Techin' Geek

At TechBBQ in Copenhagen, discussions among investors, founders, and operators from various parts of Europe revolved not merely around building with AI but rather around who ought to wield control over it. Regardless of your location during the annual Nordic conference—whether onstage, during cocktail hours, or at after-parties—the dialogue continually returned to how Europe could assert greater control over the technology driving AI.

This inquiry into sovereignty—fittingly aligned with this year’s theme of “Emerging from Agency”—was particularly pertinent following the unavailability of Anthropic’s AI models Mythos and Fable to users outside Europe earlier this year.

The situation prompted many within the European ecosystem to seriously contemplate the implications of owning the models and the infrastructure supporting this AI surge, rather than depending on the U.S. and China for that power.

One startup executive remarked to me that the Mythos and Fable incident significantly disrupted his software team, while another downplayed its impact. Indeed, the startup executive acknowledged that relying on two other geopolitical powers for AI could one day lead to considerable challenges for Europe, but at present, things remain largely satisfactory. 

“TechBBQ’s theme of agency felt especially relevant this year as the dialogue shifted from what AI is capable of to what we are genuinely prepared to allow it to do,” Ellen de Brever, an angel investor and head of partnerships at the Novo Nordisk Foundation Cellerator, shared with me during the conference that concluded on August 27.

Panels addressed the future of women’s health, a summit on collaboration between Nordic and African ecosystems for innovation and investment, and discussions on increasing the flow of growth capital into Europe. Although AI served as a common thread in numerous conversations, as de Brever noted, “the debates centered not on smarter machines, but on human judgment and who retains control.” 

“The era of AI agents isn’t solely about machines gaining agency,” she elaborated. “It’s about humans determining what to relinquish.” 

An attendee mentioned that one of the most invigorating sessions at the event was the panel featuring Signal President Meredith Whittaker, which I moderated, centered on whether privacy and AI can coexist. Whittaker was forthright and expressed strong opposition to the integration of AI assistants and agents into operating systems, akin to ChatGPT’s implementation with iMessage.

She stated that this AI epoch is establishing a “data collection apparatus” and that AI labs are utilizing savvy marketing tactics to obscure “the collateral consequences” of amassing so much data. 

“There remains a substantial market demand for privacy,” she remarked. “And especially in light of sovereignty concerns, we will find customers here.” 

In another session, Emad Mostaque, co-founder of Stability AI and Intelligent Internet, joined me on a panel discussing how an agentic workforce will influence the economy and its implications for personal sovereignty.

“Sovereignty is the capacity to resist power exerted over you,” Mostaque stated. He discussed the accumulation of power within a few AI labs, asserting that “eventually, every nation will be governed by AI and that ‘the person controlling the AI governs the nation.’” 

Mia Negru, the advocacy and engagement director for the nonprofit Life With Artificials, reached out to me post-panel to share her reflections.

“If intelligent agents take on cognitive tasks and robots increasingly handle physical work, we might be on the cusp of something far greater than another technological revolution,” she conveyed in her message. “We could need to reconsider ownership, labor, democracy, economic engagement, and ultimately who gets to be part of society.” 

However, when the panels wrapped up, attendees relaxed at happy hours and rooftop soirées, where lighter conversations eventually dominated.

Lovable, Nvidia, OpenAI, AWS Startups, and HSBC collaborated to sponsor a rooftop barbecue where I conversed with founders regarding the emerging tech hubs in Denmark.

London’s Ada Ventures hosted a gathering at the cocktail bar Bird, where I spoke with a health tech founder about the surge of innovation in the women’s brain health sector. Subsequently, there was the speakers’ dinner on an island in the heart of Copenhagen where the international press gathered to enjoy fire dancers. The after-party carried on upstairs, with guests tempted to linger for one last drink until the bar tab was closed.

“In a setting filled with discussions about machines, the most unforgettable experiences still arise from the most straightforward interactions,” de Brever reflected. “Human connection, face-to-face, forging relationships, exchanging ideas, and reminding each other of what technology can never truly replace.” 

Vijay Pande was once more recognized in academic communities than in investment ones. This changed suddenly about twelve years ago when Marc Andreessen and Ben Horowitz — who had dedicated the first five years of their firm to steering clear of healthcare and life sciences — opted to invest in the sector, entrusting Pande with the initiative. At that time, he was a chemistry professor at Stanford, best known for creating Folding@home, the distributed computing project that enabled millions of personal computers to function as a supercomputer for disease research. Over the following decade, he expanded a16z’s investment into a venture managing nearly $4 billion.

