Global Advisors | Quantified Strategy Consulting

Leadership

Quote: Max Levchin

Quote: Max Levchin

“Failing people who trust in you, hurts a lot more than just failing yourself.”
— Max Levchin, Affirm founder and CEO, Paypal founder

Max Levchin delivered a heartfelt commencement address at the University of Illinois at Urbana-Champaign in 2018, marking the 150th anniversary of the university’s first class. As an alumnus, Levchin shared his journey from a Soviet Union immigrant to a successful entrepreneur, emphasizing the importance of taking risks and embracing failure. He recounted his early experiences with failed startups, highlighting how these setbacks shaped his path to co-founding PayPal. Levchin stressed that while failure is painful, it is crucial to remain human and compassionate, especially when others are affected by your decisions. He shared a poignant lesson learned from a challenging period in his career: “Failing people who trust in you, hurts a lot more than just failing yourself.” This insight underscored the emotional weight of leadership and the responsibility towards those who believe in you. Levchin encouraged graduates to surround themselves with people who inspire them to be better and to take risks to discover their true selves. His speech was a call to action for the class of 2018 to embrace uncertainty and pursue their passions with resilience and integrity.

Quote: Yann Lecun

Quote: Yann Lecun

“Most of the infrastructure cost for AI is for inference: serving AI assistants to billions of people.”
— Yann LeCun, VP & Chief AI Scientist at Meta

Yann LeCun made this comment in response to the sharp drop in Nvidia’s share price on January 27, 2024, following the launch of Deepseek R1, a new AI model developed by Deepseek AI. This model was reportedly trained at a fraction of the cost incurred by Hyperscalers like OpenAI, Anthropic, and Google DeepMind, raising questions about whether Nvidia’s dominance in AI compute was at risk.

The market reaction stemmed from speculation that the training costs of cutting-edge AI models—previously seen as a key driver of Nvidia’s GPU demand—could decrease significantly with more efficient methods. However, LeCun pointed out that most AI infrastructure costs come not from training but from inference, the process of running AI models at scale to serve billions of users. This suggests that Nvidia’s long-term demand may remain strong, as inference still relies heavily on high-performance GPUs.

LeCun’s view aligned with analyses from key AI investors and industry leaders. He supported the argument made by Antoine Blondeau , co-founder of Alpha Intelligence Capital, who described Nvidia’s stock drop as “vastly overblown” and “NOT a ‘Sputnik moment’”, referencing the concern that Nvidia’s market position was insecure. Additionally, Jonathan Ross, founder of Groq, shared a video titled “Why $500B isn’t enough for AI,” explaining why AI compute demand remains insatiable despite efficiency gains.

This discussion underscores a critical aspect of AI economics: while training costs may drop with better algorithms and hardware, the sheer scale of inference workloads—powering AI assistants, chatbots, and generative models for billions of users—remains a dominant and growing expense. This supports the case for sustained investment in AI infrastructure, particularly in Nvidia’s GPUs, which continue to be the gold standard for inference at scale.

Infographic: Four critical DeepSeek enablers

Infographic: Four critical DeepSeek enablers

The DeepSeek team has introduced several high-impact changes to Large Language Model (LLM) architecture to enhance performance and efficiency:

