ARTIFICIAL INTELLIGENCE
An AI-native strategy firmGlobal Advisors: a consulting leader in defining quantified strategy, decreasing uncertainty, improving decisions, achieving measureable results.
A Different Kind of Partner in an AI World
AI-native strategy
consulting
Experienced hires
We are hiring experienced top-tier strategy consultants
Quantified Strategy
Decreased uncertainty, improved decisions
Global Advisors is a leader in defining quantified strategies, decreasing uncertainty, improving decisions and achieving measureable results.
We specialise in providing highly-analytical data-driven recommendations in the face of significant uncertainty.
We utilise advanced predictive analytics to build robust strategies and enable our clients to make calculated decisions.
We support implementation of adaptive capability and capacity.
Our latest
Thoughts
Global Advisors’ Thoughts: Empathy and understanding – why they are the qualities that help us achieve our own happiness and success
By Marc Wilson
Our team had just finished a book review presentation on Dale Carnegie’s “Making Friends and Influencing People”. Jane (*name changed) looked troubled: “Isn’t this stuff about manipulating people?”
Therein lies a paradox in showing empathy: without empathy for others, you face less influence, friendship, love and success. But if those are your goal rather than the sincere care for others, then your empathy is not really empathy at all.
Many people might react to empathy as “soft.” But empathy is a mark of incredible strength. It dares us to care. It requires us to put ourselves to one side. It requires us to be vulnerable – otherwise all we are doing is showing sympathy. Empathy requires self-awareness and skill.
Sympathy is easy. Sympathy does not go as far as empathy – it keeps us distant from the situation someone else is experiencing. It places us in danger of being condescending. Empathy requires us to put our self into their situation as them – not us.
Empathy gets the best out of those around us – and opens us up to be a better version of ourselves.
I find it incredibly difficult to manage a balance. A balance of being sufficiently confident and willing to share my own experience in an unbiased and helpful way – while removing enough of myself to allow someone else to find their own path and live their own experience. To be an empathetic leader, I believe I need to care about my team being at their best at work and in life.
Skills such as active listening are important to remove ourselves from the coaching we give others. But I think empathy requires us to be authentically present and involved in a way that facilitating someone else’s own solution does not.
Empathetic leadership challenges me to use my own experience and position in a way that is open to the challenges and experiences of others. And most critically demonstrates that I act out of care and acknowledgement of them.
Empathy requires that we know our self well enough that we are able to remove our projections of our own biases and feelings from the situation, appreciate the other person’s view of the world and how that impacts the situation for them.
Think about how you respond to others. How often do you respond to their experience, feelings and fears based on your own fears? Do your responses contain the word “I?” Do you fear genuinely experiencing the world as them? Do you seek to affirm your own view and experience through your response? Are you scared as being seen as similar to the other person in their own “deficiencies” and “imperfections”? How many of these imperfections are merely your own biases and fears?
Read more by clicking here: http://www.globaladvisors.biz/thoughts/20170627/empathy-and-understanding-why-they-are-the-qualities-that-help-us-achieve-our-own-happiness-and-success
Strategy Tools
Strategy Tools: Repeatable Business Models in Times of Uncertainty
By Innocent Dutiro
Innocent is an associate partner at Global Advisors and based in Johannesburg, South Africa
Research (Allen and Zook) tells us that sustained profitable growth and the methods for capturing it are much less about the choice of hot market than about the how and why of strategy and the business model translating it into action. The ongoing Coronavirus crisis is likely to put these beliefs to severe test. It is likely that the survivors and winners that emerge on the other side of the crisis will be businesses that have pursued repeatable business models.
These businesses’ approach to strategy focus less on a rigid plan to pursue growth markets and more on developing a general direction built around deep and uniquely strong capabilities that constantly learn, continuously improve, test, and adjust in manageable increments to the changing market. Repeatable business models enable organizations to distinguish between transient crises and game-changing developments while enabling them to take action that ensures their sustained prosperity. All without compromising on the beliefs that underpin the culture of the organization.
This might sound counterintuitive; how does a repeatable business model help you deal with a “black swan” event such as the COVID-19 pandemic? To answer this question, it is important to understand the three principles that underpin repeatability.
Principle 1: A strong, well-differentiated core
Differentiation drives competitive advantage and relative profitability among businesses. The basis for differentiation must deliver enhanced profitability by either delivering superior service to your core customers or offering cost economics that help you to out-invest your competitors. The unique assets, deep competencies and capabilities that make this differentiation possible and that are translated into behaviours and product features, define the “core of the core” of the business.
