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Waymo Admits Its Robotaxis Are Often Controlled By Workers In The Philippines

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Waymo’s robotaxis may drive themselves on U.S. roads, but many of their most critical decisions are still made by human operators sitting thousands of miles away in the Philippines.

Waymo has long been held up as the gold standard of autonomous driving, a symbol of how far artificial intelligence has progressed beyond human control. Yet testimony at a recent U.S. Senate hearing has exposed a less visible but increasingly important reality: when Waymo’s vehicles encounter situations they cannot resolve on their own, responsibility often shifts to remote human workers, many of whom are based in the Philippines.

The disclosure by Waymo’s chief safety officer, Mauricio Peña, cut through years of marketing around “driverless” technology. Peña told lawmakers that in rare or complex scenarios, Waymo’s robotaxis can hand over control to remote operators who guide the vehicle through the situation. These workers act as a form of last-resort intelligence, stepping in when sensors, software, and pre-trained models are insufficient to safely navigate the real world.

What unsettled lawmakers was not simply the existence of human intervention, but where that intervention takes place. The Philippines has become a global hub for outsourced digital labor, from call centers to content moderation and data labeling. Waymo’s reliance on Filipino contractors places the country at the center of America’s most advanced autonomous driving program, even as public messaging continues to emphasize full autonomy.

The logic is partly economic and partly structural for Waymo. Remote driving and support roles require a large, always-available workforce trained to respond quickly to unpredictable scenarios. The Philippines offers a deep labor pool with strong English proficiency and long experience supporting U.S. technology firms. Costs are lower than in the United States, and maintaining round-the-clock coverage across time zones is easier. In practice, this makes the Philippine workforce a quiet but critical component of Waymo’s safety architecture.

The arrangement, however, raises uncomfortable questions about what autonomy really means. If a robotaxi in San Francisco freezes at a construction zone or behaves unpredictably around emergency vehicles, a human operator in Manila or Cebu may be the one deciding how it proceeds. That human judgment, mediated through screens and networks, becomes part of the driving system itself. Autonomy, in this sense, is not the absence of humans but a reorganization of where and how their labor is used.

But this has prompted safety concerns. Senators pressed Peña on latency and reliability, given the physical distance between vehicles and operators. Even small delays in communication could matter in traffic situations unfolding in seconds. Peña maintained that Waymo has built safeguards into its systems and that remote intervention is tightly controlled. Still, the hearing underscored a basic tension: the more robotaxis are deployed at scale, the more edge cases arise, and the more human backup is required.

The focus on foreign workers also reflects a broader shift in Washington’s thinking about technology and national control. Massachusetts Senator Ed Markey called the use of overseas remote drivers “completely unacceptable,” framing the issue not just as a labor question but as one of sovereignty and security. Lawmakers voiced unease about critical transportation decisions being influenced by workers outside the United States, particularly as autonomous vehicles become more integrated into urban infrastructure.

Waymo’s case is especially sensitive because of its hardware choices. Unlike Tesla, which builds and controls its own vehicles, Waymo uses cars manufactured in multiple countries, including China. Although Peña emphasized that Waymo’s autonomous systems are installed and managed in the U.S., the combination of foreign-built vehicles and foreign-based operators has fueled suspicions about vulnerabilities in the system. In an era of heightened scrutiny over supply chains and data flows, even indirect links to China or other overseas networks attract political attention.

For the Philippine workforce itself, the role highlights another recurring pattern in the AI economy: essential labor that remains largely invisible. Much like the content moderators and data annotators who helped train large language models, remote operators supporting robotaxis occupy a gray zone. They are central to system performance but rarely acknowledged in public narratives about innovation. Pay, working conditions, and long-term career prospects for these workers are seldom discussed, even as their decisions can carry real-world consequences.

Although Waymo is not alone in this model, its prominence makes it a test case. The company has positioned itself as a leader in safe, scalable autonomy, operating commercial robotaxi services in multiple U.S. cities. As deployments expand, reliance on remote human support may grow rather than shrink, at least in the near term. That reality complicates claims that autonomy will soon eliminate human involvement in driving altogether.

The Senate hearing suggests that regulators are beginning to look past the surface of AI systems to examine the labor structures beneath them. Scrutiny of Waymo’s Philippine workforce is unlikely to fade. It touches on safety, labor practices, national security, and the credibility of autonomy itself. The technology may be cutting-edge, but its foundations rest on human judgment — relocated, outsourced, and largely unseen.

EU Moves to Rein In Meta’s AI Strategy on WhatsApp, Threatens Interim Measures

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By threatening interim measures against Meta, EU regulators are signaling that access to dominant platforms like WhatsApp will be treated as a frontline competition issue in the AI era, not one to be settled years later.