Multi-Head Latent Attention (MLA): This mechanism enables the model to process multiple facets of input data simultaneously, improving both efficiency and performance. MLA reduces the memory required to compute a transformer’s attention by a factor of 7.5x to 20x, a breakthrough that makes large-scale AI applications more feasible. Unlike Flash Attention, which improves data organization in memory, MLA compresses the KV cache into a lower-dimensional space, significantly reducing memory usage—down to 5% to 13% of traditional attention mechanisms—while maintaining performance.
Mixture-of-Experts (MoE) Architecture: DeepSeek employs an MoE system that activates only a subset of its total parameters during any given task. For instance, in DeepSeek-V3, only 37 billion out of 671 billion parameters are active at a time, significantly reducing computational costs. This approach enhances efficiency and aligns with the trend of making AI models more compute-light, allowing freed-up GPU resources to be allocated to multi-modal processing, spatial intelligence, or genomic analysis. MoE models, as also leveraged by Mistral and other leading AI labs, allow for scalability while keeping inference costs manageable.
FP8 Floating Point Precision: To enhance computational efficiency, DeepSeek-V3 utilizes FP8 floating point precision during training, which helps in reducing memory usage and accelerating computation. This follows a broader trend in AI to optimize training methodologies, potentially influencing the approach taken by U.S.-based LLM providers. Given China’s restricted access to high-end GPUs due to U.S. export controls, optimizations like FP8 and MLA are critical in overcoming hardware limitations.
DeepSeek-R1 and Test-Time Compute Capabilities: DeepSeek-R1 is a model that leverages reinforcement learning (RL) to enable test-time compute, significantly improving reasoning capabilities. The model was trained using an innovative RL strategy, incorporating fine-tuned Chain of Thought (CoT) data and supervised fine-tuning (SFT) data across multiple domains. Notably, DeepSeek demonstrated that any sufficiently powerful LLM can be transformed into a high-performance reasoning model using only 800k curated training samples. This technique allows for rapid adaptation of smaller models, such as Qwen and LLaMa-70b, into competitive reasoners.
Distillation to Smaller Models: The team has developed distilled versions of their models, such as DeepSeek-R1-Distill, which are fine-tuned on synthetic data generated by larger models. These distilled models contain fewer parameters, making them more efficient while retaining significant capabilities. DeepSeek’s ability to achieve comparable reasoning performance at a fraction of the cost of OpenAI’s models (5% of the cost, according to Pelliccione) has disrupted the AI landscape.

The Impact of Open-Source Models:

DeepSeek’s success highlights a fundamental shift in AI development. Traditionally, leading-edge models have been closed-source and controlled by Western AI firms like OpenAI, Google, and Anthropic. However, DeepSeek’s approach, leveraging open-source components while innovating on training efficiency, has disrupted this dynamic. Pelliccione notes that DeepSeek now offers similar performance to OpenAI at just 5% of the cost, making high-quality AI more accessible. This shift pressures proprietary AI companies to rethink their business models and embrace greater openness.

Challenges and Innovations in the Chinese AI Ecosystem:

China’s AI sector faces major constraints, particularly in access to high-performance GPUs due to U.S. export restrictions. Yet, Chinese companies like DeepSeek have turned these challenges into strengths through aggressive efficiency improvements. MLA and FP8 precision optimizations exemplify how innovation can offset hardware limitations. Furthermore, Chinese AI firms, historically focused on scaling existing tech, are now contributing to fundamental advancements in AI research, signaling a shift towards deeper innovation.

The Future of AI Control and Adaptation:

DeepSeek-R1’s approach to training AI reasoners poses a challenge to traditional AI control mechanisms. Since reasoning capabilities can now be transferred to any capable model with fewer than a million curated samples, AI governance must extend beyond compute resources and focus on securing datasets, training methodologies, and deployment platforms. OpenAI has previously obscured Chain of Thought traces to prevent leakage, but DeepSeek’s open-weight release and published RL techniques have made such restrictions ineffective.

Broader Industry Context:

DeepSeek benefits from Western open-source AI developments, particularly Meta’s LLama model disclosures, which provided a foundation for its advancements. However, DeepSeek’s success also demonstrates that China is shifting from scaling existing technology to innovating at the frontier.
Open-source models like DeepSeek will see widespread adoption for enterprise and research applications, though Western businesses are unlikely to build their consumer apps on a Chinese API.
The AI innovation cycle is exceptionally fast, with breakthroughs assessed daily or weekly. DeepSeek’s advances are part of a rapidly evolving competitive landscape dominated by U.S. big tech players like OpenAI, Google, Microsoft, and Meta, who continue to push for productization and revenue generation. Meanwhile, Chinese AI firms, despite hardware and data limitations, are innovating at an accelerated pace and have proven capable of challenging OpenAI’s dominance.

These innovations collectively contribute to more efficient and effective LLMs, balancing performance with resource utilization while shaping the future of AI model development.