Thus, it was somewhat surprising when Pande decided to leave it all behind in June of last year to embark on a much smaller venture. In fact, his new firm, VZVC, co-founded with long-time investor Zach Werner, centers around a limited number of concentrated bets each year rather than numerous ones, has no staff, and significantly relies on AI for its daily operations.

To dive deeper into Pande’s significant shift, we engaged him this week on why he is focusing on a few concentrated bets instead of spreading himself too thin in today’s market — as well as addressing one of the more intriguing dilemmas in AI-driven biotechnology: unlike textual data, biological information cannot be extracted from the internet, causing almost every company to develop its own isolated dataset. What implications does this hold for the advancements that AI in medicine has promised, and who actually gains access to these innovations?

This discussion has been edited for brevity and clarity. You can also listen to the full conversation (below).

You mentioned that biology is transitioning from a “science of discovery” to something that can be engineered. What does that entail?

Historically, drug development has involved a fair amount of luck. I believe the change now is that AI and machine learning enable computers to process and understand complex concepts… to determine which targets your drugs should aim for regarding specific diseases, to create those drugs, and now even to assist in clinical trials — the most costly phase of the process.

I thought clinical trials were becoming less expensive due to drug developers utilizing more synthetic data, thus requiring fewer participants.

That’s certainly a goal.

The expenses and duration for reaching clinical trials have been decreasing, especially with AI, yet executing a trial can still cost hundreds of millions of dollars, explaining why medications are so pricey. The odds of a drug progressing successfully from the initial trial to the conclusion of the third trial are merely 20%. If 8 out of 10 fail, and these processes cost hundreds of millions, the overall financial burden becomes extremely high. Typically, the cause of failure isn’t because of error from the biologist, but rather that all the tests these drugs were based on utilized animal models like mice, which are ultimately poor predictors for human outcomes. The AI model won’t be flawless, but it’ll surpass any animal model, and once it achieves that, it becomes really thrilling.

[The following phase is]: Is this drug appropriate for me?

You refer to personalized medicine. . .

The term used here is precision medicine. When individuals consult a doctor for something significant, the physician often has to make educated guesses about the situation since their capabilities are limited. Subsequently, they prescribe a medication — if that fails, another is provided, followed by yet another. This pattern occurs in cancer and various areas. We would all benefit significantly if the initial drug was the correct one. Typically, your blood test outcomes are compared to population norms. However, they should truly be compared to: is this [result] unusual for you? What we are also beginning to achieve on the medical front is [the capacity] to comprehend what would be appropriate for the individual.

Would you say the progression to this point has been gradual and consistent, or has there been a recent surge?

I believe it’s a collection of various factors [coming together]. For example, precision medicine has long relied on genomics. However, the truth is that your genome is somewhat like the original blueprint of your house; yet your home has altered considerably since its construction. There are numerous other elements now measurable in proteomics and more that are much more pertinent for comprehending diseases and your current bodily state. Moreover, there has been substantial automation in robotic measurements that integrates well with AI, complementing each other effectively.

Over the last decade, there’s been a steady progression in both AI for biology and AI for chemistry. The biology aspect focuses on how we can treat a disease, while the chemistry facet deals with how we can develop a drug targeting that specific protein. Notably, there have been substantial advancements in those ten years.

You indicated that biology is one of the few domains from which AI cannot simply extract data from the internet. How does this impact the evolution of the field?

This domain lacks the data that allows everyone to train comparable models, and your data cannot easily transfer between models. It’s a fascinating scenario from a purely AI perspective.

Doesn’t that reflect a familiar challenge in medicine — healthcare professionals working within [territorial, frequently competitive] silos?

You’re touching upon a significant issue here. Consider a patient diagnosed with a type of cancer that involves both oncology and endocrinology; these two specialists often don’t coordinate well. The intriguing aspect of AI is that it can, in theory, function as a specialist across disciplines, potentially observing patterns that any individual physician couldn’t perceive. It’s akin to assembling a team of top-notch doctors collaborating instantaneously.

Is there sufficient data sharing for that vision to become a reality? I understand the motivation for founders and investors to safeguard their [individual findings], but . . .

