NVIDIA Q1 Earnings Outlook and Market Expectations

NVIDIA’s Q1 2027 earnings are projected to be around $78.7 billion to $79.1 billion in revenue and $1.70 to $1.78 in earnings per share (EPS). This represents an approximate 78-79% increase in revenue and a 121% surge in EPS year-over-year. Wall Street experts believe there’s a high probability that NVIDIA will outperform these projections, citing the company’s consistent history of exceeding market expectations. The fact that such impressive results are anticipated despite virtually ‘zero’ sales in China underscores NVIDIA’s unparalleled market dominance. Hyperscalers’ massive AI infrastructure investment plans are also expected to positively impact NVIDIA’s performance.

Strengthening AI Semiconductor Market Dominance and Key Watch Points

NVIDIA’s earnings release is significant not just for its revenue and profit figures, but because it acts as a barometer for overall demand and investment trends in the AI semiconductor market. The key watch points for this earnings release are as follows:

• Blackwell Shipment Expansion and Data Center Revenue Growth: The initial shipments of the next-generation GPU platform, Blackwell, and the continued growth of the data center segment are crucial.
• Status of AI Chip Supply for China: Any mention regarding the supply of AI chips in the Chinese market amidst US government regulations will be closely watched.
• Next-generation Rubin Platform Schedule: The development and launch schedule of the Rubin platform, NVIDIA’s future growth engine, is attracting investor interest.
• CEO Jensen Huang’s Remarks on AI Infrastructure Investment: CEO Jensen Huang recently stated that AI infrastructure construction is still in its early stages and that AI adoption is rapidly expanding into the enterprise sector. His remarks will provide important clues for the mid-to-long-term outlook of the AI market.

Future Outlook and Investor Perspectives

The most significant factor influencing NVIDIA’s stock price after the earnings release will be the future performance outlook (guidance). Analysts project NVIDIA’s annual revenue to surge by 72% this year, reaching $372 billion, and there’s a possibility that the company’s guidance could exceed this. In particular, the approval of H200 chip sales in China and plans for the launch of Vera CPU, a new business area, are cited as factors that could accelerate NVIDIA’s growth trajectory. However, some analysts suggest that even if NVIDIA announces strong results, it might be challenging to revive the AI rally given the overall market burden from high oil prices and inflationary pressures. Ultimately, this earnings release will provide clear answers regarding the sustained fervor of the AI semiconductor market and market expectations.

NVIDIA’s earnings release will be a crucial milestone, showcasing not just the performance of an individual company, but the present and future of the vast AI industry. As the world watches with bated breath, attention will be on what message NVIDIA delivers.

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As data centers are being labeled not just ‘power-hungry behemoths’ but also ‘major culprits of environmental problems,’ concerns about sustainability are deepening as much as the pace of technological development. How will this massive digital infrastructure navigate these challenges? Let’s delve into the real reasons and solutions now.

The AI Era: Data Centers Become Power-Hungry Behemoths

The explosive growth of the artificial intelligence industry has driven data center power consumption to unimaginable levels. As demand for large language models (LLMs) and generative AI training surges, the density and power consumption of GPUs and AI accelerators have become incomparable to the past. In such an environment, the heat density generated by a single server is also enormous. The proportion of total U.S. electricity consumption accounted for by data centers surged from 1.9% in 2018 to 4.4% in 2023, with projections indicating it could skyrocket to a staggering 12% by 2028.

The problem goes beyond simply high power consumption. AI data centers exhibit unusual power consumption patterns. This has led to analyses suggesting that the U.S. power grid might not be able to cope, potentially leading to large-scale blackouts. Indeed, ultra-large data centers like OpenAI’s ‘Stargate1’ are reportedly expected to consume over 1 gigawatt (GW) of power. Perhaps for this reason, power shortages are now identified as the biggest bottleneck in data center construction. The impact of this surge in power demand is as follows:

• Rapid increase in GPU and AI accelerator power consumption due to increased AI workloads
• Growing proportion of data center power consumption in total national electricity consumption
• Increased power grid instability due to abnormal power consumption patterns
• Potential for large-scale blackouts due to the construction of gigawatt (GW)-class data centers

Environmental Regulations and Public Opposition: An Unavoidable Reality

As the perception of data centers as ‘power-hungry behemoths’ spreads, local residents’ opposition and environmental regulations have become an unavoidable reality. Specifically, there have been cases in the Silicon Valley area where data center construction was abruptly halted due to power and water shortages. The Oakley City Council in California unanimously approved a 45-day moratorium on accepting and reviewing land use applications related to data centers, a measure that could be extended for up to two years. Strong public opposition to the massive electricity and water consumption caused by large-scale data centers is the background for this decision.

