI demand materializes as expected. Alibaba's cloud and AI revenue accelerate past 50% of total. Margins recover. Stock re-rates higher. |

| **The "Bubble" Scenario** | 35% | AI demand growth slows. Competition intensifies. Quick-commerce losses persist. The $380 billion capex plan becomes a drag on returns. Stock stagnates. |

| **The "Chip" Scenario** | 25% | US sanctions tighten further. Domestic chip production cannot scale fast enough. Alibaba's AI ambitions are constrained by hardware availability. Growth stalls. |

The stock jumped because investors are betting on Scenario One. The profit collapse is the price of that bet.

## CONCLUSION: Should You Buy, Sell, or Watch?

Let me give you the bottom line.

Alibaba just reported one of the strangest earnings in recent memory: a 95% earnings collapse and a 7% stock rally. The paradox exists because the market has decided that Alibaba's future as an AI company is more important than its present as an e-commerce company.

**Here is what I believe:**

The AI opportunity is real. Alibaba's cloud growth—38% accelerating to 40%—is not imaginary. The fact that AI products now account for 30% of cloud revenue and are growing triple-digits suggests that the company is capturing real demand.

But the costs are also real. Free cash flow turned negative. Operating income turned negative. The quick-commerce war is bleeding money. And the $380 billion capex plan will keep margins compressed for years.

**What you should do right now:**

| If you are... | Your move |

|---------------|-----------|

| **A long-term investor** | Consider Alibaba as an AI play, not an e-commerce play. The valuation is reasonable if cloud and AI grow as projected. But be prepared for volatility and continued margin pressure. |

| **A trader** | The post-earnings jump may be exhausted. Watch for pullbacks to the $130-$135 range as potential entry points. |

| **A growth investor** | Watch the AI ARR numbers closely. If Wu's projection of RMB 30 billion ($4.4B) by year-end materializes, that will be a powerful catalyst. If it misses, the stock will re-rate lower. |

| **A value investor** | This is not a value stock anymore. Traditional metrics (P/E, FCF) are broken. Look elsewhere if you need current earnings. |

**The final word:**

Alibaba is making a $380 billion bet that the AI agent era will create insatiable demand for computing power. CEO Eddie Wu is willing to sacrifice margins—and current profits—to win that bet.

The market just rewarded him for that conviction.

Whether history will remember this as the moment Alibaba transformed into an AI giant—or the moment it burned billions chasing a mirage—will be decided by the demand for AI tokens, the availability of chips, and the speed of the agent revolution.

Buckle up. The bet is placed. The chips are falling.

And for the first time in years, the market is actually cheering.

## FREQUENTLY ASKING QUESTIONS (FAQ)

**Q1: Why did Alibaba stock go up if profits collapsed?**

**A:** Investors focused on the company's accelerating cloud and AI growth rather than the earnings miss. Cloud revenue surged 38% to $6.04 billion, AI product revenue grew triple-digits for the 11th consecutive quarter, and management projected AI ARR would exceed $4.4 billion by year-end. The market is betting that AI will drive future profits, even at the expense of current earnings .

**Q2: How much is Alibaba spending on AI?**

**A:** Alibaba announced a three-year RMB 380 billion ($55.96 billion) AI and cloud investment plan and has signaled it will likely exceed that amount. CEO Eddie Wu said future data center capacity will grow more than tenfold compared to 2022 levels .

**Q3: What is the "Qwen" AI model?**

**A:** Qwen is Alibaba's family of large language models, comparable to GPT-4 or Claude. It is integrated into Alibaba's e-commerce platforms, allowing users to shop directly through the AI assistant. The Qwen app now fully integrates Taobao and Tmall's commercial capabilities .

**Q4: What is "quick commerce" and why is Alibaba investing in it?**

**A:** Quick commerce refers to delivery within 60 minutes. Alibaba's "Taobao Flash Purchase" platform grew order volume 170% year-over-year to $2.9 billion in revenue. The company is willing to lose money on this segment now because it believes AI agents will need real-world delivery capabilities, and owning the delivery network creates a "closed loop" from AI query to physical delivery .

**Q5: Is Alibaba profitable at all?**

**A:** On an operating basis, Alibaba posted a small loss of $122 million for the quarter—its first operating loss since the pandemic. However, net income rose 96% due to gains from equity investments. Adjusted EBITA fell 84% to $740 million. The core business is being squeezed by AI and quick-commerce investments .

**Q6: How does US chip policy affect Alibaba?**

**A:** US sanctions restrict Alibaba's access to Nvidia's most advanced AI chips. The company has responded by developing its own GPU chips through its Pingtouge subsidiary, with over 60% of computing power now serving external customers. However, CEO Wu acknowledged production capacity remains a constraint .

**Q7: What is Alibaba's AI ARR target?**

**A:** CEO Eddie Wu projected that annualized recurring revenue (ARR) from AI models and application services will exceed RMB 10 billion ($1.47 billion) in the June 2026 quarter and surpass RMB 30 billion ($4.4 billion) by year-end. He also expects AI products to exceed 50% of cloud revenue within one year, up from 30% currently .

**Q8: Is Alibaba a buy right now?**

**A:** This article does not provide investment advice. However, investors should understand that Alibaba is now an AI and cloud play, not a traditional e-commerce value stock. The valuation is based on future growth, not current earnings. Key risks include US chip sanctions, competition from JD.com and Tencent, and the uncertain pace of AI agent adoption .

**Q9: How does Alibaba's AI strategy compare to American tech companies?**

**A:** Alibaba's strategy is similar to Amazon's approach with AWS: invest heavily in infrastructure, accept low margins in the short term, and aim for market leadership. The key difference is that Alibaba faces chip sanctions that American companies do not, forcing it to develop domestic alternatives—a constraint that could become an advantage if US-China tensions worsen .

**Q10: What is the "Alibaba Token Hub"?**

**A:** The Alibaba Token Hub (ATH) is a newly created business unit that centralizes Alibaba's AI operations, separating them from the cloud division. It is led by CEO Eddie Wu and focuses on commercializing AI models and application services. The unit is expected to generate significant subscription revenue from enterprises using Alibaba's AI infrastructure .

**Disclaimer:** This article is for informational and educational purposes only and does not constitute financial, legal, or investment advice. Alibaba's business, AI demand, and geopolitical conditions are subject to rapid change. Past performance does not guarantee future results. Please consult with a qualified financial advisor before making any investment decisions based on this content.