agents Archives - Global Advisors | Quantified Strategy Consulting

Quote: Andrew Ng – AI Guru

“For the majority of businesses, focus on building applications using agentic workflows rather than solely scaling traditional AI. That’s where the greatest opportunity lies.” – Andrew Ng – AI Guru

Andrew Ng is widely recognized as a pioneering figure in artificial intelligence, renowned for his roles as co-founder of Google Brain, former chief scientist at Baidu, and founder of DeepLearning.AI and Landing AI. His work has shaped the trajectory of modern AI, influencing its academic, industrial, and entrepreneurial development on a global scale.

The quote “For the majority of businesses, focus on building applications using agentic workflows rather than solely scaling traditional AI. That’s where the greatest opportunity lies.” captures a key transformation underway in how organizations approach AI adoption. Ng delivered this insight during a Luminary Talk at the Snowflake Summit in June 2024, in a discussion centered on the rise of agentic workflows within AI applications.

Historically, businesses have harnessed AI by leveraging static, rule-based automation or applying large language models to single-step tasks—prompting a system to generate a document or answer a question in one go. Ng argues this paradigm is now giving way to a new era driven by AI agents capable of multi-step reasoning, planning, tool use, and collaboration—what he terms “agentic workflows”.

Agentic workflows differ from traditional approaches by allowing autonomous AI agents to adapt, break down complex projects, and iterate in real time, much as a human team might tackle a multifaceted problem. For example, instead of a single prompt generating a sales report, an AI agent in an agentic workflow could gather the relevant data, perform analysis, adjust its approach based on interim findings, and refine the output after successive rounds of review and self-critique. Ng has highlighted design patterns such as reflection, planning, multi-agent collaboration, and dynamic tool use as central to these workflows.

Ng’s perspective is that businesses stand to gain the most not merely from increasing the size or data intake of AI models, but from designing systems where AI agents can independently coordinate and accomplish sophisticated goals. He likens this shift to the leap from single-threaded to multi-threaded computing, opening up exponential gains in capability and value creation.

For business leaders, Andrew Ng’s vision offers a roadmap: the frontier of competitive advantage lies in reimagining how AI-powered agents are integrated into business processes, unlocking new possibilities for efficiency, innovation, and scalability that go beyond what traditional, “one-shot” AI can deliver.

Ng continues to lead at the intersection of AI innovation and practical business strategy, championing agentic AI as the next great leap for organizations seeking to realize the full promise of artificial intelligence.

Term: AI Agents

AI Agents are autonomous software systems that interact with their environment, perceive data, and independently make decisions and take actions to achieve specific, user-defined goals. Unlike traditional software, which follows static, explicit instructions, AI agents are guided by objective functions and have the ability to reason, learn, plan, adapt, and optimize responses based on real-time feedback and changing circumstances.

Key characteristics of AI agents include:

Autonomy: They can initiate and execute actions without constant human direction, adapting as new data or situations arise.
Rational decision-making: AI agents use data and perceptions of their environment to select actions that maximize predefined goals or rewards (their “objective function”), much like rational agents in economics.
Learning and Adaptation: Through techniques like machine learning, agents improve their performance over time by learning from experience.
Multimodal abilities: Advanced agents process various types of input/output—text, audio, video, code, and more—and often collaborate with humans or other agents to complete complex workflows or transactions.
Versatility: They range from simple (like thermostats) to highly complex systems (like conversational AI assistants or autonomous vehicles).

Examples include virtual assistants that manage calendars or customer support, code-review bots in software development, self-driving cars navigating traffic, and collaborative agents that orchestrate business processes.

Related Strategy Theorist – Stuart Russell

As a renowned AI researcher and co-author of the seminal textbook “Artificial Intelligence: A Modern Approach,” Russell has shaped foundational thinking on agent-based systems and rational decision-making. He has also been at the forefront of advocating for the alignment of agent objectives with human values, providing strategic frameworks for deploying autonomous agents safely and effectively across industries.

Quote: Sholto Douglas, Anthropic researcher

“We believe coding is extremely important because coding is that first step in which you will see AI research itself being accelerated… We think it is the most important leading indicator of model capabilities.”

Sholto Douglas, Anthropic researcher

Sholto Douglas is regarded as one of the most promising new minds in artificial intelligence research. Having graduated from the University of Sydney with a degree in Mechatronic (Space) Engineering under the guidance of Ian Manchester and Stefan Williams, Douglas entered the field of AI less than two years ago, quickly earning respect for his innovative contributions. At Anthropic, one of the leading AI research labs, he specializes in scaling reinforcement learning (RL) techniques within advanced language models, focusing on pushing the boundaries of what large language models can learn and execute autonomously.

Context of the Quote

The quote, delivered by Douglas in an interview with Redpoint—a venture capital firm known for its focus on disruptive startups and technology—underscores the central thesis driving Anthropic’s recent research efforts:

“We believe coding is extremely important because coding is that first step in which you will see AI research itself being accelerated… We think [coding is] the most important leading indicator of model capabilities.”

