Jeff Dean, Chief Scientist at Google DeepMind and one of the original architects behind Google’s infrastructure, has long stood at the intersection of computational scale and sustainability. As artificial intelligence becomes the defining layer of modern computing—from search engines and voice assistants to logistics optimization and health diagnostics—the infrastructure that powers these systems must evolve in both capability and conscience. Dean’s decades-long obsession with performance optimization has become more than an engineering ethos; it’s a global playbook for how enterprise and public sector stakeholders can scale intelligently while remaining cost-effective and environmentally responsible.
This blog explores Dean’s leadership in building efficient, planet-scale AI systems. From pioneering the creation of TPUs to orchestrating hyperscale job scheduling and integrating sustainability into the research-to-deployment pipeline, Dean’s philosophy of “planetary scale responsibility” offers a framework for the future of AI infrastructure. Whether you’re managing generative models at scale, overseeing government R&D initiatives, or budgeting for long-term AI investments, Dean’s work delivers technical insight and strategic clarity.
5 Reasons Tech Leaders Should Study Jeff Dean’s Infrastructure Philosophy
For CTOs, heads of engineering, or public policy advisors in AI, Dean’s legacy is a strategic lodestar. Here are key reasons his approach matters:
- Balances massive computational needs with measurable sustainability goals
- Unlocks cost-efficiencies through custom silicon and dynamic job routing
- Reinforces ethical and transparent AI development through lifecycle-aware design
- Provides a roadmap for future-ready, AI-first infrastructure strategies
By integrating these principles, organizations can move beyond hype cycles and toward durable, resilient AI platforms. The AI race is no longer just about speed—it’s about sustainability, accountability, and systems leadership.
Dean’s career serves not only as a case study in AI leadership, but as a call to action for those designing the digital infrastructure of tomorrow. At planetary scale, every watt, every microsecond, and every model iteration counts—and no one has made that case more effectively than Jeff Dean. Dean’s career serves not only as a case study in AI leadership, but as a call to action for those designing the digital infrastructure of tomorrow.
1. The Scale of Google’s Compute Footprint
Google’s compute infrastructure is among the largest and most sophisticated in the world. Every day, billions of search queries, map lookups, translation requests, and video recommendations are powered by a hyper-distributed, meticulously optimized backend. This infrastructure doesn’t just serve consumers—it trains massive multimodal models, hosts business-critical applications, and provides compute for real-time inference pipelines at an unprecedented global scale. With models like PaLM, Gemini, and future iterations stretching beyond trillions of parameters, the required training runs now operate at the level of exaflop-scale compute—representing some of the largest coordinated computational efforts ever executed.
Jeff Dean, from his earliest work on Google’s indexing systems to his leadership of Brain and DeepMind, has consistently emphasized that scale alone is not the end goal. What matters is “efficiency per unit of compute.” It’s not just about how many servers are active—it’s about how much useful work each of them performs per watt, per cycle, and per dollar.
This principle proved critical during a pivotal moment in Google’s history. In 2000, a major outage forced Dean and Sanjay Ghemawat to rethink the indexing architecture from the ground up. Their solution laid the groundwork for MapReduce, Bigtable, and Spanner—systems that defined distributed computing for the next two decades. By designing modular and resilient systems that could self-heal and scale linearly, they enabled Google to grow without sacrificing responsiveness or efficiency.
As AI workloads became dominant, Google’s infrastructure evolved once again. Today, its global fleet of data centers represents a distributed mesh of intelligent facilities that operate as a single, unified compute fabric. These are not mere warehouses filled with racks. They are living, learning systems optimized through AI-driven feedback loops. Each data center contributes to:
- Specialized AI accelerators (TPUs) purpose-built for matrix operations
- Real-time job routing based on latency, bandwidth, and energy availability
- Load balancing informed by temperature, regional energy costs, and sustainability goals
- Continuous feedback loops measuring throughput, model failure rates, and hardware degradation
These capabilities transform Google’s data centers into adaptive organisms—able to ingest workloads from around the world, make intelligent decisions about resource allocation, and execute ML tasks at previously unimaginable speeds and scales.