Principle 2: Clear non-negotiables
Non-negotiables are the company’s core values and key criteria used to make trade-offs in decision making. These improve the focus and simplicity of strategy by translating it into practical behavioural rules and prohibitions. This reduces the distance from management to the frontline (and back). Employee loyalty and commitment is driven primarily by a strong belief in the values of the management team and the organisation’s strategy. A clearly understood strategy is evidenced through:
- Widespread understanding of the strategy at all levels within the organization.
- Seeing the world the same way throughout the organization.
- A shared vocabulary and priorities.
Principle 3: Systems for closed-loop learning
Self-conscious methods to perceive and adapt to change alongside well-developed systems to learn and drive continuous improvement are hallmarks of successful repeatable business models.
A second form of closed-loop learning is more relevant to a crisis such as the coronavirus as it relates to those less frequent situations when fundamental change in the marketplace (like technology, competition, customer need and behaviour) threatens a key element of the repeatable business model itself. A company’s ability to adapt or have a sufficient sense of urgency in response to a potentially mortal threat is key to survival and continued prosperity.
The various steps that governments are taking to contain and eradicate the virus have the potential of building habits that consumers might choose to adopt on a more permanent basis even after the pandemic. These include working from home, remote meetings, reduced commuting, greater use of online services and more cashless transactions. Businesses thus need to be prepared to adjust and adapt their strategies and business models to meet the demand created by the new behaviours. Firms with a clearly defined set of non-negotiables will find it easier to mobilize their employees towards the necessary change.
While business is currently focused on taking measures to safeguard their staff, serve their customers and preserve cash to ensure liquidity during the period of low demand and/or production, attention should also be turning to steps necessary to adapt strategies to enable competitiveness in the new normal after the pandemic.
Fast Facts
Modern portfolio theory (MPT) can be applied to business portfolio decision-making
- Shareholders seek to maximise company profits while minimising risk
- However, lower risk businesses are usually accompanied with lower returns and high risk businesses with higher returns
- Comparisons between various risk and return profiles can be measured using the Sharpe ratio – return per unit of risk
- Combinations (degree of balance sheet investment) in individual portfolios could realise higher returns per unit of risk than what is achievable in an individual business unit – some combinations are not always obvious
- By exiting a higher risk-return portfolio BU J, ABC would be able to increase its return per unit of risk from 4,3 to 4,5
- It is often psychologically difficult for businesses to exit high return portfolios
- Emotional decision-making can be muted by applying the logic of modern portfolio theory in the board room
Selected News
Quote: Dr. Fei-Fei Li – Stanford Professor – world-renowned authority in artificial intelligence
“I do think countries all should invest in their own human capital, invest in partnerships and invest in their own technological stack as well as the business ecosystem… I think not investing in AI would be macroscopically the wrong thing to do.” – Dr. Fei-Fei Li – Stanford Professor – world-renowned authority in artificial intelligence
The statement was delivered during a high-stakes panel discussion on artificial superintelligence, convened at the Future Investment Initiative in Riyadh, where nation-state leaders, technologists, and investors gathered to assess their strategic positioning in the emerging AI era. Her words strike at the heart of a dilemma facing governments worldwide: how to build national AI capabilities whilst avoiding the trap of isolationism, and why inaction would be economically and strategically untenable.
Context: The Geopolitical Stakes of AI Investment
The Historical Moment
Dr. Li’s statement comes at a critical juncture. By late 2024 and into 2025, artificial intelligence had transitioned from speculative technology to demonstrable economic driver. Estimates suggested AI could generate between $15 trillion and $20 trillion in economic value globally by 2030—a figure larger than the current gross domestic product of most nations. This windfall is not distributed evenly; rather, it concentrates among early movers with capital, infrastructure, and talent. The race is on, and the stakes are existential for national competitiveness, employment, and geopolitical influence.
In this landscape, a nation that fails to invest in AI capabilities risks profound economic displacement. Yet Dr. Li is equally clear: isolation is counterproductive. The most realistic path forward combines three pillars:
- Human Capital: The talent to conceive, build, and deploy AI systems
- Partnerships: Strategic alliances, particularly with leading technological ecosystems (the US hyperscalers, for instance)
- Domestic Technological Infrastructure: The local research bases, venture capital, regulatory frameworks, and business ecosystems that enable sustained innovation
This is not a counsel of surrender to Silicon Valley hegemony, but rather a sophisticated argument about comparative advantage and integration within global technological networks.
