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Micron Shares Jump 7% as Chipmaker Boosts U.S. Investment to $250bn, Strengthens AI Supply Chain

By
Samuel Nwite
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0

Micron Technology shares climbed 7% on Thursday after the memory chipmaker unveiled a sweeping expansion of its U.S. investment plans, underscoring its confidence that surging artificial intelligence demand will continue driving the semiconductor industry’s growth for years to come.

The company announced it will increase its planned U.S. investment to $250 billion through 2035, raising its previous commitment by $50 billion as it moves to expand domestic manufacturing and secure critical supplies needed to support next-generation memory chips.

The announcement also included a new strategic investment of up to $3 billion aimed at strengthening the U.S. semiconductor supply chain, particularly the production of silicon wafers, one of the most essential raw materials used in chip manufacturing.

As part of that initiative, Micron will invest $500 million in GlobalWafers, the Taiwan-headquartered silicon wafer manufacturer, to expand wafer development and production at the company’s facilities in Texas. The two companies have also signed a 10-year supply agreement that will secure long-term access to raw silicon wafer capacity, providing Micron with greater certainty over one of the industry’s most critical manufacturing inputs.

Silicon wafers form the foundation upon which semiconductor chips are built, making them indispensable to the production of memory chips used in data centers, smartphones, computers and AI systems.

By locking in long-term wafer supplies, Micron aims to reduce supply chain risks at a time when demand for semiconductors continues to outpace production capacity in several segments of the industry.

“Securing a reliable supply of critical input materials is essential to supporting Micron’s long-term growth and technology roadmap,” Ben Tessone, Micron’s Chief Procurement Officer, said in a statement announcing the agreement.

Semiconductor manufacturers have been making efforts to localize more of their supply chains within the United States following years of disruptions caused by the COVID-19 pandemic, geopolitical tensions and growing concerns over dependence on overseas production.

The latest spending commitment also highlights the scale of investment required for Micron to meet the computing demands created by artificial intelligence. The rapid expansion of AI infrastructure has fueled record demand for advanced memory technologies, including high-bandwidth memory (HBM), DRAM and NAND flash storage, all of which are essential components in AI servers and data centers.

As technology companies race to build larger AI clusters, memory manufacturers have responded by accelerating investments in fabrication plants, manufacturing equipment and raw material supply agreements.

Micron’s decision to raise its planned U.S. investment to $250 billion through 2035 positions the company among the largest private investors in America’s semiconductor industry. The announcement aligns with a broader industry trend in which chipmakers are committing hundreds of billions of dollars to expand manufacturing capacity as governments encourage greater domestic production of strategically important technologies.

Investors welcomed the announcement, sending Micron shares sharply higher during Thursday’s trading session.

The optimism extended well beyond Micron.

The broader semiconductor sector rallied as investors interpreted the company’s increased spending as another signal that demand linked to artificial intelligence remains exceptionally strong. Shares of semiconductor equipment manufacturers Applied Materials, KLA Corporation, and Lam Research each gained around 7%, while chip designer Arm Holdings surged 11%. The gains reflected expectations that companies supplying manufacturing equipment and chip designs will benefit alongside memory producers as investment in AI infrastructure continues to accelerate.

Analysts note that the rally also reinforced confidence that semiconductor companies remain willing to commit substantial capital to future production despite ongoing investor debate about whether AI-related spending can maintain its current pace.

Over the past year, analysts have questioned whether technology companies’ massive investments in AI infrastructure will ultimately generate returns sufficient to justify their cost. However, Micron’s decision to expand its investment programme suggests the company expects demand for advanced memory products to remain robust well into the next decade.

The long-term agreement with GlobalWafers further demonstrates how semiconductor manufacturers are moving beyond simply expanding fabrication capacity to securing every stage of the production process. The semiconductor manufacturing chain depends on a complex network of suppliers providing raw materials, specialized equipment, chemicals, and advanced manufacturing technologies. Any disruption in those inputs can delay production and affect deliveries to customers.

Micron is thus attempting to reduce those risks and strengthen the resilience of its operations by investing directly in wafer manufacturing capacity while simultaneously securing decade-long supply agreements.

The announcement has also bolstered the growing importance of semiconductor manufacturing in the United States. Federal incentives aimed at expanding domestic chip production have encouraged manufacturers to announce new factories, research facilities and supplier partnerships as Washington seeks to reduce reliance on overseas semiconductor production and strengthen national supply chains.