Sources: Global Advisors, Jack Clark – Anthropic, Antoine Blondeau, Alberto Pelliccione, infoq.com, medium.com, en.wikipedia.org, arxiv.org

Quote: Jack Clark

Quote: Jack Clark

“The most surprising part of DeepSeek-R1 is that it only takes ~800k samples of ‘good’ RL reasoning to convert other models into RL-reasoners. Now that DeepSeek-R1 is available people will be able to refine samples out of it to convert any other model into an RL reasoner.” – Jack Clark, Anthropic

Jack Clark, Co-founder of Anthropic, co-chair of the AI Index at Stanford University, co-chair of OECD working group on AI & Compute, shed light on the significance of DeepSeek-R1, a revolutionary AI reasoning model developed by China’s DeepSeek team. In an article posted in his newsletter on the 27th January 2025, Clark highlighted that it only takes approximately 800k samples of “good” RL (Reinforcement Learning) reasoning to convert other models into RL-reasoners.

The Power of Fine-Tuning

DeepSeek-R1 is not just a powerful AI model; it also provides a framework for fine-tuning existing models to enhance their reasoning capabilities. By leveraging the 800k samples curated with DeepSeek-R1, researchers can refine any other model into an RL reasoner. This approach has been demonstrated by fine-tuning open-source models like Qwen and Llama using the same dataset.

Implications for AI Policy

The release of DeepSeek-R1 has significant implications for AI policy and control. As Clark notes, if you need fewer than a million samples to convert any model into a “thinker,” it becomes much harder to control AI systems. This is because the valuable data, including chains of thought from reasoning models, can be leaked or shared openly.

A New Era in AI Development

The availability of DeepSeek-R1 and its associated techniques has created a new era in AI development. With an open weight model floating around the internet, researchers can now bootstrap any other sufficiently powerful base model into being an AI reasoner. This has the potential to accelerate AI progress worldwide.

Key Takeaways:

Fine-tuning is key : DeepSeek-R1 demonstrates that fine-tuning existing models with a small amount of data (800k samples) can significantly enhance their reasoning capabilities.
Open-source and accessible : The model and its techniques are now available for anyone to use, making it easier for researchers to develop powerful AI reasoners.
Implications for control : The release of DeepSeek-R1 highlights the challenges of controlling AI systems, as valuable data can be leaked or shared openly.

Conclusion

DeepSeek-R1 has marked a significant milestone in AI development, showcasing the power of fine-tuning and open-source collaboration. As researchers continue to build upon this work, we can expect to see even more advanced AI models emerge, with far-reaching implications for various industries and applications.

Quote: Marc Andreessen

Quote: Marc Andreessen

“DeepSeek-R1 is AI’s Sputnik moment.” – Marc Andreessen, Andreesen Horowitz

In a 27th January 2025 X statement that sent shockwaves through the tech community, venture capitalist Marc Andreessen declared that DeepSeek’s R1 AI reasoning model is “AI’s Sputnik moment.” This analogy draws parallels between China’s breakthrough in artificial intelligence and the Soviet Union’s historic achievement of launching the first satellite into orbit in 1957.

The Rise of DeepSeek-R1

DeepSeek, a Chinese AI lab, has made headlines with its open-source release of R1, a revolutionary AI reasoning model that is not only more cost-efficient but also poses a significant threat to the dominance of Western tech giants. The model’s ability to reduce compute requirements by half without sacrificing accuracy has sent shockwaves through the industry.

A New Era in AI

The release of DeepSeek-R1 marks a turning point in the AI arms race, as it challenges the long-held assumption that only a select few companies can compete in this space. By making its research open-source, DeepSeek is empowering anyone to build their own version of R1 and tailor it to their needs.

Implications for Megacap Stocks

The success of DeepSeek-R1 has significant implications for megacap stocks like Microsoft, Alphabet, and Amazon, which have long relied on proprietary AI models to maintain their technological advantage. The pen-source nature of R1 threatens to wipe out this advantage, potentially disrupting the business models of these tech giants.

Nvidia’s Nightmare

The news comes as a blow to Nvidia CEO Jensen Huang, who is ramping up production of his Blackwell microchip, a more advanced version of his industry-leading Hopper series H100s. The chip controls 90% of the AI semiconductor market, but R1’s ability to reduce compute requirements may render these chips less essential.