Across the U.S., at least 48 data center projects were reportedly halted or delayed in 2025 alone due to local public opposition. While data centers appear to be quiet buildings on the outside, they are, in reality, closer to industrial facilities that consume massive amounts of power and water. Some facilities use millions of liters of water per day. A power company official explained that when a single data center is built, the regional power demand structure itself changes. The main reasons for local residents’ opposition are as follows:

• Concerns about resource depletion due to massive electricity and water consumption
• Environmental debates over power grid expansion and new power generation facility construction
• Worsening shortages of agricultural and domestic water in drought-prone areas
• Controversy over massive tax incentives compared to the number of jobs provided

Technological Innovation and Challenges for Sustainability

The power shortages and environmental problems surrounding data centers are ultimately challenges that must be solved through technological innovation. In particular, highly efficient and reliable thermal management strategies have emerged as key to responding to the ultra-high heat generation environment caused by increasing AI workloads. Beyond air cooling systems, the spread of liquid cooling is accelerating. The adoption of cold plate-based liquid cooling systems is rapidly increasing, especially for high-power GPU servers, and some large data centers are even implementing immersion cooling. Liquid is much more efficient at heat transfer than air, making it a more effective alternative in high power density environments.

Furthermore, as improving power usage effectiveness (PUE) becomes a critical task in data center operations, thermal design is also being approached from an energy-saving perspective. Beyond simply lowering temperatures, optimizing structural design to maintain maximum performance for the same power consumption is emerging as a key factor. The accelerating transition to ‘AI-native data centers’ is also noteworthy. All data center functions, from workload placement to cable diagnostics, will be AI-assisted, and energy efficiency management and power cost negotiation are expected to be automated. These efforts are essential for increasing the sustainability of data centers.

• Expansion of liquid cooling technologies (e.g., liquid cooling, immersion cooling) to address high-density heat generation
• Transition to energy-efficiency-centric design to improve Power Usage Effectiveness (PUE)
• Energy efficiency management and optimization through AI-based autonomous operating systems
• Strengthening integrated thermal design at the system level and applying thermal simulations

Data centers are essential infrastructure for our era. While facing the enormous challenges of power shortages and environmental issues, efforts for technological innovation and sustainable operation will continue. Ultimately, evolving into more efficient and eco-friendly data centers by turning this crisis into an opportunity is the path we must take.

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In particular, the major update announced on April 17, 2026, was truly game-changing for software development. Not only does it automate coding tasks, but it can even generate images. I was quite surprised at first. It makes me think that existing development methods could be completely shaken up. Today, let’s honestly explore how Codex is evolving and what impact it will have on our development ecosystem.

Codex Beyond Coding Assistance: Now It Controls Computers?

To be honest, when Codex first came out, I thought, ‘Oh, it’s just a tool to make coding a bit easier.’ But my perspective completely changed after seeing the March and April 2026 updates. Now, Codex isn’t just writing code; it’s become an AI agent that operates directly on the user’s computer, utilizing various applications.

• Enhanced Desktop App Support: Expanding desktop app support, starting with macOS and extending to Windows, allows developers to integrate Codex more deeply into their actual work environments.
• Background Task Processing: It can handle repetitive code modifications or test executions, resume interrupted tasks, and even manage long-term projects spanning several days. The era where AI does human work has arrived.
• Automation of the Entire Development Workflow: With Codex automating or assisting with overall development processes like code writing, modification, test execution, and responding to code reviews, many predict a significant boost in developers’ work efficiency.

‘Shock and Awe’ Among Developers: What’s Behind It?

Many say that this radical evolution of Codex has been met with ‘shock and awe’ in the developer community. In particular, there were articles in February 2026 about a significant drop in software company stock prices and increased job insecurity following the release of the Codex app. This, combined with OpenAI CEO Sam Altman’s ‘helplessness’ remark, further fueled developers’ outrage. Critics argued that it was hypocritical to express helplessness after releasing technology that threatens developers’ jobs.