Dr. Fei-Fei Li: The Person and Her Arc
Early Life and Foundational Values
Dr. Fei-Fei Li’s perspective is shaped by her personal trajectory. Born in Chengdu, China, she emigrated to the United States at age fifteen, settling in New Jersey where her parents ran a small business. This background infuses her thinking: she understands both the promise of technological mobility and the structural barriers that constrain developing economies. She obtained her undergraduate degree in physics from Princeton University in 1999, with high honours, before pursuing doctoral studies at the California Institute of Technology, where she worked across computer science, electrical engineering, and cognitive neuroscience, earning her PhD in 2005.
The ImageNet Revolution
In 2007, whilst at Princeton, Dr. Li embarked on a project that would reshape artificial intelligence. Observing that cognitive psychologist Irving Biederman estimated humans recognise approximately 30,000 object categories, Li conceived ImageNet: a massive, hierarchically organised visual database. Colleagues dismissed the scale as impractical. Undeterred, she led a team (including Princeton professors Jia Deng, Kai Li, and Wei Dong) that leveraged Amazon Mechanical Turk to label over 14 million images across 22,000 categories.
By 2009, ImageNet was published. More critically, the team created the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), an annual competition that invited researchers worldwide to develop algorithms for image classification. This contest became the crucible in which modern deep learning was forged. When Geoffrey Hinton’s group achieved a breakthrough using convolutional neural networks in 2012, winning the competition by a decisive margin, the deep learning revolution was catalysed. ImageNet is now recognised as one of the three foundational forces in the birth of modern AI.
What is instructive here is that Dr. Li’s contribution was not merely technical but infrastructural: she created a shared resource that democratised AI research globally. Academic groups from universities across continents—not just Silicon Valley—could compete on equal footing. This sensibility—that progress depends on enabling distributed talent—runs through her subsequent work.
Career Architecture and Strategic Leadership
Following her Princeton years, Dr. Li joined Stanford University in 2009, eventually becoming the Sequoia Capital Professor of Computer Science—a title of singular prestige. From 2013 to 2018, she directed Stanford’s Artificial Intelligence Lab (SAIL), one of the world’s premier research institutes. Her publications exceed 400 papers in top-tier venues, and she remains one of the most cited computer scientists of her generation.
During a sabbatical from Stanford (January 2017 to September 2018), Dr. Li served as Vice President and Chief Scientist of AI/ML at Google Cloud. Her mandate was to democratise AI technology, lowering barriers for businesses and developers—work that included advancing products like AutoML, which enabled organisations without deep AI expertise to deploy machine learning systems.
Upon returning to Stanford in 2019, she became the founding co-director of the Stanford Institute for Human-Centered Artificial Intelligence (HAI), an explicitly multidisciplinary initiative spanning computer science, social sciences, humanities, law, and medicine—all united by the conviction that AI must serve human flourishing, not vice versa.
Current Work and World Labs
Most recently, Dr. Li co-founded and serves as chief executive officer of World Labs, an AI company focused on spatial intelligence and generative world models. This venture extends her intellectual agenda: if large language models learn patterns over text, world models learn patterns over 3D environments, enabling machines to understand, simulate, and reason about physical and virtual spaces. For robotics, healthcare simulation, autonomous systems, and countless other domains, this represents the next frontier.
Recognition and Influence
Her standing is reflected in numerous accolades: election to the National Academy of Engineering, the National Academy of Medicine (2020), and the American Academy of Arts and Sciences (2021); the Intel Lifetime Achievement Innovation Award in 2023; and inclusion in Time magazine’s 100 Most Influential People in AI. She is colloquially known as the “Godmother of AI.” In 2023, she published a memoir, The Worlds I See: Curiosity, Exploration and Discovery at the Dawn of AI, which chronicles her personal journey and intellectual evolution.
Leading Theorists and Strategic Thinkers: The Landscape of AI and National Strategy
The backdrop to Dr. Li’s statement includes several strands of thought about technology, development, and national strategy:
Economic and Technological Diffusion
- Erik Brynjolfsson and Andrew McAfee (The Second Machine Age, Machine Platform Crowd): These MIT researchers have articulated how technological revolutions create winners and losers, and that policy choices—not technology alone—determine whether gains are broadly shared. They underscore that without intentional intervention, automation and AI tend to concentrate wealth and opportunity.
- Dani Rodrik (Harvard economist): Rodrik’s work on “premature demonetarisation” and structural transformation highlights the risks faced by developing economies when technological progress accelerates faster than institutions can adapt. His analysis supports Dr. Li’s argument: countries must actively build capacity or risk being left behind.