For Micron, the latest investment underscores the company’s belief that artificial intelligence will remain one of the semiconductor industry’s most powerful growth drivers. This is because as AI models become larger and data centers require increasing amounts of memory, demand for advanced memory chips is expected to continue rising, supporting further investment across the semiconductor ecosystem.

Thursday’s market reaction suggests investors share that view. Rather than seeing Micron’s expanded spending as an added financial burden, the market interpreted it as evidence that one of the world’s leading memory manufacturers expects the AI boom and the demand for the chips that power it to remain strong for many years.

Uber Deploys ‘Agentic Pods’ To Embed AI Engineers Across Business Units, Slashing Task Completion Times

By
Samuel Nwite
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0

Uber is expanding its use of artificial intelligence beyond software development by embedding teams of AI specialists directly into business departments to redesign workflows and build AI agents capable of automating complex operational tasks.

The initiative, known internally as “Agentic Pods,” reflects a growing shift among large technology companies from using AI primarily as a coding assistant to deploying autonomous AI systems that can perform multi-step business processes traditionally handled by employees.

Praveen Neppalli Naga, Uber’s technology chief, said in a post on X that the company assigned 30 of its most AI-proficient engineers to work alongside employees in departments including finance, legal and human resources.

Rather than relying on process documentation, the engineers spent two weeks observing how teams actually performed their day-to-day work before building AI agents tailored to those tasks.

“You can’t automate them effectively by looking at process diagrams or documentation,” Naga wrote. “You have to understand how the work actually gets done.”

Over the past two months, Uber has completed 16 Agentic Pods, each focused on identifying repetitive, time-consuming work that could be redesigned with AI.

AI Agents Target Complex Business Processes

Unlike conventional automation software that follows predefined rules, Uber’s AI agents are designed to interact with multiple internal systems, gather information, analyse data and complete workflows that previously required human intervention.

Many of the projects involved tasks that employees performed manually across several software platforms. One example cited by Naga was the preparation of financial pacing reports, which previously required staff to collect information from multiple systems over two days.

With AI agents handling much of the process, those reports can now be generated in approximately 10 minutes.

Another workflow involved allocating capital across the roughly 150 cities where Uber operates. According to Naga, a task that previously required about 15 hours of manual analysis now takes around 30 minutes using AI agents.

Uber’s approach also highlights the growing importance of a relatively new role within the technology industry. Instead of remaining within engineering departments, AI specialists are increasingly working directly with business teams to understand operational challenges before designing AI-powered solutions.

The so-called forward-deployed engineers have become one of the few technology hiring categories to remain active even as broader layoffs continue across the industry. These engineers typically work closely with customers or internal business units, translating operational needs into AI applications rather than focusing solely on software development.

Uber has effectively adopted that model internally by embedding engineers within its own departments.

The strategy is seen as a recognition that many business processes rely on informal knowledge and practical experience that cannot easily be captured in manuals or workflow diagrams. Observing employees perform their work often reveals shortcuts, exceptions and decision-making patterns that AI systems need to replicate effectively.

AI Spending Under Scrutiny

Uber’s expansion of AI agents comes as technology companies continue investing heavily in artificial intelligence while facing growing pressure from investors to demonstrate measurable returns.

Like many large technology firms, Uber has significantly increased spending on AI infrastructure, software tools and model access.

Earlier this year, Naga told The Information that Uber exhausted its annual budget for Anthropic’s Claude Code AI assistant well before the end of the year, underscoring the rapid pace of the company’s AI adoption.

However, senior executives have acknowledged that translating those investments into customer-facing products has proved more challenging.

In May, Uber Chief Operating Officer Andrew Macdonald said on a podcast that the company was finding it increasingly difficult to justify the scale of its AI spending. Although AI has improved internal productivity, Macdonald said the investments have not yet resulted in a proportional increase in useful consumer features for riders or drivers.

However, Uber’s Agentic Pods underpins one of the fastest-growing areas of enterprise AI: the deployment of autonomous agents capable of carrying out sequences of tasks with limited human supervision. Unlike traditional chatbots, AI agents can retrieve information, make decisions based on predefined objectives, interact with enterprise software, and complete end-to-end workflows.

Many technology companies see such systems as the next stage in workplace automation, potentially reshaping functions ranging from finance and legal operations to customer service and supply chain management.