In fact, there’s a good reason for these concerns. Since March 2026, GPT-5.4 has become the primary model for Codex, leading to a massive improvement in performance. With GPT-5.4 mini also being used as a supplementary model, Codex now appears less like a mere coding tool and more like a system that systematically organizes agent tasks. These changes are bound to cause significant ripples across the entire software industry.

The Future Codex Envisions: The Dawn of the ‘Super App’ Era?

As Codex evolves beyond simple coding to an AI agent that directly controls computers, the future of software development seems poised to enter an entirely new phase. There are a few particularly noteworthy features:

• Image Generation and Utilization: By leveraging the GPT-Image-1.5 model to generate and modify images, visual tasks can be integrally performed across various development stages, including UI design, product concepts, and game creation.
• Powerful Collaboration Features: The ability to understand the context of collaboration tools like Slack, Gmail, and Notion, track tasks, and suggest follow-up actions will significantly boost the efficiency of team-based development. It can even review GitHub comments and continue modification tasks.
• Agent SDK Provision: Providing an SDK that allows developers to build and operate Codex-based AI agents directly is also key. This demonstrates Codex’s intent to expand beyond a mere tool into a platform.

Looking at these features, it’s predicted that Codex will not just be a coding agent but the center of a massive AI ecosystem, offering various functions bundled together, much like a ‘super app’ on a smartphone. In fact, within OpenAI, Codex-based AI agents are reportedly being used to autonomously operate data platforms, debug failures, and manage releases. At this point, it’s fair to consider it a core future technology.

These changes in Codex will present both new challenges and opportunities for developers. It will require thinking beyond just writing good code to how to effectively utilize and instruct AI agents to create greater value. I’m truly excited about the software world that Codex will create in the future.

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What’s the big deal about ‘Constitutional AI’?

Honestly, the term ‘Constitutional AI’ was a bit unfamiliar at first. But it turns out this is the core that defines Anthropic’s identity. While other AI companies are focused on performance competition, this company puts immense effort into making AI operate safely and beneficially for humans. Simply put, it’s about embedding a kind of guideline that allows AI to judge ‘right and wrong’ on its own, which is truly not an easy task.

• AI Ethics and Safety First: They focus on creating AI that aligns with human values, beyond just being smart.
• Self-Improvement Mechanism: AI is designed to autonomously reject harmful responses and learn in a better direction.
• Long-term Trust Building: This is an approach to build long-term AI stability and trust rather than short-term performance.

Thanks to this approach, Anthropic is firmly establishing its image as ‘safe AI’. As AI becomes smarter, concerns about ethical issues and potential misuse also grow, right? In that sense, their actions are quite significant.

Claude 3 Leads the Era of General-Purpose AI

When it comes to Anthropic, you can’t leave out the Claude models. In particular, the Claude 3 model released last year was truly ‘insane’ in terms of performance. It surpassed the limitations of previous models, showing remarkable advancements in complex reasoning capabilities and multilingual processing. Many experts have even evaluated Claude 3 as a step closer to Artificial General Intelligence (AGI).

• Excellent Language Understanding: It can grasp complex contexts and nuances, enabling natural conversations.
• Processing Various Data Formats: It understands and analyzes various forms of information, including text and images.
• Fast Response Speed: It boasts fast processing speed, significantly improving user experience.

Thanks to this model, Anthropic has gained quite strong competitiveness in the enterprise solution market. It has reached a level where it can perform complex tasks in real business environments, beyond just answering questions. Honestly, I think this is a level of technology that deserves ‘king-level’ recognition.

Partnerships and Ecosystem Expansion, the Strategy of a Quiet Giant

Another important strategy for Anthropic is strong partnerships and ecosystem expansion. Rather than trying to do everything alone, they are spreading their technology to wider areas through strategic collaborations. In particular, partnering with giants like Amazon and Google was truly a stroke of genius. They have been able to further advance the Claude model and reach more users by leveraging the vast resources and infrastructure of these companies.

• Cloud Platform Integration: They integrate the Claude model into major cloud platforms like Amazon Web Services (AWS) and Google Cloud to increase accessibility.
• Startup and Developer Support: They support various startups and developers to create innovative services using the Claude API.
• Industry-Specific Solution Development: They are developing AI solutions optimized for specific industries such as finance and healthcare with partner companies.