Key Infrastructure Takeaways: How Google Built a Planetary-Scale Backbone
Understanding the magnitude and precision of Google’s AI infrastructure provides critical insights for tech leaders and policymakers:
- Google’s global compute network runs on coordinated AI agents managing task allocation
- TPUs deliver unmatched performance per watt and per dollar in large-scale ML
- Model training is shifted in real-time to locations with the lowest carbon footprint
- Data centers operate as intelligent entities, optimizing based on energy grids, weather, and latency
Google’s engineering philosophy, led by Dean, treats infrastructure not as a fixed asset but as an evolving system. When viewed through this systems lens, the data center becomes less about hardware and more about orchestration—about building a responsive, intelligent layer between silicon and software that adapts in real-time to the needs of users and the planet alike.
This approach holds enormous implications for other enterprises, governments, and national labs looking to scale AI responsibly. The roadmap Dean helped define isn’t proprietary—it’s principled. It’s grounded in systems thinking, energy efficiency, ethical AI, and operational clarity. For those leading digital transformation at scale, Dean’s compute footprint isn’t just a benchmark—it’s a directional compass.
Google’s compute infrastructure is among the largest and most sophisticated in the world. Every day, billions of search queries, map lookups, translation requests, and video recommendations are powered by a hyper-distributed, meticulously optimized backend. This infrastructure doesn’t just serve consumers—it trains massive multimodal models, hosts business-critical applications, and provides compute for real-time inference pipelines at an unprecedented global scale. With models like PaLM, Gemini, and future iterations stretching beyond trillions of parameters, the required training runs now operate at the level of exaflop-scale compute—representing some of the largest coordinated computational efforts ever executed.
Jeff Dean, from his earliest work on Google’s indexing systems to his leadership of Brain and DeepMind, has consistently emphasized that scale alone is not the end goal. What matters is “efficiency per unit of compute.” It’s not just about how many servers are active—it’s about how much useful work each of them performs per watt, per cycle, and per dollar.
This principle proved critical during a pivotal moment in Google’s history. In 2000, a major outage forced Dean and Sanjay Ghemawat to rethink the indexing architecture from the ground up. Their solution laid the groundwork for MapReduce, Bigtable, and Spanner—systems that defined distributed computing for the next two decades. By designing modular and resilient systems that could self-heal and scale linearly, they enabled Google to grow without sacrificing responsiveness or efficiency.
As AI workloads became dominant, Google’s infrastructure evolved once again. Today, its global fleet of data centers represents a distributed mesh of intelligent facilities that operate as a single, unified compute fabric. These are not mere warehouses filled with racks. They are living, learning systems optimized through AI-driven feedback loops. Each data center contributes to:
- Specialized AI accelerators (TPUs) purpose-built for matrix operations
- Real-time job routing based on latency, bandwidth, and energy availability
- Load balancing informed by temperature, regional energy costs, and sustainability goals
- Continuous feedback loops measuring throughput, model failure rates, and hardware degradation
These capabilities transform Google’s data centers into adaptive organisms—able to ingest workloads from around the world, make intelligent decisions about resource allocation, and execute ML tasks at previously unimaginable speeds and scales.
Key Infrastructure Takeaways: How Google Built a Planetary-Scale Backbone
Understanding the magnitude and precision of Google’s AI infrastructure provides critical insights for tech leaders and policymakers:
- Google’s global compute network runs on coordinated AI agents managing task allocation
- TPUs deliver unmatched performance per watt and per dollar in large-scale ML
- Model training is shifted in real-time to locations with the lowest carbon footprint
- Data centers operate as intelligent entities, optimizing based on energy grids, weather, and latency
Google’s engineering philosophy, led by Dean, treats infrastructure not as a fixed asset but as an evolving system. When viewed through this systems lens, the data center becomes less about hardware and more about orchestration—about building a responsive, intelligent layer between silicon and software that adapts in real-time to the needs of users and the planet alike.