A New Era of Innovation

Perplexity AI founder Aravind Srinivas praised DeepSeek’s team for catching up to the West by employing clever solutions, including switching from binary encoding to floating point 8. This innovation not only reduces costs but also demonstrates that China is no longer just a copycat, but a leader in AI innovation.

Quote: Marc Benioff

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: Jeffrey Emanuel

Quote: Jeffrey Emanuel

“With R1, DeepSeek essentially cracked one of the holy grails of AI: getting models to reason step-by-step without relying on massive supervised datasets.” – Jeffrey Emanuel

Jeffrey Emanuel’s statement (“The Short Case for Nvidia Stock” – 25th January 2025) highlights a groundbreaking achievement in AI with DeepSeek’s R1 model, which has made significant strides in enabling step-by-step reasoning without the traditional reliance on vast supervised datasets:

Innovation Through Reinforcement Learning (RL):
- The R1 model employs reinforcement learning, a method where models learn through trial and error with feedback. This approach reduces the dependency on large labeled datasets typically required for training, making it more efficient and accessible.
Advanced Reasoning Capabilities:
- R1 excels in tasks requiring logical inference and mathematical problem-solving. Its ability to demonstrate step-by-step reasoning is crucial for complex decision-making processes, applicable across various industries from autonomous systems to intricate problem-solving tasks.
Efficiency and Accessibility:
- By utilizing RL and knowledge distillation techniques, R1 efficiently transfers learning to smaller models. This democratizes AI technology, allowing global researchers and developers to innovate without proprietary barriers, thus expanding the reach of advanced AI solutions.
Impact on Data-Scarce Industries:
- The model’s capability to function with limited data is particularly beneficial in sectors like medicine and finance, where labeled data is scarce due to privacy concerns or high costs. This opens doors for more ethical and feasible AI applications in these fields.
Competitive Landscape and Innovation:
- R1 positions itself as a competitor to models like OpenAI’s o1, signaling a shift towards accessible AI technology. This fosters competition and encourages other companies to innovate similarly, driving advancements across the AI landscape.

In essence, DeepSeek’s R1 model represents a significant leap in AI efficiency and accessibility, offering profound implications for various industries by reducing data dependency and enhancing reasoning capabilities.

Quote: Andrej Karpathy

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

Quote: Dario Amodei

Quote: Dario Amodei

“Anthropic is a policy actor, Anthropic is not a political actor.” – Dario Amodei

This quote by Dario Amodei was made on the 21st January 2025 at Davos. Anthropic, as an entity, focuses primarily on influencing policies rather than engaging in overtly political activities.

The context of this statement emphasizes Anthropic’s commitment to its role as a policy influencer, ensuring that their actions are not driven by partisan politics but instead guided by the principles and strategies outlined in their policies.

Quote: Joseph McCarthy

Quote: Joseph McCarthy

“At war’s end, we were physically the strongest nation on earth and, at least potentially, the most powerful intellectually and morally. Ours could have been the honor of being a beacon on the desert of destruction, a shining living proof that civilization was not yet ready to destroy itself. Unfortunately, we have failed miserably and tragically to arise to the opportunity.”

Joseph McCarthy

Joseph McCarthy was an American politician and U.S. Senator from Wisconsin, best known for his role in the anti-communist movement during the early Cold War period. Born on November 14, 1908, McCarthy gained national prominence in the early 1950s when he claimed that numerous communists and Soviet spies had infiltrated the U.S. government and other institutions.

His most notable period of influence came during the “Red Scare,” a time characterized by heightened fears of communist influence in the United States. McCarthy’s aggressive tactics included making unsubstantiated accusations against government officials, military personnel, and various public figures, leading to a widespread atmosphere of fear and suspicion. This period, often referred to as “McCarthyism,” was marked by intense scrutiny and persecution of individuals based on their political beliefs or associations.

McCarthy’s methods and lack of evidence eventually led to his downfall. His influence waned after the televised Army-McCarthy hearings in 1954, where his aggressive questioning and bullying tactics were exposed to the public. The Senate formally censured him later that year, and he died on May 2, 1957, from health complications related to alcoholism. McCarthy’s legacy is often associated with the dangers of political extremism and the violation of civil liberties in the name of national security.