- Mariana Mazzucato (University College London): Mazzucato’s research on the entrepreneurial state emphasises that breakthrough innovations—including AI—depend on public investment in foundational research, education, and infrastructure. Her work buttresses the case for public and private sector partnership.
Artificial Intelligence and Cognition
- Geoffrey Hinton, Yann LeCun, and Yoshua Bengio: The triumvirate of deep learning pioneers recognised that neural networks could scale to superhuman performance in perception and pattern recognition, yet have increasingly stressed that current approaches may be insufficient for general intelligence. Their candour about limitations supports a measured, long-term investment view.
- Stuart Russell (UC Berkeley): Russell has been a prominent voice calling for AI safety and governance frameworks to accompany capability development. His framing aligns with Dr. Li’s insistence that human-centred values must guide AI research and deployment.
Geopolitics and Technology Competition
- Michael Mazarr (RAND Corporation): Mazarr and colleagues have analysed great-power competition in emerging technologies, emphasising that diffusion of capability is inevitable but the pace and terms of diffusion are contestable. Nations that invest in talent pipelines and partnerships will sustain influence; those that isolate will atrophy.
- Kai-Fu Lee: The Taiwanese-American venture capitalist and author (AI Superpowers) has articulated how the US and China are in a competitive race, but also how smaller nations and regions can position themselves through strategic partnerships and focus on applied AI problems relevant to their economies.
- Eric Schmidt (former Google CEO): Schmidt, who participated in the same FII panel as Dr. Li, has emphasised that geopolitical advantage flows to nations with capital markets, advanced chip fabrication (such as Taiwan’s TSMC), and deep talent pools. Yet he has also highlighted pathways for other nations to benefit through partnerships and focused investment in particular domains.
Human-Centred Technology and Inclusive Growth
- Timnit Gebru and Joy Buolamwini: These AI ethics researchers have exposed how AI systems can perpetuate bias and harm marginalised communities. Their work reinforces Dr. Li’s emphasis on human-centred design and inclusive governance. For developing nations, this implies that AI investment must account for local contexts, values, and risks of exclusion.
- Turing Award recipients and foundational figures (such as Barbara Liskov on systems reliability, and Leslie Valiant on learning theory): Their sustained emphasis on rigour, safety, and verifiability underpins the argument that sustainable AI development requires not just speed but also deep technical foundations—something that human capital investment cultivates.
Development Economics and Technology Transfer
- Paul Romer (Nobel laureate): Romer’s work on endogenous growth emphasises that ideas and innovation are the drivers of long-term prosperity. For developing nations, this implies that investment in research capacity, education, and institutional learning—not merely adopting foreign technologies—is essential.
- Ha-Joon Chang: The heterodox development economist has critiqued narratives of “leapfrogging” technology. His argument suggests that nations building indigenous technological ecosystems—through domestic investment in research, venture capital, and entrepreneurship—are more resilient and capable of adapting innovations to local needs.
The Three Pillars: An Unpacking
Dr. Li’s framework is sophisticated precisely because it avoids two traps: technological nationalism (the fantasy that any nation can independently build world-leading AI from scratch) and technological fatalism (the resignation that small and medium-sized economies cannot compete).
Human Capital
The most portable, scalable asset a nation can develop is talent. This encompasses:
- Education pipelines: From primary through tertiary education, with emphasis on mathematics, computer science, and critical thinking
- Research institutions: Universities, national laboratories, and research councils capable of contributing to fundamental and applied AI knowledge
- Retention and diaspora engagement: Policies to keep talented individuals from emigrating, and mechanisms to attract expatriate expertise
- Diversity and inclusion: As Dr. Li has emphasised through her co-founding of AI4ALL (a nonprofit working to increase diversity in AI), innovation benefits from diverse perspectives and draws from broader talent pools
Partnerships
Rather than isolating, Dr. Li advocates for strategic alignment:
- North-South partnerships: Developed nations’ hyperscalers and technology firms partnering with developing economies to establish data centres, training programmes, and applied research initiatives. Saudi Arabia and the UAE have pursued this model successfully
- South-South cooperation: Peer learning and knowledge exchange among developing nations facing similar challenges
- Academic and research collaborations: Open-source tools, shared benchmarks (as exemplified by ImageNet), and collaborative research that diffuse capability globally
- Technology licensing and transfer agreements: Mechanisms by which developing nations can access cutting-edge tools and methods at affordable terms
Technological Stack and Ecosystem
A nation cannot simply purchase AI capability; it must develop home-grown institutional and commercial ecosystems:
- Open-source communities: Participation in and contribution to open-source AI frameworks (PyTorch, TensorFlow, Hugging Face) builds local expertise and reduces dependency on proprietary systems
- Venture capital and startup ecosystems: Policies fostering entrepreneurship in AI applications suited to local economies (agriculture, healthcare, manufacturing)
- Regulatory frameworks: Balanced approaches to data governance, privacy, and AI safety that neither stifle innovation nor endanger citizens
- Domain-specific applied AI: Rather than competing globally in large language models, nations can focus on AI applications addressing pressing local challenges: medical diagnostics, precision agriculture, supply-chain optimisation, or financial inclusion
Why Inaction Is “Macroscopically the Wrong Thing”
Dr. Li’s assertion that not investing in AI would be fundamentally mistaken rests on several converging arguments:
Economic Imperatives
AI is reshaping productivity across sectors. Nations that fail to develop internal expertise will find themselves dependent on foreign technology, unable to adapt solutions to local contexts, and vulnerable to supply disruptions or geopolitical pressure. The competitive advantage flows to early movers and sustained investors.