For Uber, the focus appears to be on improving internal efficiency before expanding AI capabilities into consumer products. Naga said the company intends to scale the initiative by establishing a dedicated team focused exclusively on identifying business processes that can be redesigned with AI.

“We’re now forming a dedicated team to scale this further and go deeper,” he said.

“They’ll deeply understand the work, redesign it from the ground up, and use AI to fundamentally change how the business operates.”

The initiative suggests Uber views AI not simply as a tool for incremental productivity gains, but as a means of fundamentally restructuring how work is performed across the company.

July’s Strong Seasonal Record Faces a Reality Check as CryptoQuant’s Bull Score Remains Weak

By
Paul Ugbede Godwin
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0

July has historically been one of Bitcoin’s stronger months, often delivering positive returns even during challenging market cycles. Over the past decade, the month has closed in the green more often than not, reinforcing the idea that seasonal trends can influence investor sentiment.

Bitcoin gained approximately 20% in July 2018 despite trading in the aftermath of the 2017 bull market collapse. Similarly, the cryptocurrency rallied about 17% in July 2022, recovering from one of its steepest drawdowns amid tightening monetary policy and widespread market uncertainty.

These historical performances have encouraged traders to view July as a potentially favorable period for accumulating digital assets.

Seasonal optimism often attracts renewed buying interest as investors anticipate a repeat of previous recoveries. However, experienced market participants understand that history provides context rather than certainty. Every market cycle is shaped by a unique combination of macroeconomic conditions, liquidity, investor psychology, and on-chain activity.

This year, the optimism surrounding July is being tempered by a key on-chain indicator from CryptoQuant. The firm’s Bull Score currently stands at just 20, significantly below the threshold of 60 that it considers necessary to support a sustained bullish rally.

The metric combines several indicators designed to measure the overall strength of Bitcoin’s market structure, including network activity, demand, liquidity, and investor behavior. A Bull Score of 20 suggests that the market lacks the broad participation typically associated with powerful upward trends.

While prices may experience temporary rallies or short-term rebounds, the underlying fundamentals do not yet indicate the kind of widespread demand that has historically fueled major bull markets. In previous cycles, sustained price appreciation was often accompanied by stronger on-chain metrics, increased capital inflows, rising transaction activity.

The disconnect between Bitcoin’s favorable seasonal history and its current Bull Score highlights the importance of looking beyond historical averages. Markets evolve constantly, and seasonal patterns can be overridden by broader economic forces.

Factors such as global interest rate expectations, institutional investment flows, regulatory developments, and geopolitical uncertainty continue to influence cryptocurrency prices. These external variables can either reinforce or negate traditional seasonal trends.

The current environment presents both opportunity and caution. If historical patterns repeat, July could once again provide positive returns and improve market sentiment. Even modest gains could encourage sidelined investors to re-enter the market, creating momentum that supports higher prices.

However, without stronger underlying fundamentals, any rally may struggle to sustain itself over the longer term. The coming weeks will therefore be closely watched for signs that on-chain conditions are improving.

Rising exchange inflows, stronger network usage, increased whale accumulation, and higher institutional participation could all contribute to lifting CryptoQuant’s Bull Score toward the critical 60 level. Achieving that threshold would suggest that bullish momentum is becoming more fundamentally supported rather than driven solely by market optimism.

July’s impressive historical record offers reasons for cautious optimism, but past performance alone cannot guarantee future results. CryptoQuant’s subdued Bull Score serves as a reminder that strong rallies require more than favorable seasonal trends—they require robust market participation, healthy liquidity, and growing investor confidence.

Until those conditions strengthen, Bitcoin’s seasonal advantage may face a significant test.

Elon Musk’s xAI Introduces Grok 4.5 to Challenge the AI Industry’s Leading Models

By
Ojukwu Emmanuel
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0

Elon Musk’s xAI has officially released Grok 4.5, marking the latest advancement in its rapidly evolving AI lineup.

Announcing the launch, the company said via a blog post,

“Today, we’re launching Grok 4.5, SpaceXAI’s smartest model built to excel at coding, agentic tasks, and knowledge work. It’s our strongest model ever and was trained alongside Cursor”.

Grok 4.5 raises the bar for AI engineering with smarter reasoning and faster performance. The model has been trained on a broad and carefully curated dataset spanning coding, science, engineering, and mathematics, positioning it as one of the most capable AI models for tackling complex technical challenges.