This statement reflects both the technical philosophy and the strategic direction of Anthropic’s latest research. Douglas views coding not only as a pragmatic benchmark but as a foundational skill that unlocks model self-improvement and, by extension, accelerates progress toward artificial general intelligence (AGI).

Claude 4 Launch: Announcements and Impact

Douglas’ remarks came just ahead of the public unveiling of Anthropic’s Claude 4, the company’s most sophisticated model to date. The event highlighted several technical milestones:

Reinforcement Learning Breakthroughs: Douglas described how, over the past year, RL techniques in language models had evolved from experimental to demonstrably successful, especially in complex domains like competitive programming and advanced mathematics. For the first time, they achieved “proof of an algorithm that can give us expert human reliability and performance, given the right feedback loop”.
Long-Term Vision: The launch positioned coding proficiency as the “leading indicator” for broader model capabilities, setting the stage for future models that can meaningfully contribute to their own research and improvement.
Societal Implications: Alongside the technical announcements, the event and subsequent interviews addressed how rapid advances in AI—exemplified by Claude 4—will impact industries, labor markets, and global policy, urging stakeholders to prepare for a world where AI agents are not just tools but collaborative problem-solvers.

Why This Moment Matters

Douglas’ focus on coding as a metric is rooted in the idea that tasks requiring deep logic and creative problem-solving, such as programming, provide a “canary in the coal mine” for model sophistication. Success in these domains demonstrates a leap not only in computational power or data processing, but in the ability of AI models to autonomously reason, plan, and build tools that further accelerate their own learning cycles.

The Claude 4 launch, and Douglas’ role within it, marks a critical inflection point in AI research. The ability of language models to code at—or beyond—expert human levels signals the arrival of AI systems capable of iteratively improving themselves, raising both hopes for extraordinary breakthroughs and urgent questions around safety, alignment, and governance.

Sholto Douglas’ Influence

Though relatively new to the field, Douglas has emerged as a thought leader shaping Anthropic’s approach to scalable, interpretable, and safe AI. His insights bridge technical expertise and strategic foresight, providing a clear-eyed perspective on the trajectory of rapidly advancing language models and their potential to fundamentally reshape the future of research and innovation.

Quote: Jensen Huang, Nvidia CEO

“AI inference token generation has surged tenfold in just one year, and as AI agents become mainstream, the demand for AI computing will accelerate. Countries around the world are recognizing AI as essential infrastructure – just like electricity and the internet.”

Jensen Huang, Nvidia CEO

Context: The Nvidia 2026 Q1 results

On May 28, 2025, NVIDIA announced its financial results for the first quarter of fiscal year 2026, reporting a record-breaking revenue of $44,1 billion, a 69% increase from the previous year. This surge was primarily driven by robust demand for AI chips, with the data center segment contributing significantly, achieving a 73% year-over-year revenue increase to $39,1 billion.

Despite these impressive figures, NVIDIA faced challenges due to U.S. export restrictions on its H20 chips to China, resulting in a $4,5 billion charge for excess inventory and an anticipated $8 billion revenue loss in the second quarter. During the earnings call, Huang criticized these restrictions, stating they have inadvertently spurred innovation in China rather than curbing it.

In the context of these developments, Huang remarked, “AI inference token generation has surged tenfold in just one year, and as AI agents become mainstream, the demand for AI computing will accelerate. Countries around the world are recognizing AI as essential infrastructure—just like electricity and the internet.” This statement underscores the transformative impact of AI across various sectors and highlights the critical role of AI infrastructure in modern economies.

Under Huang’s leadership, NVIDIA has not only achieved remarkable financial success but has also been at the forefront of AI and computing innovations. His strategic vision continues to shape the company’s trajectory, navigating complex international dynamics while driving technological progress.

Jensen Huang: Visionary Leader Behind Nvidia

Early Life and Education

Jensen Huang, born in Tainan, Taiwan, in 1963, immigrated to the United States at a young age. He pursued his undergraduate studies in electrical engineering at Oregon State University, earning a Bachelor of Science degree, and later completed a Master of Science in Electrical Engineering at Stanford University. Before founding Nvidia, Huang gained industry experience at LSI Logic and Advanced Micro Devices (AMD), building a foundation in semiconductor technology and business leadership.

Founding Nvidia and Early Struggles

In 1993, at the age of 30, Huang co-founded Nvidia with Chris Malachowsky and Curtis Priem. The company’s inception was humble—its first meetings took place in a local Denny’s restaurant. The early years were marked by intense challenges and uncertainty. Nvidia’s initial focus on graphics accelerator chips nearly led to its demise, with the company surviving on a critical $5 million investment from Sega. By 1997, Nvidia was just a month away from running out of payroll funds before the release of the RIVA 128 chip turned its fortunes around.

