Techin' Geek

Meta founder and CEO Mark Zuckerberg is trying to sell investors on his prediction for the future — one where billions of people will have their own personal AI agents in the next five years. (Let’s hope that future also comes with data centers efficient enough to power all those agents — without triggering a fresh wave of climate disasters.)

“I think that it’s extremely unlikely if you look out five years from now, for example — whatever period of time you want — that you don’t have billions of people with a personal agent that understands your goals and that is just working on your behalf 24/7 to achieve your goals in whatever the domain is that you care about,” Zuckerberg said on Wednesday’s quarterly earnings call with investors.

He added that he could see people using these agents to help them with their finances, health, interpersonal relationships, and household management.

“As we move toward a future where we’re all interacting with multiple agents, I think that WhatsApp and our other messaging surfaces are going to become increasingly important,” he said, noting that WhatsApp is already the leading platform where users interact with Meta AI.

Meta is not alone in setting high expectations for AI systems that can act on a person’s behalf rather than just answer questions. Google emphasized custom AI agents as a key new feature in its Search overhaul, which sparked outcry from users who felt bogged down by the constant onslaught of AI results on Google. Meanwhile, subscriptions to Anthropic’s Claude have skyrocketed as engineers fawn over the agentic coding assistant Claude Code.

Compared to its competitors, however, Meta may not enjoy as much confidence from investors as it continues dumping cash into innovative projects that may or may not pan out — Meta’s stock dropped almost 10% after posting this quarter’s earnings. Meta’s Reality Labs, the organization responsible for its AR glasses, VR headsets, and related software, lost around $4.6 billion this quarter, roughly in line with the losses the division has posted each quarter since 2021. That’s a running total now of around $88 billion.

Meta’s AI spending is likely to climb even higher, which is more of a concern at this juncture. The company reported free cash flow of $784 million this quarter, down from $8.55 billion the same quarter last year. That’s a 91% drop year over year, exacerbated by the company’s investments in AI infrastructure. This week, Meta and BlackRock announced a partnership to build a $14 billion data center in El Paso, Texas.

“We believe that there will continue to be a significantly higher margin on selling intelligence rather than selling compute directly, but we think that there’s a big opportunity, obviously, to sell compute as well,” Zuckerberg said.

Ultimately, he believes that the personal agents that Meta is developing will be “the foundation for our next wave of products and revenue lines in the months and years ahead.”

So far, Meta’s business agents, rolled out globally on WhatsApp and Messenger this quarter, have been adopted by more than one million businesses. It may be harder to get people to adopt consumer AI agents, but the road to “billions” has to start somewhere — the company can’t get there on enterprise agents alone.

The largest electrical grid in the U.S. has struggled to cope with an onslaught of data centers. Now, after an auction to add more generating capacity fell short, the grid’s operator, PJM Interconnection, has said it will cut off data centers and other large users during power shortages.

The decision arrives as the breakneck pace of data center construction has grid operators scrambling to generate power. By 2035, data centers are expected to use 4x more electricity than they do today.

PJM won’t start curtailing supply until June 2027, and the cuts will only apply to data centers that are 50 megawatts or larger. The grid operator is running another auction for new generating capacity.

Similar to other demand response programs, which have existed for decades and typically include large users like manufacturers, the customers who have their power cut will be compensated. Such programs typically give customers advance notice, ranging from 30 minutes to a few days, depending on forecasted demand. 

The move will likely spur many new data centers — and potentially existing ones — to set up their own sources of on-site power. Those that don’t will probably rely on backup generators, which tend to be costlier to run and frequently more polluting. 

Many data centers favor diesel generators since the fuel is widely available and can be stored on-site. Federal regulations allow such generators to be used for up to 50 hours per year for demand response events, and up to 100 hours per year for events like emergencies and maintenance.

This week, Vantage Data Centers came under fire for its apparent coordination with Virginia environmental regulators to cast doubt on a report that said diesel backup generators could contribute to tens of millions of dollars in annual health damages for people living near a 96 megawatt data center in Northern Virginia.

PJM has come under fire in recent months for the way it has managed new generating capacity and large new users, including data centers. The grid operator’s territory runs from Virginia to Illinois, covering 67 million customers. Over the last year, wholesale electricity prices have nearly doubled, and PJM’s independent market monitor blamed data centers for much of the increase. 