I believe a significant trend is emerging where we’re witnessing a shift towards compiling atlases of biological information — which, from a technological perspective, typically involve foundation models. As these become more prevalent, I anticipate we will observe a trend similar to the one observed with open-source LLMs, which outperform corporate counterparts: open-source foundation models in biology having a wide-reaching effect.

You’re affiliated with Genesis Therapeutics, which originated from your lab at Stanford, and Insitro, the drug discovery firm launched by Daphne Koller, a former Stanford colleague. You also mention incubating a company with a founder you have known for 20 years. What attributes do you seek in founders, and in what sectors?

I’ve primarily focused on two areas. One is AI for healthcare delivery, where I have considerable experience from a16z, and the other is AI for clinical trials.

One of the most crucial aspects for me [regarding founders] is establishing mutual trust — founders who demonstrate high integrity and follow through on their commitments… I envision this collaboration lasting well into the future, ideally, extending over the next 5 to 10 years and beyond, as they develop their next company. I want to work with individuals who prioritize long-term thinking, not just competitive success, but genuinely pondering the question: how can we succeed together?

What do you think you’ve achieved correctly and incorrectly in your investment journey so far?

When I first began discussing AI, machine learning, technology, and bio-medicine over a decade ago, I faced considerable resistance, with many asserting, ‘Oh, that will never materialize. It won’t be useful,’ and so forth. That skepticism has largely dissipated, and witnessing this progression has been immensely rewarding.

I suppose it took me time to fully grasp that while the allure of the most advanced technologies is undeniable, the core always comes back to go-to-market strategy. I remind my founders, particularly those from scientific or product backgrounds, to channel their brilliance and creativity towards the go-to-market strategy since that aspect is at least as challenging, if not more so, than the technical side.

Can you elucidate how you’re structuring this new firm differently compared to your previous experience at a16z?

Currently, we are operating in a distinctly different manner… VZ is named after myself, Vijay, and my co-founder, Zach Werner — the “Z.” We intentionally maintain a smaller size… from an investment standpoint, it’s just the two of us. Initially, we planned to hire associates, but we discovered that with our existing networks, that wasn’t necessary.

What does “concentrated” really mean?

We’re [not] aiming for 30 investments per year… we’re looking at approximately five, not many — very concentrated. Adding a company in a typical fund is akin to adding a Facebook friend — it’s a quick process. For Zach and me, it’s more like… wanting to have another child. It’s a significant decision for us.

Given this structure, who are you up against for deals?

Interestingly, with this model, we aren’t typically competing for top rounds — people often create opportunities for us. It’s a markedly different approach compared to pursuing desirable Series A or B investments. Generally, others are eager to bring us on as investors due to the unique contributions Zach and I can make and our hands-on involvement. When I consider figures who inspire me, I look at someone like Antonio Gracias at Valor — he’s become well-known through the SpaceX deal, but he has been committed to his work for 20 years. What Thrive has accomplished with a more concentrated portfolio also serves as a real source of inspiration. Obviously, a16z remains part of my essence, but I see those other entities as newer influences on our perspective.

What trends do you believe are currently overhyped in AI and biotech?

The truth is that AI can uncover insights concealed from human comprehension. The nuance becomes complicated when claims arise that AI will solve everything. The hesitation stems not from mistrust in AI — it’s about skepticism regarding data quality. LLMs thrive due to the abundance of data available for training. When the necessary data simply isn’t present, AI cannot magically provide a solution to that issue.

Hollywood’s elite — individuals used to hefty eight-figure paychecks and lavish film locations — are now placing their bets on something much more compact: microdramas.

Microdramas are brief, scripted series meant for viewing on mobile devices. Episodes usually last just a few minutes and often incorporate soap opera-like narratives, intense plot twists, and cliffhangers crafted to keep audiences engaged and frequently paying for subsequent episodes.

This format gained popularity in China before rapidly expanding worldwide. Omdia anticipates that revenue generated by microdramas in the U.S. will hit $1.5 billion this year. An increasing number of applications have surfaced to take advantage of this trend, including ReelShort and DramaBox, with TikTok also joining the arena by launching its PineDrama app earlier this year.

Hollywood talent is now beginning to step in, either by featuring in fresh series, starting their own ventures, or funding platforms.