Employment and Social Cohesion
While AI will displace some jobs, it will create others—particularly for workers skilled in AI-adjacent fields. Nations that invest in reskilling and education can harness these transitions productively. Those that do not risk deepening inequality and social fracture.
Sovereignty and Resilience
Over-reliance on foreign AI systems limits national agency. Whether in healthcare, defence, finance, or public administration, critical systems should rest partly on domestic expertise and infrastructure to ensure resilience and alignment with national values.
Participation in Global Governance
As AI governance frameworks emerge—whether through the UN, regional bodies, or multilateral forums—nations with substantive technical expertise and domestic stakes will shape the rules. Those without will have rules imposed upon them.
The Tension and Its Resolution
Implicit in Dr. Li’s statement is a tension worth articulating: the world cannot support 200 competing AI superpowers, each building independent foundational models. Capital and talent are finite. Yet neither is the world a binary of a few AI leaders and many followers. The resolution lies in specialisation and integration:
- A nation may not lead in large language models but excel in robotics for agriculture
- It may not build chips but pioneer AI applications in healthcare or education
- It may not host hyperscaler data centres but contribute essential research in AI safety or fairness
- It will necessarily depend on global partnerships whilst developing sovereign capacity in domains critical to its citizens
This is neither capitulation nor isolation, but rather a mature acceptance of global interdependence coupled with strategic autonomy in domains of national importance.
Conclusion: The Compass for National Strategy
Dr. Li’s counsel, grounded in decades of research leadership, industrial experience, and global perspective, offers a compass for policymakers navigating the AI era. Investment in human capital, strategic partnerships, and home-grown technological ecosystems is not a luxury or academic exercise—it is fundamental to national competitiveness, prosperity, and agency. The alternative—treating AI as an external force to be passively absorbed—is indeed “macroscopically” mistaken, foreclosing decades of economic opportunity and surrendering the right to shape how this powerful technology serves human flourishing.
Polls
No Results Found
The page you requested could not be found. Try refining your search, or use the navigation above to locate the post.
Services
Global Advisors is different
We help clients to measurably improve strategic decision-making and the results they achieve through defining clearly prioritised choices, reducing uncertainty, winning hearts and minds and partnering to deliver.
Our difference is embodied in our team. Our values define us.
Corporate portfolio strategy
Define optimal business portfolios aligned with investor expectations
BUSINESS UNIT STRATEGY
Define how to win against competitors
Reach full potential
Understand your business’ core, reach full potential and grow into optimal adjacencies
Deal advisory
M&A, due diligence, deal structuring, balance sheet optimisation
Global Advisors Digital Data Analytics
14 years of quantitative and data science experience
An enabler to delivering quantified strategy and accelerated implementation
Digital enablement, acceleration and data science
Leading-edge data science and digital skills
Experts in large data processing, analytics and data visualisation
Developers of digital proof-of-concepts
An accelerator for Global Advisors and our clients
Join Global Advisors
We hire and grow amazing people
Consultants join our firm based on a fit with our values, culture and vision. They believe in and are excited by our differentiated approach. They realise that working on our clients’ most important projects is a privilege. While the problems we solve are strategic to clients, consultants recognise that solutions primarily require hard work – rigorous and thorough analysis, partnering with client team members to overcome political and emotional obstacles, and a large investment in knowledge development and self-growth.
Get In Touch
16th Floor, The Forum, 2 Maude Street, Sandton, Johannesburg, South Africa
+27114616371