Chinese tech giant Baidu has started testing autonomous vehicles in London as part of its partnership with Lyft and Freenow, the German taxi and multi-mobility app that Lyft now owns. Baidu is the latest in a string of companies to test self-driving technology in the UK ahead of commercial robotaxi deployments.

The testing, which began Tuesday with human safety operators, comes nearly a year after the two companies struck a strategic partnership to deploy Baidu’s purpose-built Apollo Go RT6 robotaxi across key European markets through the Lyft platform. The vehicles will eventually be available through Freenow, which Lyft acquired in 2025 for about $197 million.

That deal gave Lyft a foothold in Europe’s ride-hailing market, where a handful of well-funded companies are now jockeying to be first to market with robotaxis.

London is particular is shaping up to be a key battleground in the region. In April, Waymo began testing its autonomous vehicles with human safety operators in the city. Uber and its self-driving tech partner, Wayve, also announced plans to launch a robotaxi service in London this year. That initial service — which customers can now sign up for on an interest list — will have human safety operators behind the wheel before fully driverless operations begin later.

Baidu and Freenow by Lyft (as the latter service is now called) said they expect to invite the public to hail their robotaxis in 2027. The companies, which didn’t provide a more detailed timeline, noted that the launch will depend on regulatory approval.

For now, dozens of test vehicles will operate within London’s borough of Brent. Lyft and Freenow said they continue discussions with safety and city officials, including Transport for London (TfL) and the Centre for Connected and Autonomous Vehicles (CCAV). The UK government is in the process of creating autonomous vehicle regulations and opened applications in May for companies interested in an AV pilot program that lets companies test self-driving vehicles under government oversight.

When the service does launch, Freenow by Lyft said it will operate a hybrid network — employing the same language rival Uber has used — meaning human drivers operating taxis and private-hire vehicles will work alongside the robotaxis.

“As a platform with deep roots in the taxi industry, our priority is ensuring that autonomous technology supports the professional drivers who keep London moving,” Thomas Zimmermann, CEO of Freenow by Lyft, said in a statement.

The cutting-edge of physical AI resembles a Jenga game inside a warehouse located in San Leandro, California.

That warehouse is home to Encord, a firm specializing in data tools designed to train AI models. Andrew Ceja functions as a pilot—the term the company uses for its robotic trainers—and he is meticulously extracting wooden blocks from a precarious tower while equipped with a headset that features a camera to monitor his view. While this method for gathering robot training data is rather typical, this particular headset is outfitted with sensors that record his brain waves as he skillfully dismantles the block structure.

Encord is among a limited yet expanding group of startups betting that the next significant limitation for humanoid and warehouse robots will not be model architecture, but rather the significant lack of authentic physical training data. Instead of merely aiding robotics firms in managing existing data, Encord aims to cultivate a business model around generating the data that is currently absent.

The brain wave headset that Ceja is utilizing was developed by Zander Labs, a German neuroscience startup betting that gauging brain activity—to infer mental states such as error, intent, and surprise—can produce a more advantageous data set for training models. Encord’s collaboration with Zander is presently a pilot initiative; Encord claims that the objective is to create an initial brain wave-tagged data set, test it through customer robotics models, and assess whether it genuinely enhances performance before deciding to expand the project.

Lucas Gehrke, a neuroscientist from Zander overseeing the project, indicates that the level of brain activity registered at any given moment during a task can provide insights for model developers trying to determine when their highest-effort models should be deployed.

According to Vineeth Velmurugan, Encord’s head of robot learning, this is the “bleeding edge” of addressing the robotics data bottleneck. A former member of OpenAI’s robot lab and Berkshire Grey, a warehouse automation company, Velmurugan joined Encord to establish the company’s internal data-creation team.

Encord was established to assist businesses developing machine-vision applications in annotating data and evaluating models. As their clients—Velmurugan indicates that Encord collaborates with numerous leading robotics companies, although he cannot disclose their names—began implementing end-to-end learning in robotic manipulation tasks, executives realized they needed to create training data independently, not just manage it. “The data simply does not exist,” Velmurugan remarked.