Quote: J. Edgar Hoover

Quote: J. Edgar Hoover

“It is the function of mass agitation to exploit all the grievances, hopes, aspirations, prejudices, fears, and ideals of all the special groups that make up our society, social, religious, economic, racial, political. Stir them up. Set one against the other. Divide and conquer. That’s the way to soften up a democracy.”

J. Edgar Hoover

J. Edgar Hoover was an American law enforcement official who served as the first Director of the Federal Bureau of Investigation (FBI) from its founding in 1935 until his death in 1972. Born on January 1, 1895, Hoover played a significant role in shaping modern policing and the FBI’s investigative techniques. He was known for his efforts to combat organized crime, political corruption, and civil rights movements, often employing controversial methods, including surveillance and infiltration.

Hoover’s tenure was marked by his strong belief in the need for a powerful federal law enforcement agency to maintain order and protect national security. He was also known for his controversial stance on civil liberties, often prioritizing national security over individual rights. His legacy is complex, as he is both credited with modernizing the FBI and criticized for his authoritarian tactics and abuses of power, particularly in relation to civil rights activists and political dissidents.

Quote: Richard M. Nixon

Quote: Richard M. Nixon

“We are reaping the whirlwind for a decade of growing disrespect for law, decency and principle in America.”

Richard M. Nixon

Richard Nixon, the 37th President of the United States, made this statement during his address to the Bohemian Club in San Francisco on July 29, 1967. At the time, America was in the throes of the Vietnam War and the Civil Rights Movement, both of which were causing significant social and political upheaval. Nixon’s quote reflects his concern about the growing disregard for law, decency, and principle in America, which he believed was leading to a whirlwind of consequences.

Nixon himself would later become a symbol of this whirlwind when he resigned from the presidency in 1974 following the Watergate scandal. His administration’s involvement in the break-in at the Democratic National Committee headquarters and the subsequent cover-up was seen as a blatant disregard for the law, leading to a loss of public trust in the government.

Quote: Harold S. Geneen

Quote: Harold S. Geneen

“In the business world, everyone is paid in two coins: cash and experience. Take the experience first; the cash will come later.”

Harold S. Geneen

Harold S. Geneen (1910-1997) was an American businessman most renowned for his role as President and CEO of International Telephone and Telegraph (ITT) from 1959 to 1977. Under his leadership, ITT grew from a medium-sized business into an international conglomerate, acquiring over 350 companies in diverse sectors such as insurance, hotels, and telecommunications.

Born in Bournemouth, England, Geneen moved to the United States as a child. He graduated from New York University with a degree in accounting and began his career at the Bell Telephone Company. He later held executive positions at several companies, including Hygrade Food Products and Jones & Laughlin Steel Company, before joining ITT.

Geneen was known for his hands-on management style and his belief in the power of detailed financial analysis. He was a pioneer of the modern multinational corporation and is often credited with creating the first conglomerate. His management philosophy was encapsulated in his famous quote, “Management must manage.”

The quote, “In the business world, everyone is paid in two coins: cash and experience. Take the experience first; the cash will come later,” reflects Geneen’s belief in the value of experience and knowledge. He believed that financial success was a byproduct of learning and growth, a philosophy that guided his career and contributed to his remarkable success.

Geneen’s legacy continues to influence modern business practices. His emphasis on the importance of experience over immediate financial gain has been echoed by numerous business leaders and remains a guiding principle for many in the corporate world.

Quote: Unknown source

Quote: Unknown source

“If serving is below you, leading is above you.”

Unknown source

Leadership gurus have widely recognized the concept of servant leadership as a powerful approach to leading others. This style of leadership prioritizes serving the needs, growth, and well-being of followers over personal advancement or authority.

Robert Greenleaf, often regarded as the father of modern servant leadership, emphasized that true leaders should embody humility and focus on facilitating and supporting their team rather than controlling them. He believed that leadership is not about gaining power but about serving others selflessly.

Other notable leadership gurus like Stephen Covey, Simon Sinek, and Jim Rohn have also highlighted the importance of servant leadership in building trust, fostering collaboration, and inspiring a shared vision among followers. They argue that leaders who prioritize service over personal gain create an environment of mutual respect, transparency, and self-awareness, which ultimately leads to more effective and sustainable leadership.