This shift is understandable. Film studios seek more economical and quicker methods to generate content, and numerous talents and crew are finding themselves out of work. Concurrently, TikTok, Instagram Reels, and YouTube Shorts are capturing a larger share of viewers’ focus, making microdramas an increasingly appealing sector within the entertainment field.

Here are some renowned personalities who are taking advantage of the microdrama phenomenon.

James Franco

The most recent notable Hollywood figure to enter this domain is James Franco, who stars in the series “Love, Lies & Frank,” available for streaming exclusively on Shortical.

Franco portrays the chief executive of an advertising firm who becomes embroiled in crime and werewolf-related chaos. The concept appears to follow a familiar structure typical of microdramas, filled with excessively dramatic sequences and a secret romance.

This project stands out as one of the pioneers produced under the new SAG-AFTRA verticals agreement, a union contract aimed specifically at low-budget microdramas. It offers an initial insight into how this format can open new avenues for actors and other talent in Hollywood.

Additionally, Franco’s participation has drawn interest for another reason: this venture signifies his attempt at a comeback after facing sexual misconduct allegations and a legal resolution in 2021. A review from Variety of the show was not particularly favorable, stating that the actor “seems to be sleepwalking through his lines.”

What motivated Franco to take on a microdrama role remains uncertain, yet his casting is still noteworthy. It is uncommon to see a prominent actor, who has earned millions and starred in major box office hits, take the lead in a small series intended for mobile viewing.

Issa Rae

Issa Rae stands out as a natural talent to welcome the microdrama surge. She was the creator and star of the 2011 web series “The Misadventures of Awkward Black Girl,” which propelled her career forward.

In May, Issa Rae’s company Hoorae Media collaborated with TikTok’s new PineDrama app to unveil “Screen Time,” a suspenseful tale centered on a double date that takes a sinister turn when an unidentified individual takes control of the television and forces the couples to face their secrets.

The series quickly emerged as a sensation, racking up nearly 75 million views within its first week and becoming the leading vertical series on TikTok and PineDrama at that moment. It eventually surpassed 150 million views.

Jesse Tyler Ferguson

“Modern Family” actor Jesse Tyler Ferguson is also entering the field with “Theater Kids Vs. Zombies,” an upcoming musical comedy for the vertical storytelling platform aTwist.

The concept aligns closely with what the title suggests: a group of theater students discovers their high school overrun by zombies and realizes that their best chance of survival involves staging a musical. 

Ferguson’s venture could be significant, as this series seems to diverge from the typical romance and soap-opera conventions dominating microdramas, instead embracing comedy and theater culture.

Taye Diggs

Taye Diggs not only featured in a microdrama but is also becoming more heavily invested in the field.

In January, Diggs was announced as the star and executive producer of “Off Limits & All Mine” for the platform CandyJar. He portrays a widowed entrepreneur who gets entangled in a secret romance with his best friend’s grown daughter.

More significantly, Diggs has established his own mobile-first vertical storytelling platform, Microhouse Films, aimed at granting filmmakers better control over the production and monetization of their works. The platform’s inaugural launch included eight projects, one of which is “Tides of Temptation,” a series developed in partnership with Lifetime.

This step is important because instead of merely joining the microdrama trend, Diggs is now contributing to shaping the industry itself.

Kim Kardashian

Kim Kardashian’s participation showcases another avenue through which celebrities are engaging in the industry. Although she has yet to star in a microdrama, she has already invested in the sector by putting her money behind GammaTime, alongside her mother, Kris Jenner, and other angel investors.

As I approach the conclusion of my 20s later this year, I’ve officially hit the point where sleeping in an awkward position or stretching a bit too much can lead to discomfort. I’ve always had my doubts about massage guns, largely because I’ve tested a handful of off-brand models and figured they’d just end up gathering dust.

However, after several weeks of trying out the Therabody Theragun Sense (2nd Gen), I was taken aback. It turns out massage guns aren’t the fads I had assumed they were.

The Theragun Sense, priced at $299, is a massage gun focused on wellness that aims to alleviate everyday discomfort, relieve muscle tightness, encourage relaxation, and ease soreness. It serves more as a gentle massage and relaxation device than a robust recovery massage gun.

If you’re in search of a more intense deep-tissue massage, consider looking into the Theragun PRO or Prime. But if a heavily featured, powerful massage gun—including heated therapy and deep-tissue functionality—isn’t a necessity for you, the Theragun Sense is an excellent choice for regular use. 