This approach holds enormous implications for other enterprises, governments, and national labs looking to scale AI responsibly. The roadmap Dean helped define isn’t proprietary—it’s principled. It’s grounded in systems thinking, energy efficiency, ethical AI, and operational clarity. For those leading digital transformation at scale, Dean’s compute footprint isn’t just a benchmark—it’s a directional compass.
2. Innovations in Efficiency: TPUs, Scheduling, and Model Optimization
One of Jeff Dean’s most profound and enduring contributions to modern computing is his insistence that innovation cannot occur in isolation. Hardware, software, and system design must evolve as a cohesive whole. In the era of planetary-scale AI, where every model training run carries financial, environmental, and operational implications, Dean’s philosophy has shifted the industry’s priorities. This ethos materialized in the creation of Tensor Processing Units (TPUs), advances in hyperscale job scheduling, and transformative approaches to model optimization. Each of these pillars plays a role in enabling scalable, sustainable, and accessible AI infrastructure.
TPUs: Hardware Co-designed for AI
In 2015, Google introduced its first Tensor Processing Unit (TPU)—a new class of application-specific integrated circuits (ASICs) optimized exclusively for machine learning workloads. Unlike general-purpose CPUs or repurposed GPUs, TPUs were purpose-built from the ground up to handle tensor operations efficiently, with low latency and high throughput.
Jeff Dean recognized early on that the increasing scale of neural networks would outpace traditional computing architectures. He proposed co-designing hardware and software together to unlock unprecedented performance gains. TPUs would not only accelerate training and inference; they would reshape the economics of artificial intelligence itself.
- TPU v4 delivers over 100x performance per watt compared to legacy ML hardware, driving massive reductions in power consumption.
- Their architecture includes high-speed interconnects and optimized memory access, allowing models to scale horizontally across “TPU pods”.
- TensorFlow, Google’s open-source ML framework, was customized to align with TPU operations, ensuring tight software-hardware synergy.
As of 2024, TPU clusters have powered Google’s most demanding models—including Gemini and Pathways—at global scale. These breakthroughs were not the result of incremental silicon gains but rather holistic infrastructure rethinking: from compiler to chip, every layer was tuned for ML.
Scheduling at Hyperscale
But compute efficiency isn’t solely about chips—it’s also about how jobs are routed. Under Dean’s direction, Google radically reengineered job scheduling at planetary scale, enabling real-time allocation of AI workloads across continents.
Rather than rely on static rules or pre-assigned capacity, Google’s job schedulers use dynamic, machine-learned algorithms that factor in:
- Data center energy mix (e.g., prioritizing sites running on solar or wind)
- Regional temperature patterns (cooler climates reduce cooling needs)
- Bandwidth congestion and network latency (minimizing packet loss and delivery delay)
- Hardware utilization and thermal thresholds (avoiding throttling or wear-and-tear)
This orchestration layer acts like a digital nervous system. Jobs are continuously re-evaluated and redistributed to maximize both performance and sustainability. It’s not uncommon for massive LLM training jobs to “follow the sun,” migrating to locations where conditions are most favorable.
The use of reinforcement learning models to make these decisions in real time reflects Dean’s belief that performance optimization is itself a problem best solved by AI. The result is a feedback-rich scheduling system that evolves continuously, learning from every run to improve future allocations.
Model Optimization
While infrastructure innovations grabbed headlines, Dean quietly led equally transformative work on making the models themselves leaner, faster, and more efficient.
- Sparse activation techniques reduce the number of active weights in a neural network during inference, cutting compute without sacrificing accuracy.
- Weight-sharing and low-rank matrix factorization reduce redundant computations, especially in vision and language models.