Designed with both intelligence and efficiency in mind, the model demonstrates strong performance across real-world engineering tasks, surpassing many comparable leading AI systems in these domains.

Grok 4.5 was developed using tens of thousands of NVIDIA GB300 GPUs, supported by advanced training and stability techniques tailored for large-scale AI development.

Rather than relying solely on massive token volumes, the development process placed significant emphasis on data quality. Extensive filtering, deduplication, quality scoring, and domain-specific curation ensured that the training data remained comprehensive, relevant, and high in signal.

A key differentiator of Grok 4.5 is its reinforcement learning (RL) framework, which prioritizes per-token intelligence. The model underwent training across hundreds of thousands of tasks, with a strong emphasis on multi-step software engineering, complex technical reasoning, and other engineering-focused workflows.

These tasks were evaluated using automated and model-based grading systems, enabling continuous refinement of reasoning capabilities.

The training infrastructure also supports highly asynchronous learning, allowing agentic rollouts to run for extended periods while training simultaneously continues across tens of thousands of GPUs.

This approach has resulted in more intelligent, efficient, and reliable reasoning for software development and other complex engineering applications.

Grok 4.5 also stands out for its coding capabilities. From solving advanced Rust and C/C++ programming challenges to building complete applications from a single prompt, the model consistently demonstrates the ability to generate production-ready software with minimal user specification.

Its end-to-end development capabilities enable users to move from concept to functional application quickly and efficiently. Performance has also been optimized for speed. Grok 4.5 operates at approximately 80 tokens per second (TPS) while delivering roughly twice the token efficiency of competing leading models on comparable tasks.

This combination enables faster responses, lower computational costs, and more efficient execution of demanding workloads.

The model now serves as the default engine behind Grok Build, where its capabilities extend beyond software development. It can create sophisticated Excel workbooks that incorporate web-based research, multi-sheet formulas, and embedded notes for future reference.

In addition, Grok 4.5 can produce polished Microsoft PowerPoint presentations using native shapes to design complex diagrams and layouts, while also generating well-structured, professional documents in Microsoft Word.

By combining advanced reasoning, large-scale training, efficient inference, and versatile productivity features, Grok 4.5 represents a significant advancement in AI-powered engineering, software development, and workplace productivity.

The new model, which recently completed private beta testing at SpaceX and Tesla, is now available to users and promises significant improvements in reasoning, coding, speed, and overall performance.

Musk and the xAI team position it as a direct competitor to leading models like Anthropic’s Claude Opus, with claims of matching or exceeding performance in key benchmarks while offering better token efficiency and lower costs.

Industry observers note the accelerated pace of development at xAI, which continues to leverage unique datasets and computational resources from Musk’s ecosystem of companies.

The launch comes amid intense competition in the AI sector, with xAI pushing for frequent updates and rapid iteration.

Asian Investors Grow More Selective on AI, Shifting Focus From Hype to Long-Term Winners

By
Samuel Nwite
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0

Artificial intelligence remains the dominant investment theme in global markets, but some of Asia’s largest investors are becoming increasingly selective as soaring valuations, record infrastructure spending and uncertainty over future returns prompt a reassessment of where the biggest opportunities lie.

Rather than chasing every company linked to AI, institutional investors are increasingly positioning portfolios around businesses they believe can either withstand AI-driven disruption or benefit from the technology’s expansion without depending on uncertain breakthroughs in AI applications.

The shift in sentiment was evident at the Reuters NEXT Asia conference in Singapore, where senior executives from some of the region’s largest investment firms said the next phase of the AI investment cycle will require greater discipline as markets begin asking tougher questions about valuations and profitability.

For much of the past two years, global equity markets have been propelled by enthusiasm surrounding artificial intelligence. Technology companies developing AI models, semiconductor manufacturers, cloud computing providers, and data center operators have all benefited from an unprecedented wave of investment.

That rally has lifted stock markets to record highs, but investors are increasingly debating whether corporate earnings can continue expanding fast enough to justify current valuations and whether the trillions of dollars being committed to AI infrastructure will ultimately generate attractive returns.

Rohit Sipahimalani, Chief Investment Officer of Singapore state investment company Temasek, said investors cannot afford to focus solely on companies building AI technology.

“You want to ride that trend,” Sipahimalani said during an interview at the Reuters NEXT Asia event.