European Union competition regulators have taken a decisive step against Meta Platforms, warning that they may order the U.S. technology group to stop blocking rival artificial intelligence services from WhatsApp while an antitrust investigation is still ongoing.

The move underscores Brussels’ growing determination to intervene early in fast-moving digital markets, particularly as AI becomes embedded in services used daily by hundreds of millions of people.

On Monday, the European Commission said it had sent a statement of objections to Meta, formally accusing the company of breaching EU competition rules by abusing a dominant position. At issue is Meta’s decision, implemented on January 15, to allow only its own Meta AI assistant to operate on WhatsApp, effectively excluding competing AI chatbots from access to the messaging service’s Business API.

What makes the case especially significant is the Commission’s stated willingness to impose interim measures, a tool used sparingly in EU antitrust enforcement. Such measures are designed to prevent “serious and irreparable harm” to competition before a final ruling is reached, reflecting regulators’ concern that delays could allow market power to become entrenched beyond repair.

“We must protect effective competition in this vibrant field,” EU antitrust chief Teresa Ribera said, arguing that dominant technology companies should not be allowed to leverage their existing platforms to tilt emerging AI markets in their favor.

Ribera said the Commission was considering swift action to preserve access for competitors to WhatsApp while the investigation proceeds, warning that Meta’s policy risks causing lasting damage to competition in Europe.

The case sits at the intersection of two forces reshaping global regulation: the rise of generative AI and the EU’s increasingly interventionist stance toward Big Tech. WhatsApp is one of the most widely used messaging platforms in Europe, giving Meta a powerful distribution channel at a time when AI companies are racing to integrate chatbots into consumer and business workflows. Regulators fear that denying rivals access to such a platform could distort competition at a formative stage of the market.

Meta has rejected that view, saying the Commission is overstating WhatsApp’s importance as a gateway for AI services. In an emailed statement, a company spokesperson said there was “no reason for the EU to intervene,” adding that users can access AI tools through app stores, operating systems, devices, websites, and partnerships across the industry. The spokesperson said the Commission’s reasoning “incorrectly assumes the WhatsApp Business API is a key distribution channel for these chatbots.”

The dispute echoes similar concerns raised outside Europe. In December, Italy’s competition authority moved against Meta on the same issue, prompting the Commission to cite the Italian case as a precedent for considering interim measures. By contrast, a Brazilian court last month suspended an interim order imposed by that country’s antitrust agency, illustrating how regulators and courts globally are still grappling with how to apply competition law to AI-driven markets.

However, the urgency appears to outweigh the risk of legal pushback for Brussels. The Commission has repeatedly argued that traditional antitrust timelines, which can stretch over several years, are ill-suited to digital markets where competitive dynamics can shift in months. In AI, officials worry that first-mover advantages tied to user access and data could lock in winners long before regulators reach final decisions.

The investigation also highlights the EU’s willingness to press ahead with enforcement despite criticism from the United States, where officials and industry groups have accused European regulators of disproportionately targeting American technology companies. The Commission has insisted that its actions are technology-neutral and grounded in law, even as geopolitical tensions rise around trade, industrial policy, and technological leadership.

The outcome of the case could have implications far beyond Meta. A decision to impose interim measures would send a strong signal to other platform owners that integrating proprietary AI tools into dominant services may attract swift regulatory scrutiny if rivals are shut out. It would also reinforce the EU’s broader strategy under its competition rules and new digital laws to keep markets open while technologies are still evolving.

The case adds to Meta’s mounting regulatory burden in Europe, where it is already subject to obligations under the Digital Markets Act. For the AI sector, it sharpens a central question: whether control over platforms with massive user bases will be allowed to shape the competitive landscape of artificial intelligence, or whether regulators will step in early to keep those gateways open.

The Commission said its decision on interim measures will depend on Meta’s response and its right of defense. Even so, the warning alone marks a turning point, suggesting that in the race to define the AI economy, Europe is no longer prepared to wait until the finish line to decide whether the competition was fair.

Alphabet Returns to Debt Markets as AI Spending Soars, Warning of New Risks to Ads and Excess Capacity

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Alphabet’s AI spending spree is reshaping its balance sheet, risk profile, and core business model, marking a turning point in how even Big Tech funds and manages growth.

Alphabet is turning again to the debt market to help bankroll one of the most aggressive artificial intelligence investment drives in corporate history, while simultaneously flagging fresh risks tied to the technology’s rapid rise and the sheer scale of its infrastructure buildout.