- Model pruning, quantization, and distillation shrink model size and memory footprint, enabling real-time deployment even on edge devices.
This efficiency-first mindset allows production models like PaLM 2 and Gemini to outperform larger, more bloated counterparts. By training smarter—not just harder—Dean’s teams deliver high performance with dramatically lower infrastructure costs.
His approach also emphasizes lifecycle design: models are built with maintainability in mind, enabling modular updates, reusability of embeddings, and continual fine-tuning instead of re-training from scratch. This turns what would be compute-expensive retraining into an agile, sustainable pipeline.
Why It Matters: The Business and Environmental Case for Integrated Efficiency
In today’s AI-driven economy, optimization is no longer a backend concern—it is a strategic differentiator. Jeff Dean’s cross-disciplinary innovations prove that efficiency and innovation are not mutually exclusive. They compound.
Takeaways for Enterprise and Public Sector Leaders:
- TPUs set the new standard for energy-efficient AI compute, reducing both cost and environmental impact
- AI-driven scheduling enables real-time optimization based on renewable energy availability, latency, and hardware usage
- Efficient models extend AI capabilities to edge devices and under-resourced environments without sacrificing power
- Integrated hardware-software co-design is essential for staying competitive in LLM development and deployment
By viewing AI infrastructure as a layered ecosystem, Dean redefined what scalability means. It’s no longer enough to train bigger models—leaders must now also train better, cheaper, and cleaner ones. His model of continuous co-evolution across the stack stands as a model for CTOs, policymakers, and AI architects navigating the trillion-parameter frontier.
As AI continues to demand more of our resources—data, electricity, talent—Dean’s contributions serve as a reminder that responsibility and scale must rise in parallel. Efficiency, when embedded into every layer of design, becomes a growth lever—not a limitation.
3. Sustainability Practices: Energy, Carbon, and Responsible AI
Under Jeff Dean’s leadership, sustainability evolved from a peripheral concern into a core design principle within Google’s AI and infrastructure strategy. As awareness grew around the carbon footprint of large-scale machine learning, Dean moved swiftly to define operational standards that prioritized both environmental stewardship and long-term systems viability. In a landscape where billion-parameter models were pushing boundaries—and budgets—Dean’s framework helped align technical ambition with ecological responsibility.
His core belief? Compute at planetary scale should not compromise planetary health. Instead, AI infrastructure must be designed to align with global sustainability goals, from carbon neutrality to energy transparency.
Carbon-Aware Compute
Google was among the first technology companies to introduce carbon-intelligent computing—the practice of scheduling workloads to align with the availability of renewable energy. This strategy meant that instead of running compute-intensive jobs whenever and wherever compute was available, the company began routing them to data centers during windows of low-carbon intensity.
Dean’s innovations in hyperscale job scheduling enabled this capability. Training jobs for large models could be dynamically scheduled to run in:
- Data centers powered by solar or wind, reducing dependency on fossil fuels
- Off-peak hours, when grid strain was lowest and renewable input highest
- Geographies with favorable energy profiles, where renewables dominate the local grid
These decisions were not hard-coded—they were guided by predictive models that leveraged real-time energy market data. In doing so, Dean laid the groundwork for a more sustainable AI lifecycle, where environmental context becomes an active variable in compute decision-making.
Open Metrics and Accountability
For sustainability to be taken seriously in tech, transparency must be standard practice. Dean was a key proponent of publishing AI energy usage statistics in major research papers. By embedding metrics such as carbon cost per model, training kilowatt-hours, and hardware lifecycles into peer-reviewed documentation, Dean helped normalize a new form of scientific accountability.
This transparency created a virtuous cycle:
- It informed better infrastructure decisions
- It encouraged public debate around responsible AI
- It raised the bar for academic and industry benchmarks
Today, leading AI research groups regularly disclose the environmental impact of their models—an industry norm that can be traced directly to Dean’s early advocacy.