The premise that generative AI can replicate its success with robots as it has with chatbots continues to encounter this same hurdle. Large language models (LLMs) were constructed from the text of the entire internet and beyond. Securing the equivalent raw materials for teaching neural networks about physical manipulation is difficult: self-driving car companies gather this data themselves, but scaling it is a challenge. Training from video can be effective, but it falls short in comparison to authentic real-world data. Velmurugan believes it will necessitate a data set roughly five times larger than YouTube’s video corpus to achieve a breakthrough—a scale that elucidates why data generation has evolved into a commercial endeavor rather than merely a research concern.

Satisfy your egocentric data requirements

Companies developing robotic intelligence are now relying on two primary sources: “Egocentric” video gathered by workers wearing cameras, often supplemented with additional viewing angles and metrics, and data collection from robots operated remotely. Encord engages in both, sourcing egocentric data from various factories worldwide and utilizing its San Leandro facility to test novel modalities, such as brain wave data, or assemble data sets around particular skills for fine-tuning.

During a TechCrunch visit, pilots were operating leader-follower systems—robotic arms working in tandem, with one arm directly controlled by a human and the other mimicking its movements—to gather data on tasks like pouring coffee from a pot into mugs (which tends to be quite messy) and stacking poker chips. “Every humanoid firm has requested these components,” Velmurugan notes.

Storage shelves were filled with boxes of artificial flowers in vases, books, plastic vegetables, cat litter trays and scoops, bags, and bundles of wires, the essential materials used for training manipulators in household tasks.

At one station, another pilot, Sofia Infante, skillfully manipulates robotic arms to connect and disconnect ethernet cables from the back of a server—the type of task data center operators hope could be automated, provided robots could achieve the necessary precision. Trying my hand at the controls, I understood why that remains elusive: robotic grippers are significantly less agile than human fingers and lack the versatility we take for granted in our arms.

Another innovative data modality that Encord is pursuing involves a set of sensors attached to the forearm to detect electrical signals in muscles. Videos of humans manipulating objects typically don’t capture the entirety of the hand, but Velmurugan aims to construct a 3D representation of the hand’s location at any point using the arm sensors, fostering a more comprehensive grasp for models.

Encord’s data sets are accompanied by physical descriptions of the content of each video—“right hand tightens bolt”—to assist LLM-based models in comprehending the actions unfolding. Velmurugan estimates that this type of thorough annotation holds 100 times the value of “poor-quality ego data” for training specific tasks, and it incurs only 20 times the cost to produce, which, on paper, seems like a reasonable exchange.

However, “20 times more” is still a substantial sum, and that’s the challenge: scraping content from the internet, as LLM creators did by sourcing information from Stack Overflow and other websites, cost frontier labs nearly nothing. Producing physical training data does not, and that represents the limitation of comparing physical AI to LLMs. This kind of data must be generated, not merely gathered, which alters the economics of constructing these models.

Velmurugan claims that advancements are underway—with Encord’s insight into programs across the industry, he can observe startups and frontier labs alike determining what is effective and what is not in enhancing physical AI models. This perspective—being positioned among many robotics firms simultaneously—is also part of Encord’s appeal. It can identify which data techniques are gaining traction across the industry prior to any individual customer.

This will keep the roughly dozen pilots at Encord’s facility engaged. Both Infante and Ceja are members of an emerging workforce constructing the foundations for neural networks; they previously worked at Scale, another AI data annotation firm, before joining Encord.

Ceja had experience at a waste management firm where his technological interests led him to oversee the maintenance of a robotic trash sorter. Now, as the Jenga tower teeters, he expresses his enjoyment of the challenges presented by training tasks for robots — “It’s something new every day!”

The introduction of the newest AI model from a Chinese firm — Moonshot AI’s Kimi — has sparked renewed discussions about American competitiveness and the contrast between open and proprietary AI.

While social media has seen abundant discourse, it appears this conversation is also taking place behind closed doors in Washington, D.C., where OpenAI and Anthropic have allegedly lobbied policymakers regarding concerns over open Chinese models.

In the recent episode of TechCrunch’s Equity podcast, Kirsten Korosec, Sean O’Kane, and I deliberated why this issue seems to provoke such strong reactions. Beyond the idea that some individuals should “touch grass” instead of spending weekends debating on X, Sean highlighted that, in many respects, this “feels like we’re witnessing echoes of past anxieties,” with many in Silicon Valley “anticipating that something is about to emerge and overshadow everything else.”