The renewed trip to the debt market is seen as a signal that the artificial intelligence boom has reached a scale where even one of the world’s most cash-generative companies is rethinking how it finances growth — and openly acknowledging the risks that come with it.

In its latest annual filing, released alongside earnings, the Google parent laid out a more cautious narrative around AI than investors have been accustomed to hearing. While executives continue to frame AI as a once-in-a-generation opportunity, the company also warned that the speed, cost, and uncertainty of the buildout could strain operations, expose it to financial liabilities, and, in a worst-case scenario, leave it sitting on underutilized infrastructure.

That backdrop explains why Alphabet is preparing to raise about $20 billion through a multi-tranche bond sale, including an ultra-long 100-year sterling bond, according to people familiar with the deal quoted by CNBC. Demand has been strong, with the offering reportedly several times oversubscribed, underscoring how eager investors remain to lend to top-tier technology names even as borrowing rises.

The fundraising follows a $25 billion bond sale late last year and caps a sharp pivot in Alphabet’s capital structure. Long-term debt quadrupled in 2025 to $46.5 billion, a striking shift for a company that, for years, relied overwhelmingly on internal cash flows. The reason is simple: the scale of AI investment now dwarfs what operating cash alone can comfortably support without trade-offs.

Alphabet said it may spend up to $185 billion in capital expenditures this year, more than double its 2025 outlay. That figure places it firmly among the biggest corporate spenders on infrastructure in history. The money is being poured into data centers, specialized chips, custom silicon, power generation, cooling systems, and high-capacity networking — all essential to training and running large language models such as Gemini.

Yet the filing makes clear that this is not just about owning servers. Alphabet is increasingly relying on long-term leasing arrangements with third-party data-center operators to secure capacity quickly. While this approach accelerates deployment, it also raises costs and complexity, and creates contractual obligations that could become a burden if demand forecasts miss the mark.

Large commercial agreements, the company warned, could increase liabilities if Alphabet or its partners fail to perform. This is a notable admission in an industry that has largely emphasized upside while downplaying execution risk.

Management is keenly aware of the tension. Chief financial officer Anat Ashkenazi told analysts the company wants to invest “in a fiscally responsible way” and preserve a strong financial position. But when CEO Sundar Pichai was asked what worries him most, his answer was blunt: compute capacity. Power availability, land, supply chains, and the pace of expansion now dominate the strategic agenda.

Alphabet’s predicament mirrors a broader shift across Big Tech. Microsoft, Meta, and Amazon are all dramatically increasing capital spending, and together with Alphabet are projected to lift capex by more than 60% this year compared with already record levels in 2025. The collective outlay is fueling what Nvidia CEO Jensen Huang has described as the largest infrastructure buildout in human history.

For Alphabet, however, the stakes extend beyond infrastructure efficiency. AI is beginning to intersect directly with its core advertising business, which still accounts for the majority of revenue and profits. As generative AI tools become more capable, there is a growing question about how users interact with the internet — and whether traditional search, the backbone of Google’s ad machine, could be disrupted.

That concern appeared explicitly in Alphabet’s risk disclosures for the first time. The company acknowledged that consumers may reduce their use of conventional search as AI assistants answer questions directly, potentially altering traffic patterns and advertising formats. Alphabet said it is adapting with new ad products and strategies, but cautioned there is no guarantee these efforts will succeed.

So far, financial performance suggests resilience. Advertising revenue rose 13.5% year on year in the fourth quarter to $82.28 billion, easing fears that AI is already eroding demand. But investors are looking beyond current results to the medium-term implications of a shift in user behavior that could be structural rather than cyclical.

At the center of Alphabet’s AI push is Gemini, its flagship model and assistant, which now boasts more than 750 million monthly active users, up sharply from the previous quarter. The rapid uptake underscores why the company feels compelled to invest at scale: falling behind rivals such as OpenAI or Anthropic would risk ceding influence over the next generation of computing interfaces.

Yet scale cuts both ways. The more capital Alphabet commits upfront, the greater the risk of overcapacity if monetization lags, competition intensifies, or technological change renders today’s infrastructure less valuable. The company’s own warning about “excess capacity” reflects a growing recognition that the AI arms race may not deliver returns evenly or immediately.

Alphabet’s decision to lean more heavily on debt also reflects a subtle recalibration of financial strategy. Borrowing allows the company to preserve cash for flexibility, smooth out investment cycles, and take advantage of still-favorable credit markets. But it also ties Alphabet more closely to investor sentiment and interest-rate dynamics, at a time when capital markets are increasingly sensitive to execution risk.

In that sense, the bond sale is symbolic. It shows how AI has pushed Big Tech into territory once associated with capital-intensive industries such as energy or telecommunications, where long-dated assets, fixed costs, and leverage are part of the business model.