Huang’s leadership style was forged in these difficult times. He often reminded his team, “Our company is thirty days from going out of business,” a mantra that underscored the urgency and resilience required to survive in Silicon Valley’s fast-paced environment. Huang has credited these hardships as essential to his growth as a leader and to Nvidia’s eventual success.

Transforming the Tech Landscape

Under Huang’s stewardship, Nvidia pioneered the invention of the Graphics Processing Unit (GPU) in 1999, revolutionizing computer graphics and catalyzing the growth of the PC gaming industry. More recently, Nvidia has become a central player in the rise of artificial intelligence (AI) and accelerated computing, with its hardware and software platforms powering breakthroughs in data centers, autonomous vehicles, and generative AI.

Huang’s vision and execution have earned him widespread recognition, including election to the National Academy of Engineering, the Semiconductor Industry Association’s Robert N. Noyce Award, the IEEE Founder’s Medal, and inclusion in TIME magazine’s list of the 100 most influential people.

Quote: Marc Benioff

“AI agents. That’s beginning of an unlimited workforce.” – Marc Benioff

Marc Benioff is discussing the potential impact of AI agents on the workforce during a conversation between Marc Benioff, the CEO of Salesforce, and Bloomberg at the World Economic Forum (WEF) in Davos on the 24th January 2025. He mentions that with AI agents, companies can scale their sales and service operations without having to hire more employees. This is illustrated by an example of a customer, Wiley, which was able to avoid hiring gig workers during its “back to school” season due to the use of Salesforce’s agent force technology.

Benioff emphasizes that this is just the beginning of an unlimited workforce, implying that AI agents will continue to revolutionize the way companies operate and potentially lead to significant changes in the job market. He also highlights the benefits of using AI agents, such as increased productivity and the ability to redeploy human resources to other areas of the business.

The quote suggests that Benioff is optimistic about the potential of AI agents to transform businesses and create new opportunities for growth and innovation. However, it also raises questions about the impact on employment and the future of work in general.

Quote: Andrej Karpathy

“I think 2025-2035 is the decade of agents…… you’ll spin up organizations of Operators for long-running tasks of your choice (eg running a whole company).” – Andrej Karpathy, renowned AI Researcher & Leader

The concept of agents, as described by Andrej Karpathy on X on the 23rd January 2025, is a revolutionary idea that has been gaining traction in the field of artificial intelligence (AI). An agent refers to an AI-enabled software system that can perform tasks autonomously, making decisions and taking actions on its own. This technology has the potential to transform various aspects of our lives, from personal assistance to complex organizational management.

The Digital World: A Precedent for Agent-Based Automation

Karpathy draws an analogy between digital agents and humanoid robots in the physical world. Just as a humanoid robot can perform tasks autonomously using its sensors and actuators, a digital agent can interact with its environment through interfaces such as keyboards, mice, or even voice commands. This gradual shift towards autonomy will lead to a mixed-world scenario where humans serve as high-level supervisors, monitoring and guiding low-level automation.

The Role of OpenAI’s Operator

OpenAI’s Operator project is a pioneering effort in developing digital agents that can perform complex tasks. By integrating multimodal interfaces (images, video, audio) with large language models (LLMs), Operator has demonstrated the potential for agents to assist humans in various domains, such as ordering food or checking hotel information.

Challenges and Opportunities

However, Karpathy emphasizes that significant challenges remain before agents can become a reality. These include:

Multimodal integration: Seamlessly integrating multiple interfaces (e.g., images, video, audio) with LLMs to enable more comprehensive understanding of tasks.
Long task horizons: Developing agents capable of handling complex, long-running tasks that require sustained attention and decision-making.
Scalability and reliability: Ensuring that agents can operate reliably and efficiently in various environments and scenarios.

Despite these challenges, Karpathy believes that the decade of 2025-2035 will be marked by significant advancements in agent technology. He envisions a future where humans can spin up organizations of operators to manage complex tasks, such as running an entire company. This would enable CEOs to focus on high-level strategy and oversight, while agents handle day-to-day operations.

Implications and Future Directions

The emergence of agents has far-reaching implications for various industries, including:

Business: Agents could revolutionize organizational management, enabling companies to operate more efficiently and effectively.
Healthcare: Agents could assist in patient care, freeing up medical professionals to focus on high-level decision-making.
Education: Agents could personalize learning experiences, adapting to individual students’ needs and abilities.

As Karpathy notes, the market size and opportunity for agent-based automation are substantial, particularly in the physical world. However, the digital world is likely to see faster adoption due to the relative ease of flipping bits compared to moving atoms.

In conclusion, the concept of agents has the potential to transform various aspects of our lives, from personal assistance to complex organizational management. While significant challenges remain, Karpathy’s vision for a future where humans and agents collaborate to achieve remarkable outcomes is an exciting prospect that warrants continued research and development.
Andrej Karpathy is a renowned AI Researcher & Leader, former Director of AI at Tesla, Co-Founder of OpenAI, and Instructor of Stanford’s CS231n Course