“But the equally big issue is disruption because of AI to many other businesses.”

He explained that Temasek has increased its investments in businesses backed by tangible assets, arguing those companies are less vulnerable to disruption from rapid advances in artificial intelligence.

“We’ve increased our exposure to businesses that are more around hard assets, which are likely to be less disrupted by AI,” he said.

Temasek already holds stakes in AI companies, including OpenAI and Anthropic, and announced this week that it intends to increase its exposure to artificial intelligence significantly. The sovereign investment company plans to raise AI-related investments to as much as 15% of its portfolio over the next five years, up from approximately 6% today.

Even with that ambitious expansion, Sipahimalani said the investment approach will remain diversified.

“You’ve got to look at the entire value chain,” he said.

“There are some areas where there’s froth, the other areas where there’s real cash flows.”

“We try to play across the entire spectrum.”

This underscores a growing distinction within financial markets between companies benefiting from genuine commercial demand and those whose valuations have been driven primarily by investor enthusiasm. That distinction is becoming increasingly important as AI-related stocks experience sharper swings in share prices.

Investors have repeatedly questioned whether the rapid appreciation of AI companies and semiconductor manufacturers risks creating another speculative bubble similar to previous technology booms. Instead of attempting to predict which AI applications will ultimately dominate the market, some investors are choosing a simpler strategy.

Stephanie Hui, Head of Private and Growth Equity for Asia-Pacific at Goldman Sachs Asset Management, said her firm is concentrating on the infrastructure supporting AI rather than the applications themselves.

“I am not smart enough to tell you today which applications are going to be winning, it’s way too early,” Hui said during a panel discussion.

Goldman Sachs Asset Management has invested in businesses that supply the underlying infrastructure required for AI deployment, including companies specializing in liquid cooling systems and data centers, rather than betting on individual AI software companies.

As AI models become more powerful, they require more energy-intensive computing infrastructure. Advanced liquid cooling technologies are becoming essential for preventing overheating in densely packed AI servers, while new data centers continue to be built to accommodate rising computational demand.

“We are not going for the front end at this moment,” Hui said.

“We are going for the simple stuff that facilitates an end proxy for AI adoption.”

The strategy points to what many investors describe as a “picks and shovels” approach, borrowing from the California gold rush, where suppliers of essential equipment often generated more consistent returns than miners searching for gold.

Investors hope to benefit regardless of which companies ultimately emerge as long-term winners by investing in infrastructure providers rather than AI application developers.

Even among supporters of artificial intelligence, concerns about valuation are becoming more prominent.

Fred Hu, Chairman of Chinese investment firm Primavera Capital Group, said he remains convinced that AI will reshape industries but warned against excessive optimism in financial markets.

“I’m a big believer in the AI revolution but as valuations keep going up, as more and more capital goes into AI… it begs the question, how much is enough,” Hu said.

There has been growing unease that investor enthusiasm may be running ahead of commercial reality. Technology companies have announced hundreds of billions of dollars in spending on AI infrastructure, including data centers, advanced semiconductors and networking equipment.

While those investments have driven strong earnings for companies supplying AI hardware, investors now want evidence that businesses deploying AI can generate sustainable revenue growth sufficient to justify those enormous capital expenditures.

Satoshi Ueyama of Bain Capital Japan said the investment opportunities remain significant but stressed that infrastructure spending alone cannot sustain the industry’s momentum. For AI investments to generate attractive returns, businesses must ultimately create products and services that customers are willing to pay for.

“There were ample investment opportunities,” Ueyama said, but he cautioned that AI infrastructure requires end-users if the economics are to make sense.

His firm’s strategy is therefore focused on identifying companies capable of using AI to improve products and services in sectors such as consumer applications and business services.

“AI is real but at the same time there’s no denying some parts of the markets are over-excited,” Ueyama said.

“Not all AI investment is going to be successful at this stage.”

Being a major institutional investor suggests that the AI investment narrative is entering a more mature phase. During the early stages of the generative AI boom, investors largely rewarded companies simply for announcing AI strategies or increasing spending on AI infrastructure.

Today, attention is shifting toward more fundamental questions about business models, profitability, and long-term returns.

Rather than abandoning artificial intelligence, investors appear to be refining their strategies, seeking exposure across the AI value chain while avoiding areas where valuations have become detached from underlying cash flows.

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