What I appreciate most about the massage gun is its built-in visually guided routines. It offers four routines, and with the Therabody app, you can access even more, syncing your preferred ones to the device via Bluetooth.

The Theragun Sense features an LCD display that guides you through routines step by step. After selecting a routine, the screen instructs you on where to apply the massage gun, how much time to spend on each area, and the pressure to use.

As someone who had little idea of how to utilize a massage gun other than directing it at a tight muscle and hoping for relief, I found the Therabody Sense made the process much simpler.

The routine I enjoyed the most was the six-minute “Sleep” routine, which provides a quick full-body massage to help prepare you for bedtime. The app offers a variety of other routines, including “Pre-Walk Warm-up,” “Desk Relief,” “Lower Back Recovery,” and many more. 

The massage gun includes five speed options to adjust the pressure, allowing for a lighter touch, medium pressure, or a more intense release of tension. I appreciated the ability to alter the intensity based on how sore I felt that day.

As the device is made to be lightweight, I found it easy to handle and access those areas on my back and shoulders that are less accessible.

For those seeking more customization, the Therabody app elevates the guided experience with Coach, the company’s AI feature that formulates tailored recovery suggestions based on your objectives and activity. Coach can provide recommendations on which areas to focus on, what settings or attachments to utilize, and the optimal timing for your recovery sessions. 

I also valued the relatively quiet motor, allowing me to use the device while watching television or relaxing without being disturbed by the noise of the massage gun. 

Included with the Theragun Sense is a USB-C charging cable, a travel pouch, and two massage attachments: the Standard Ball and the Dampener, designed for targeting knots and sensitive areas. For an additional $35, you can purchase the “Supersoft” attachment for extra delicate areas of the body. 

In summary, the Theragun Sense (2nd Gen) won me over. It’s user-friendly, comfortable to hold, and notably eliminates the uncertainty involved with using a massage gun. While priced at $299, it’s not inexpensive, but I consider it a fantastic choice for those seeking a daily wellness device that aids in relaxation and recovery.

Prior to this week, the prevailing narrative about Nvidia was essentially this: Throughout the initial years of the AI surge, Nvidia stood as the sole provider of premium GPUs, which became extraordinarily lucrative as the sector expanded. Recently, hyperscalers like Amazon and Google have begun developing their own chips, resulting in Nvidia no longer being the sole option available, prompting investors to question the sustainability of its edge.

This is a captivating narrative, and largely accurate. After experiencing a tenfold increase in its market capitalization from early 2023 to mid-2025, Nvidia’s stock has followed a more tempered path over the last year, influenced by worries regarding GPU rivalry.

A fresh narrative has emerged following the company’s earnings report on Wednesday, and investors are starting to recognize that Nvidia’s strengths extend far beyond GPUs. As AI’s computational needs escalate to gigawatt levels, orchestration has transformed into a progressively intricate responsibility. Unsurprisingly, Nvidia has developed much of the cutting-edge hardware required to manage it, providing the company with a substantial advantage in the systems surrounding the GPU, despite facing intensified competition in GPU production itself. 

For all the discussions regarding computing as a commodity, managing a megascale data center at optimal efficiency remains extraordinarily challenging — and as deployments become larger and more rapid, this challenge only intensifies.

Rack by Rack

You can observe some of this simply by examining the specifics of what Nvidia is offering. The company is currently introducing its Vera Rubin architecture, which combines the Rubin GPU with various other units, such as the Vera CPU, the Groq 3 LPX inference accelerator, and similar racks for storage and networking.

Over the past week, I’ve engaged with representatives at Nvidia about the functions of these systems, and the findings have been quite revealing. Like the Rubin GPU itself, they are highly specialized systems, but rather than solely processing tokens, they ensure that everything surrounding the GPU operates as efficiently as possible. If the GPU serves as the engine, these components function as the rest of the vehicle.

Particularly, the Vera CPU is concentrated on the challenge of data orchestration. “Vera is significant because there’s a limit to the memory you can fit within a single server or any compute platform,” Jason Hardy, Nvidia’s VP of storage technology, explained to me. 

As data centers have escalated their computing capabilities, the memory capacity has likewise increased, which is why businesses like Micron have thrived in the recent infrastructure boom’s second wave. However, delivering that data to the GPU at the appropriate moment is not trivial — and as firms strive to lower tokens-per-watt, they are coming to realize the critical importance of directing that traffic.