Lifecycle Optimization
Dean’s sustainability vision wasn’t confined to model training. He championed a full-stack, lifecycle-aware approach, ensuring that efficiency was embedded from development through deployment. This philosophy includes:
- Efficient training loops, incorporating techniques like early stopping, progressive batching, and curriculum learning
- Long-lived model architectures with modular updates, reducing the need for retraining from scratch
- Deployment to edge devices, minimizing server-side compute draw and enabling low-energy inference in the field
This approach mirrors Dean’s larger systems ethos: optimize not just individual tasks, but the entire flow—from model ideation to inference. Every watt saved is a watt that compounds across thousands of training runs, hundreds of product deployments, and billions of user interactions.
Why Sustainability Is the New Competitive Edge in AI
Enterprises and governments are under increasing pressure to meet net-zero goals. AI, if unmanaged, threatens to derail those ambitions. Jeff Dean’s model of sustainable AI offers a roadmap that satisfies both regulatory expectations and market incentives.
Takeaways for Policy and Engineering Leaders:
- Carbon-aware job scheduling significantly reduces AI’s environmental footprint
- Energy transparency builds public trust and scientific credibility
- Lifecycle optimization makes models leaner, greener, and more adaptable
- Edge deployments enable AI to scale without centralizing energy burden
Dean’s work shows that sustainable AI is not a constraint—it’s an enabler. It opens new use cases, extends operational runway, and improves stakeholder confidence. More importantly, it ensures that the next decade of AI progress doesn’t come at the cost of ecological collapse.
For leaders developing infrastructure at scale—whether public clouds, academic clusters, or sovereign compute strategies—Dean’s sustainability principles are no longer optional. They are foundational.
4. ROI Examples: Performance Gains and Environmental Dividends
Efficiency at planetary scale isn’t just an engineering triumph—it’s a strategic asset. Jeff Dean’s systems-level thinking has translated not only into technical breakthroughs but also into tangible, quantifiable returns for Alphabet and the broader AI ecosystem. His innovations deliver value on multiple fronts: operational cost savings, improved speed-to-insight, reputational advantage in ESG frameworks, and unlocked bandwidth for ambitious experimentation.
These aren’t abstract benefits. They’re reflected in internal metrics, external shareholder confidence, and Google’s continued dominance in machine learning research and deployment. From silicon efficiency to carbon-aware training pipelines, the results demonstrate that performance optimization—when embedded at every layer—compounds over time into competitive advantage.
TPU Deployment: Unleashing Silicon ROI
The rollout of TPUs across Google’s infrastructure has produced dramatic returns in both cost and performance:
- ~5x reduction in model inference cost at scale compared to GPU-based infrastructure for production workloads.
- Latency reductions of 50%+ in key inference pathways, directly improving user experience in ad ranking, search response, and voice assistant interactions.
- Lower hardware depreciation rates, due to custom optimization that extends lifecycle use and improves power-to-performance ratios.
- Higher throughput per dollar, enabling research teams to run more experiments in parallel without linearly increasing compute budgets.
These benefits have cascading effects. Faster inference means better ad targeting and ranking—a direct revenue lever. Cheaper training costs free up cycles for researchers to explore more hypotheses, leading to faster model iteration. And better power performance means lower overhead in data center energy provisioning and cooling—contributing to both environmental and financial sustainability.
Sustainability-Linked Training: Turning ESG into Strategy
Efficiency also delivers non-obvious dividends—particularly around environmental, social, and governance (ESG) metrics. Dean’s carbon-aware scheduling architecture enabled the Gemini model family to be trained in ways that aligned with both environmental stewardship and public transparency:
- 30% reduction in CO₂ emissions for the full Gemini training run, compared to a similar-scale model trained without carbon-aware infrastructure.
- 7–12% lower energy bills in participating data centers by routing compute to low-demand, renewable-heavy grids in real time.