Currently, Alphabet retains enormous advantages: scale, data, engineering talent, and a balance sheet that remains strong by almost any measure. But its latest filings and financing plans suggest a more candid tone about the challenges ahead. The AI boom may still be in its early innings, but Alphabet is already grappling with the reality that building the future of computing is expensive, complex, and fraught with trade-offs that even Google cannot fully control.

Crypto Funds Recorded Over $1.5B Outflows Last Week 

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Crypto funds record over $1.5B worth of outflows last week refer to recent reports on digital asset investment products including spot Bitcoin ETFs and broader crypto ETPs, amid a sharp market correction in early 2026.

U.S. spot Bitcoin ETFs saw significant outflows, with reports citing around $1.5 billion in net outflows over a multi-day streak. This contributed to broader selling pressure as Bitcoin’s price declined notably from highs near $98,000 toward lows under $60,000 in some reports.

This streak ended with a rebound of approximately $562 million in inflows on February 2, 2026, though outflows resumed in subsequent days. For the most recent week, global crypto investment products (ETPs/digital asset funds) recorded much lower net outflows of $187 million, per CoinShares’ weekly report.

This marks a sharp slowdown down ~89% from prior weeks’ figures around $1.7 billion each, after two consecutive heavy outflow weeks totaling billions. Analysts interpret this as an early sign of potential stabilization or an inflection point in investor sentiment, despite ongoing price weakness.

Bitcoin products led with $264 million in outflows. Some altcoins saw inflows like XRP ~$63 million, Solana and Ethereum modest positive flows. Total assets under management (AuM) for these products fell to $129.8 billion, the lowest since March 2025.

Trading volumes hit record levels ~$63 billion weekly suggesting high activity amid the volatility. The crypto market has faced headwinds including institutional selling, macro uncertainty, whale activity, and geopolitical factors, leading to significant Bitcoin price drops, liquidations (billions in leveraged positions), and ETF AuM declines (e.g., spot Bitcoin ETFs dipping below $100 billion in some periods).

Year-to-date flows have turned negative in places, with cumulative outflows flipping prior inflows. The trend has moderated recently, hinting at easing pressure.

The recent wave of crypto fund outflows (over $1.5B in prior weeks, slowing to $187M globally last week per CoinShares) has had a mixed but predominantly negative impact on Ethereum ETFs, particularly U.S. spot products.

Ethereum has faced sustained pressure amid broader market weakness, with ETH price dropping sharply around 34.5% in one reported week to lows near $1,850–$2,000 as of early February 2026.

U.S. Spot Ethereum ETFs saw significant net outflows of approximately $166M for the week (February 2–6), marking the third consecutive week of net redemptions. This contrasts with the broader global crypto ETP slowdown.

BlackRock’s iShares Ethereum Trust (ETHA) led outflows, with ~$152M pulled (despite strong historical inflows of $12B+). Fidelity’s (FETH) saw notable redemptions (~$59M in some breakdowns). Grayscale’s Ethereum Mini Trust showed a relative bright spot with inflows (~$33M).

Daily granularity showed ongoing pressure, e.g., $16.75M–$16.8M net outflow on February 6 alone; third straight day of withdrawals in some reports with BlackRock’s ETHA contributing heavily. In contrast to U.S. spot trends, global CoinShares data for Ethereum products showed modest inflows of $5.3M last week—highlighting divergence between U.S.-focused spot ETFs and broader/ international ETPs.

Persistent ETF outflows have removed direct spot buying support, exacerbating ETH’s decline amid macro factors like Fed policy caution, geopolitical risks, whale activity. This has contributed to high liquidations ~$1–1.2B in ETH futures and negative funding rates, signaling risk-off deleveraging.

Bitcoin products drove most outflows ~$264M globally, $318M U.S. spot, but Ethereum’s relative resilience in some global metrics (small inflows) suggests selective investor rotation toward altcoins like XRP ($63M inflows) and Solana ($8.2M).

Outflow pace has moderated overall (down sharply from prior $1.7B+ weeks), with record ETP trading volumes (~$63B) indicating repositioning rather than full exodus. Ethereum’s modest global inflows could hint at early recovery interest, though U.S. spot weakness persists.

Ethereum spot ETFs sit around $12B total, with cumulative net inflows still positive historically ($11.8B), but recent trends have eroded momentum. Ethereum ETFs have been hit harder than the global average in U.S. spot terms (third week of outflows), contributing to downward price pressure and investor caution.

However, smaller global inflows and slowing total crypto outflows suggest potential stabilization or rotation away from BTC dominance.