Kirsten observed that imposing strict limitations on Chinese AI models might primarily serve the interests of a select few companies: “Are we promoting and ensuring that Americans prevail in the AI race, or are we guaranteeing that specific frontier labs outperform others?

Continue reading for a snippet of our discussion, modified for brevity and clarity.

Anthony Ha: For those who have kept track of the conversations around Chinese AI, this will likely seem very familiar from the launch of DeepSeek, where essentially a Chinese model is released; on certain benchmarks, it appears to perform comparably with some of the frontier models; and a segment of the tech industry goes into a frenzy. 

Some of this [debate] received additional attention because one of the individuals commenting about it was [an executive] at OpenAI. However, there [is] this persistent question of: Can Chinese firms surpass US firms, at least in some facets, and do so much more affordably and transparently?

Sean O’Kane: Indeed, many aspects of this feel reminiscent of previous crises. One of my favorite instances is: Everyone is so prepared for and expects that something is going to emerge and overshadow everything else. My favorite illustration from this past week was people revealing that “Wow, Kimi completed an entire replication of macOS in 30 minutes.” Sure, it produced an impressive graphical imitation of macOS, but it’s not an operating system.

We continually see these occurrences where everyone in the tech sector is so on edge. Particularly with some of the Chinese models that surface, there’s this anticipation, and I think this gets to the heart of why people reacted so strongly last weekend. (Also, by the way: Go outside, connect with nature, it’s the weekend. Everyone in the industry was exchanging jabs on Twitter all weekend.) But this restlessness is quite intriguing to me because, a week later, I don’t sense that anyone is feeling as dire as they did a week ago.

Kirsten Korosec: We have an excellent piece by one of our reporters, Tim Fernholz, who endeavors to unpack the frenzy surrounding this in the United States. He identifies several factors, ultimately concluding — and I don’t want to preempt his conclusion, but I suspect one stands out more than others. 

There are worries that these Chinese open weight models may possess an inherent bias towards China, alongside other concerns regarding security and safeguards. However, a significant concept here is protectionism, and who is going to metaphorically “win the race”? Will it be the US or China? This appears to be fueling much of the anxiety. 

I’m curious if you concur with that, Anthony?

Anthony: I completely concur. I think the China element invariably injects a certain degree of hysteria. And I’m not implying that people shouldn’t be wary of how the U.S. measures up against China across various sectors. But the panic elevates so quickly.

This also reminds me of the discussions surrounding TikTok a few years back. It’s not that I believed the concerns surrounding TikTok were entirely unfounded, but the extent of the alarm was striking — it appears that as soon as the term China is added to any dialogue, the intensity escalates dramatically.  Moreover, in this instance, it’s tied to discussions about open [weights] and the notion that AI is so potent and potentially hazardous that the only way to manage it is through these proprietary models from American frontier firms. 

Naturally, most individuals asserting this [have] motivations behind their claims. David Sacks, who served as the AI czar for the Trump administration [and] now holds a different role within the Trump administration, was vocal on X about how, “I can’t believe individuals are opposing data centers, we’re complicating our position, there’s too much regulation.” Hence, it’s a method to advocate for the viewpoints they already hold regarding AI. “If China surpasses us, that’s unimaginable, so you must comply with what I advocate.”

Kirsten: Exactly, and if we were to impose universal bans on Chinese open weight models — I’m not suggesting that there aren’t legitimate concerns here, but let’s consider this scenario. Should we do so, it would favor models developed by OpenAI, for example, and compel businesses to utilize those instead of opting for models like Kimi.

Thus, we must truly ask: Are we promoting and ensuring that Americans outpace others in the AI race, or are we merely ensuring that particular frontier laboratories excel over the others?

Sean: At this juncture, it should be noted that a significant portion of this discussion was sparked by the head of strategic futures at OpenAI, Dean Ball, who was the first to publish an extensive post mentioning some of these apprehensions.

One part of me speculates that the backlash was due to disagreements with what Dean articulated. Another part believes the reaction stemmed from the fact that he simply voiced the concern aloud. He essentially posited that the US should create regulatory FUD — fear, uncertainty, and doubt — and complicate the capacity for these open weight models to compete with the US. [Ball subsequently distanced himself from this argument.]

To me, it seems you can interpret some responses from individuals, like, “You’re not meant to vocalize that, Dean.”