In today’s complex globalized world, where organizations face increasing challenges and ethical concerns, servant leadership has gained renewed relevance. Its emphasis on serving others first aligns with the needs of modern businesses, governments, and communities that are navigating an interconnected and rapidly changing landscape.

Quote: Søren Kierkegaard

Quote: Søren Kierkegaard

“Of all deceivers fear most yourself!”

Søren Kierkegaard

Quote: Ernest Hemingway

Quote: Ernest Hemingway

“You can’t get away from yourself by moving from one place to another.”

Ernest Hemingway

Quote: Marcus Aurelius

Quote: Marcus Aurelius

“Whenever you are about to find fault with someone, ask yourself the following question: What fault of mine most nearly resembles the one I am about to criticize?”

Marcus Aurelius

Quote: David Stoddard

Quote: David Stoddard

“Mentoring is not about making people like you, but about helping them become the best version of themselves.”

David Stoddard

Global Advisors’ Thoughts: Leading a deliberate life

Global Advisors’ Thoughts: Leading a deliberate life

By Marc Wilson
Marc is a partner at Global Advisors and based in Johannesburg, South Africa

Download this article at https://globaladvisors.biz/blog/2018/06/26/leading-a-deliberate-life/.

Picket fences. Family of four. Management position.

Mid-life crisis. Meaning. Purpose.

Someone once said that, “At 18, I had all the answers. At 35, I realised I didn’t know the question.”

Serendipity has a lot going for it. Many people might sail through life taking what comes and enjoying the moment. Others might be open to chance and have nothing go right for them.

Some people might strive to achieve, realise rare successes and be bitterly unhappy. Others might be driven and enjoy incredible success and fulfilment.

Perhaps the majority of us become beholden to the momentum of our lives.

We might study, start a career, marry, buy a dream house, have children, send them to a top school. Those steps make up components of many of our dreams. They are steps that may define each subsequent choice. As I discussed this with a friend recently, he remarked that few of these steps had been subject of deliberations in his life – increasingly these steps were the outcome of momentum. Each will shape every step he takes for the rest of his life. He would not have things any other way, but if he knew what he knows now, he might have been more deliberate about choice and consequence…..

Read more at https://globaladvisors.biz/blog/2018/06/26/leading-a-deliberate-life/

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Global Advisors’ Thoughts: Is insecurity behind that dysfunction?

Global Advisors’ Thoughts: Is insecurity behind that dysfunction?

By Marc Wilson
Marc is a partner at Global Advisors and based in Johannesburg, South Africa

Download this article at http://www.globaladvisors.biz/inc-feed/20170907/thoughts-is-insecurity-behind-that-dysfunction

We tend to characterise insecurity as what we see in overtly fragile, shy and awkward people. We think that their insecurity presents as lack of confidence. And often we associate it with under-achievement.

Sometimes we might be aware that insecurities can lie behind the -ias, -isms and the phobias. Body dysmorphia? Insecurity about attractiveness. Racism? Often the need to find security by claiming superiority, belonging to group with power, a group you understand and whose acceptance you want. Homophobia? Often insecurity about one’s own sexuality or masculinity / feminity.

So it is often counter-intuitive when we discover that often behind incredible success lies – insecurity! In fact, an article I once read described the successful elite of strategy consulting firms as typically “insecure over-achievers.”

Insecurity must be one of the most misunderstood drivers of dysfunction. Instead we see its related symptoms and react to those. “That woman is so overbearing. That guy is so aggressive! That girl is so self-absorbed. That guy is so competitive.” Even, “That guy is so arrogant.”

How is it that someone we might perceive as competitive, arrogant or overconfident might be insecure? Sometimes people overcompensate to hide a weakness or insecurity. Sometimes in an effort to avoid feeling defensive of a perceived shortcoming, they might go on the offensive – telling people they are the opposite or even faking security.

Do we even know what insecurity is? The very need to…

Read the rest of “Power, Control and Space” at http://www.globaladvisors.biz/inc-feed/20170907/thoughts-is-insecurity-behind-that-dysfunction

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