- Improved investor sentiment, as Alphabet’s sustainability metrics began surfacing in ESG evaluations from BlackRock, MSCI, and other institutional indexes.
These outcomes are particularly meaningful in a market where sustainability is now integral to brand perception and investor confidence. Dean’s work helped position Google not just as a tech leader, but as a climate-forward innovator.
Multiplier Effects: Innovation Acceleration and Resource Unlocks
What often goes unspoken in ROI calculations is the innovation acceleration effect: optimized infrastructure doesn’t just save money—it creates opportunity.
- Resource-recycling frameworks allow partially trained models to be used as backbones in other projects, avoiding redundant compute.
- Edge-optimized models, developed using lightweight distillation and quantization techniques, enable new business units (e.g., Android, Google Cloud) to launch AI services in previously unreachable markets.
- Research democratization: lower cost per experiment means more junior researchers, students, and interdisciplinary teams can prototype without permission gatekeeping.
These outcomes expand the organizational bandwidth for R&D and reduce time-to-market for AI-infused features—critical in a space where velocity equals relevance.
Why ROI-Driven AI Infrastructure Is the Future of Competitive Advantage
The future of enterprise AI will be defined by those who can do more with less—more model capacity, more users served, more accuracy delivered—on less energy, time, and cost. Jeff Dean’s work proves that responsible infrastructure doesn’t slow you down—it fuels your ability to accelerate intelligently.
Takeaways for CFOs, Strategy Leaders, and Innovation Heads:
- TPUs lower inference costs and increase model throughput, providing direct financial return
- Carbon-aware training aligns AI operations with ESG targets, enhancing brand equity and compliance
- Efficient design unlocks new markets, especially for mobile, emerging regions, and latency-sensitive use cases
- Cost savings fund deeper R&D cycles, reducing bottlenecks and driving long-term innovation capacity
For AI-native companies, the question is no longer whether infrastructure should be optimized—it’s whether it can afford not to be. Dean’s framework offers a high-leverage, low-regret path for scaling AI in the era of efficiency. His contributions are a proof point that AI, when built responsibly, doesn’t just scale—it pays for itself in dividends.
5. Strategic Implications for Enterprise and Government Leaders
Jeff Dean’s body of work transcends his internal contributions to Google—it serves as a template for how enterprises and governments should approach the modernization and governance of AI at scale. The strategic frameworks embedded in his system-level thinking present actionable blueprints for decision-makers grappling with the rapidly evolving intelligence stack. Dean doesn’t just optimize for model performance—he optimizes for institutional longevity, cost efficiency, environmental impact, and infrastructure autonomy. These principles have wide-reaching implications for national AI strategies, C-suite decision-making, and regulatory planning.
Total Cost of Intelligence (TCI)
Just as cloud computing popularized the concept of Total Cost of Ownership (TCO) to evaluate infrastructure investments holistically, Dean’s approach introduces a new metric: Total Cost of Intelligence (TCI). TCI shifts the conversation from raw model performance to operational efficiency—encouraging leaders to assess the real costs behind each prediction, insight, or decision served by an AI system.
Executives must now ask:
- What is the monetary and environmental cost of every insight we generate?
- How do model retraining cycles and fine-tuning loops impact our energy footprint and cloud bills?
- Where are we incurring hidden inefficiencies—such as redundant inference passes, poorly optimized data pipelines, or compute over-provisioning?
By introducing these questions, Dean reframes AI not as a fixed asset, but as a living, evolving service that incurs variable costs over time. This makes cost optimization, green compute strategies, and tooling decisions not just technical challenges—but boardroom priorities.
Infrastructure Sovereignty
As more nations and Fortune 500 firms contemplate deploying national LLMs, vertical-specific AI assistants, or sovereign decision platforms, Dean’s systems architecture philosophy exposes the fragility of overreliance on general-purpose AI infrastructure. The lesson: real strategic leverage comes not from renting intelligence, but from owning its physical and logical substrate.