These partnerships will greatly help Anthropic grow rapidly while building a stable foundation. Especially in 2026, as these collaborative relationships become even stronger, Anthropic’s influence across the entire AI ecosystem will grow even further.
Anthropic is not just competing on technology; it’s taking a balanced approach that considers the fundamental safety and ethics of AI while not neglecting practical performance. It will be fascinating to watch what new milestones they set in the AI market in 2026. Personally, I believe their actions will offer important implications for the future direction of AI technology.

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Exactly 10 years later, in 2026, we are living amidst the questions and changes AlphaGo brought forth. Shall we frankly discuss how the events on the Go board back then impacted the future of humanity beyond a simple game, and how far AI has come today?

The Match of the Century: AlphaGo and Lee Sedol 9-dan

Honestly, everyone was surprised watching that match, weren’t they? The very idea of artificial intelligence beating a human in Go was hard to imagine. AlphaGo had already won against Fan Hui 2-dan in 2015, but the match against Lee Sedol 9-dan truly captivated the attention of people worldwide. In particular, the 4th game, where Lee Sedol 9-dan achieved his sole victory, is still talked about today for its ‘divine move’.

This match went beyond the rules and strategies of the game of Go, prompting deep reflection on the potential of artificial intelligence. Many believed that AI could not mimic human intuition and creativity, but AlphaGo made unexpected moves, shaking up the conventional wisdom of the Go world.

• AlphaGo’s Groundbreaking Victory: In March 2016, it defeated Lee Sedol 9-dan 4-1, impressing the world with the capabilities of artificial intelligence.
• Lee Sedol 9-dan’s ‘Divine Move’: Lee Sedol 9-dan’s 78th move in the 4th game is regarded as a move that induced an error in AlphaGo, remembered as a masterpiece demonstrating human creativity.
• Paradigm Shift in the Go World: After AlphaGo’s appearance, professional Go players began to research new joseki and fuseki through AI, completely changing the way Go is studied.

AlphaGo’s Evolution and the Advancement of AI Technology

AlphaGo was not just a project for Go. The DeepMind development team developed this technology with the goal of a single artificial intelligence that could be applied to all IT services. Even after the match with Lee Sedol 9-dan, AlphaGo continued to evolve. ‘AlphaGo Zero’ emerged, becoming even more powerful by learning purely from self-play without human game records, and then ‘AlphaZero’, a general-purpose AI that mastered not only Go but also chess, shogi, and all two-player perfect information games.

The core of this evolution lies in technologies like deep learning and reinforcement learning. AlphaGo improved its skill by learning from vast amounts of data and finding optimal moves through self-play. Initially, it used GPUs, but later, Google’s self-developed TPUs (Tensor Processing Units) enabled more efficient computation.

• AlphaGo’s Core Technology: Combines trained deep neural networks (DNN) with Monte Carlo tree search (MCTS) to find optimal moves.
• Evolved Learning Methods: Initially, it used both supervised learning and reinforcement learning, but subsequent models like AlphaGo Master and Zero improved their skill solely through reinforcement learning without supervised learning.
• Diverse Hardware Utilization: Initially used CPUs and GPUs, and later utilized TPUs specialized for deep learning to maximize computational efficiency.

2026: The Significance of 10 Years After AlphaGo

In 2026, the 10th anniversary of the AlphaGo vs. Lee Sedol 9-dan match, artificial intelligence has become such a natural part of our lives. In the Go world, AI acts as a ‘teacher’, proposing new fuseki and strategies, and Lee Sedol 9-dan even participated in an experiment to design ‘future Go’ with AI again after 10 years. Lee Sedol 9-dan now goes so far as to say, “AI is simply a god.”

AlphaGo has retired from Go, but its technology has expanded into various fields such as drug discovery, climate change prediction, autonomous vehicles, and medical diagnosis. In particular, in 2026, expectations for ‘physical AI’ with physical forms like autonomous cars and robots are growing. The possibilities demonstrated by AlphaGo continue to inspire the journey towards artificial general intelligence (AGI). Isn’t it truly amazing that the innovation that began on a Go board is now contributing to solving complex problems in the real world?

AlphaGo was more than just a program that played Go well; it posed fundamental questions about how far artificial intelligence can develop and how humans and AI can coexist. 10 years later, we are in the process of finding answers to those questions, and it seems inevitable that we are even more excited about the future that AI will create.

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