“We observed improvements exceeding 3x in these operations, where the Vera CPU enables acceleration,” Hardy remarked. “Thus, we can now fully utilize our flash without creating bottlenecks.”

Similar versions of this issue can be identified beyond Nvidia. When OpenAI created its Jalapeño chip, a primary goal was to entirely circumvent these obstacles by minimizing data movement.

“We designed Jalapeño to reduce data movement and communication delays,” the company mentioned in a blog entry earlier this month. “Its expansive domain allows the entire workload to remain within a single connected system, thereby minimizing data movement and ensuring that the complete request remains rapid and efficient from start to finish.”

This presents an alternative approach, eliminating data movement by executing a workload within a single integrated chip. Yet the underlying principle is consistent, boosting efficiency through more intelligent traffic management instead of merely relying on increased processor cycles. This, in turn, paves the way for a new tier of infrastructure for companies to vie over.

This new emphasis on data orchestration does not guarantee a victory for Nvidia. The firm will need to contend with competing chipmakers and hyperscalers just as it has with GPUs. However, the competition has evolved to a new dimension, where creating a rival GPU is less pertinent than ensuring the efficiency of the entire system. 

And at least during the initial stages, Nvidia appears to have a significant advantage.

The excitement surrounding humanoid robots is not particularly recent. Credit Tesla’s CEO Elon Musk and his Optimus robot, as well as the numerous videos showcasing Boston Dynamics’ Atlas robot, for that.

However, beneath that excitement lies significant advancement. The physical skills of robots are steadily advancing, and researchers now believe that the AI methods utilized in large language models can enable sophisticated robots to learn virtually any task.

These favorable conditions have motivated a new wave of companies to seize the potential profits from humanoid robots. Many of the newest players are automakers from China.

Earlier this week, Xpeng’s robotics division secured over $900 million at a post-money valuation exceeding $6.3 billion. The funding round, spearheaded by IDG Capital with contributions from Gaorong Ventures, Tencent, and Alibaba, was characterized by the company as the largest single-round private financing ever documented in China’s “embodied AI” sector (AI systems integrated directly into physical devices).

This month, AiMOGA, the robotics division of China’s Chery Automobile, reportedly started preparations for an IPO, while BYD introduced a humanoid robot named Xiao Di. Other Chinese automotive manufacturers, including Changan, GAC, Li Auto, SAIC, and Seres, are also in the process of developing humanoid robots.

Among these, Xpeng stands as the Chinese automaker that closely monitors and adopts Tesla’s strategies, according to Michael Dunne, CEO of advisory firm Dunne Insights, based in San Diego and Singapore.

“It’s the most dedicated to autonomy, and it’s the first to significantly invest in humanoid robots,” Dunne stated to TechCrunch, adding that Xpeng’s founder He Xiaopeng is a tech billionaire noted for his adaptability and swift changes. “He perceives razor-thin profits in cars on the immediate horizon. Robots appear much more promising.”

Xiaopeng and Xpeng co-president Brian Gu are optimistic enough to have invested their own money into the robotics division. According to the Wall Street Journal, the duo contributed around $100 million to the recent funding round.

Xpeng is focusing on Iron, a humanoid robot that has a lifelike human form and is designed for commercial deployment.

Chinese automakers such as Xpeng do offer a manufacturing advantage.

“They possess all the hardware necessary to complete the task,” Dunne remarked. “The question is whether they can keep pace with Tesla in AI.”

Naturally, many other firms are also developing humanoid robots, including Agility Robotics, Apptronik, and Figure, all pursuing the same aim: large-scale commercial deployment.

Hyundai-owned Boston Dynamics is nearing that objective. Hyundai plans to integrate Boston Dynamics’ Atlas humanoid robot into its factory in Georgia this year, with the goal of employing the robots for tasks like parts sequencing by 2028. The Korean automaker, which has teamed up with Google’s AI research lab DeepMind to expedite the development of Atlas, is opening a U.S. facility this year called a Robot Metaplant Application Center, which will instruct robots on how to navigate movements like lifts and turns.

Other automotive companies are also joining the trend, including supplier Mobileye, which purchased humanoid robot startup Mentee Robotics earlier this year for $900 million. Even Rivian is exploring robotics with its Mind Robotics spinout — although its robots are not anticipated to resemble the humanoids under development elsewhere.