Dean’s legacy in custom chip design (e.g., TPUs), optimized AI compilers, and modular ML stacks shows that organizations that invest in bespoke infrastructure can achieve:
- Lower inference latency and training costs at scale
- Greater alignment between hardware capabilities and algorithmic needs
- Long-term control over data security, privacy guarantees, and export restrictions
For governments especially, this raises a question of AI sovereignty. If national LLMs are running on foreign hyperscaler infrastructure, can that nation claim autonomy over its intelligence layer? Dean’s career affirms that purpose-built systems unlock national resilience—both economically and geopolitically.
AI Policy and ESG Alignment
As AI becomes a global economic driver, it also becomes a sustainability risk. Regulators in the EU, U.S., and Asia-Pacific are beginning to scrutinize the carbon impact of large-scale AI systems. Dean’s work, which emphasizes efficiency and system-wide accountability, provides a proactive framework for aligning AI development with environmental, social, and governance (ESG) goals.
Key implications include:
- Transparent accounting of AI emissions: Just as companies report Scope 1–3 carbon emissions, forward-thinking AI leaders must quantify the compute emissions behind model training and inference workloads.
- Integrating sustainability into ML ops: Rather than treating green compute as an afterthought, Dean’s model advocates baking carbon awareness into development cycles—through better profiling tools, model sparsity techniques, and intelligent scheduling of compute jobs to low-emission time windows.
- Net-zero-aligned AI roadmaps: Governments funding AI development—particularly in healthcare, defense, and education—must begin mandating that AI projects are compatible with national carbon neutrality targets. Dean’s approach shows that performance and sustainability are not mutually exclusive—they’re synergistic when designed from the systems level up.
Conclusion
Jeff Dean has redefined what it means to do AI at scale. He has proven that innovation and sustainability are not opposing forces, but complementary outcomes of good engineering. His vision challenges every CTO, infrastructure planner, and public leader to treat efficiency not as a constraint—but as a feature.
In an era where AI defines national competitiveness and corporate strategy, Dean’s work is a reminder: real scale requires responsibility. And responsibility, in the hands of systems thinkers, becomes a source of enduring advantage.
Works Cited
- A Carbon‑Aware Computing Platform for Reducing ML Emissions (Google Research Blog, 2022)
- The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design (Jeff Dean, arXiv, Nov 2019)
- In‑Datacenter Performance Analysis of a Tensor Processing Unit (Jouppi et al., ISCA 2017)
- Introducing Gemini: our largest and most capable AI model (Google Blog, Dec 2023)
- We now do more computing where there’s cleaner energy (Google Sustainability Blog, 2021)
- Hardware harvesting at Google: Reducing waste and emissions (Google Cloud Blog, Apr 2025)
- Tensor Processing Unit (Wikipedia, updated June 2025)
- Gemini (language model) (Wikipedia, June 2025)
- Introducing Gemini 2.0: our new AI model for the agentic era (Google Blog, Dec 2024)
- Google DeepMind Unveils Its Most Powerful AI Offering Yet (Time Magazine, Dec 2023)
- Google’s Gemini Robotics AI Model Reaches Into the Physical World (Wired, Mar 2025)
- Google has launched Gemini 2.0, its new AI model for practically everything (The Verge, Dec 2024)
- Klover.ai. (n.d.). Inside Google’s AI powerhouse: Distributed systems lessons from Jeff Dean. Klover.ai. https://www.klover.ai/inside-googles-ai-powerhouse-distributed-systems-lessons-from-jeff-dean/
- Klover.ai. (n.d.). Culture of excellence: Leadership and innovation strategies from Jeff Dean. Klover.ai. https://www.klover.ai/culture-of-excellence-leadership-and-innovation-strategies-from-jeff-dean/
- Klover.ai. (n.d.). Jeff Dean. Klover.ai. https://www.klover.ai/jeff-dean/