Bitcoin Hashrate Faces Largest Single Downward Adjustment 

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Bitcoin’s mining difficulty has experienced its largest single downward adjustment since China’s 2021 mining crackdown, with a drop of approximately 11% specifically around 11.16% in the most recent reports.

This adjustment took effect recently, at block height ~935,424, reducing the difficulty from over 141.67 trillion to about 125.86 trillion. This marks one of the steepest declines in Bitcoin’s history outside of the massive hashrate exodus following China’s ban in mid-2021, which caused even larger drops in some cases.

The decline stems from a significant reduction in the network’s total hashrate estimated ~20% drop in some analyses, as miners went offline or reduced operations due to: Plummeting Bitcoin prices — BTC has fallen sharply from recent highs, pressuring profitability.

From peaks around $70/PH/s to roughly $35/PH/s, making many operations unprofitable. Severe winter storms in the U.S. causing widespread power outages and forcing curtailments, especially in major mining regions. Some miners shifting resources to higher-margin opportunities like AI computing projects.

This “miner capitulation” — where less efficient or higher-cost operators shut down — is a natural self-correcting mechanism in Bitcoin’s protocol. Difficulty adjusts every ~2,014 blocks roughly two weeks to keep average block times near 10 minutes.

With slower block times drifting to ~11.4 minutes pre-adjustment, the network lowered difficulty to make mining easier again. Short-term relief for remaining miners — Lower difficulty reduces competition, potentially boosting rewards and profitability for those still online; an “automatic pay raise” in BTC terms for survivors.

Hashrate dips temporarily reduce security, but the adjustment helps stabilize it by incentivizing participation. It highlights stress in the mining sector amid broader crypto market weakness, but historically, such resets have preceded recoveries as inefficient players exit and stronger ones consolidate.

The next difficulty adjustment is estimated around February 19-20, 2026, with projections suggesting a rebound (increase) to around 140-143 trillion if hashrate stabilizes or recovers. This event underscores Bitcoin’s resilience through automatic difficulty adjustments, even during tough periods.

Hashrate often written as “hash rate” is one of the most important metrics in Proof-of-Work (PoW) blockchains like Bitcoin. It measures the total computational power dedicated to the network for mining—specifically, how many hash calculations (guesses) the entire network (or an individual miner/machine) can perform per second.

In Bitcoin mining, miners compete to solve a cryptographic puzzle by finding a specific number (called a nonce) that, when combined with the block’s data and run through the SHA-256 hashing algorithm, produces a hash (a 256-bit/64-character hexadecimal number) that meets the current target — meaning the hash starts with a certain number of leading zeros (or is below a very small threshold value). Each attempt to find this valid hash is one “hash calculation.”

Because the output is essentially random, miners must make billions/trillions of guesses per second ? this brute-force guessing is what consumes enormous electricity and requires specialized hardware.

 

The Bitcoin network’s total hashrate is fluctuating around ~980–1,100 EH/s roughly 1 ZH/s in recent peaks before the recent drop, down from higher levels earlier in 2026 due to the difficulty adjustment and temporary offline capacity (e.g., from U.S. winter storms).

Difficulty is a separate but tightly linked parameter: it automatically adjusts every 2,016 blocks (~2 weeks) to keep average block time at 10 minutes. If hashrate rises sharply (more miners join), blocks get found faster ? difficulty increases to make the puzzle harder.

If hashrate drops (miners go offline), blocks slow down ? difficulty decreases as we just saw with the ~11% drop. In practice, network hashrate and difficulty move in tandem over time: higher hashrate forces higher difficulty, and vice versa.

The network estimates total hashrate from observed block times and current difficulty (it’s not directly reported by every miner). A high hashrate makes the blockchain extremely resistant to attacks, especially a 51% attack; where an attacker controls >50% of hashrate to rewrite history, double-spend, or censor transactions.

With ~1,000 EH/s today, mounting a 51% attack would require controlling hardware and energy equivalent to a small country’s power grid — astronomically expensive and practically infeasible. Rising hashrate generally indicates growing participation, new efficient hardware (ASICs), and miner confidence in Bitcoin’s future price/profitability.

Hashrate per unit of power determines who survives low-price environments. When BTC price falls or electricity costs rise, inefficient miners capitulate ? hashrate drops temporarily ? difficulty adjusts down ? survivors get a relative “pay raise” in BTC rewards.

Sudden large drops like the recent one signal stress while steady climbs show resilience and investment in infrastructure. Hashrate is Bitcoin’s “heartbeat” — it quantifies the raw brute-force computing muscle protecting the ledger, self-regulating through difficulty adjustments, and reflecting miner economics in real time.