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
Podcast – The Real AI Signal from Davos 2026
While the headlines from Davos were dominated by geopolitical conflict and debates on AGI timelines and asset bubbles, a different signal emerged from the noise. It wasn’t about if AI works, but how it is being ruthlessly integrated into the real economy.
In our latest podcast, we break down the “Diffusion Strategy” defining 2026.
3 Key Takeaways:
- China and the “Global South” are trying to leapfrog: While the West debates regulation, emerging economies are treating AI as essential infrastructure.
- China has set a goal for 70% AI diffusion by 2027.
- The UAE has mandated AI literacy in public schools from K-12.
- Rwanda is using AI to quadruple its healthcare workforce.
- The Rise of the “Agentic Self”: We aren’t just using chatbots anymore; we are employing agents. Entrepreneur Steven Bartlett revealed he has established a “Head of Experimentation and Failure” to use AI to disrupt his own business before competitors do. Musician will.i.am argued that in an age of predictive machines, humans must cultivate their “agentic self” to handle the predictable, while remaining unpredictable themselves.
- Rewiring the Core: Uber’s CEO Dara Khosrowshahi noted the difference between an “AI veneer” and a fundamental rewire. It’s no longer about summarising meetings; it’s about autonomous agents resolving customer issues without scripts.
The Global Advisors Perspective: Don’t wait for AGI. The current generation of models is sufficient to drive massive value today. The winners will be those who control their “sovereign capabilities” – embedding their tacit knowledge into models they own.
Read our original perspective here – https://with.ga/w1bd5
Listen to the full breakdown here – https://with.ga/2vg0z

Strategy Tools
PODCAST: Effective Transfer Pricing
Our Spotify podcast discusses how to get transfer pricing right.
We discuss effective transfer pricing within organizations, highlighting the prevalent challenges and proposing solutions. The core issue is that poorly implemented internal pricing leads to suboptimal economic decisions, resource allocation problems, and interdepartmental conflict. The hosts advocate for market-based pricing over cost recovery, emphasizing the importance of clear price signals for efficient resource allocation and accurate decision-making. They stress the need for service level agreements, fair cost allocation, and a comprehensive process to manage the political and emotional aspects of internal pricing, ultimately aiming for improved organizational performance and profitability. The podcast includes case studies illustrating successful implementations and the authors’ expertise in this field.
Read more from the original article.

Fast Facts
Fast Fact: Great returns aren’t enough
Key insights
It’s not enough to just have great returns – top-line growth is just as critical.
In fact, S&P 500 investors rewarded high-growth companies more than high-ROIC companies over the past decade.
While the distinction was less clear on the JSE, what is clear is that getting a balance of growth and returns is critical.
Strong and consistent ROIC or RONA performers provide investors with a steady flow of discounted cash flows – without growth effectively a fixed-income instrument.
Improvements in ROIC through margin improvements, efficiencies and working-capital optimisation provide point-in-time uplifts to share price.
Top-line growth presents a compounding mechanism – ROIC (and improvements) are compounded each year leading to on-going increases in share price.
However, without acceptable levels of ROIC, the benefits of compounding will be subdued and share price appreciation will be depressed – and when ROIC is below WACC value will be destroyed.
Maintaining high levels of growth is not as sustainable as maintaining high levels of ROIC – while both typically decline as industries mature, growth is usually more affected.
Getting the right balance between ROIC and growth is critical to optimising shareholder value.
Selected News
Quote: Andrew Ng – AI guru, Coursera founder
“My most productive developers are actually not fresh college grads; they have 10, 20 years of experience in coding and are on top of AI… one tier down… is the fresh college grads that really know how to use AI… one tier down from that is the people with 10 years of experience… the least productive that I would never hire are the fresh college grads that… do not know AI.” – Andrew Ng – AI guru, Coursera founder
In a candid discussion at the World Economic Forum 2026 in Davos, Andrew Ng unveiled a provocative hierarchy of developer productivity, prioritising AI fluency over traditional experience. Delivered during the session ‘Corporate Ladders, AI Reshuffled,’ this perspective challenges conventional hiring norms amid AI’s rapid evolution. Ng’s remarks, captured in a live YouTube panel on 19 January 2026, underscore how artificial intelligence is redefining competence in software engineering.
Andrew Ng: The Architect of Modern AI Education
Andrew Ng stands as one of the foremost pioneers in artificial intelligence, blending academic rigour with entrepreneurial vision. A British-born computer scientist, he earned his PhD from the University of California, Berkeley, and later joined Stanford University, where he co-founded the Stanford AI Lab. Ng’s breakthrough came with his development of one of the first large-scale online courses on machine learning in 2011, which attracted over 100,000 students and laid the groundwork for massive open online courses (MOOCs).
In 2012, alongside Daphne Koller, he co-founded Coursera, transforming global access to education by partnering with top universities to offer courses in AI, data science, and beyond. The platform now serves millions, democratising skills essential for the AI age. Ng also led Baidu’s AI Group as Chief Scientist from 2014 to 2017, scaling deep learning applications at an industrial level. Today, as founder of DeepLearning.AI and managing general partner at AI Fund, he invests in and educates on practical AI deployment. His influence extends to Google Brain, which he co-founded in 2011, pioneering advancements in deep learning that power today’s generative models.
Ng’s Davos appearances, including 2026 interviews with Moneycontrol and others, consistently advocate for AI optimism tempered by pragmatism. He dismisses fears of an AI bubble in applications while cautioning on model training costs, and stresses upskilling: ‘A person that uses AI will be so much more productive, they will replace someone that doesn’t use AI.’1,3
Context of the Quote: AI’s Disruption of Corporate Ladders
The quote emerged from WEF 2026’s exploration of how AI reshuffles organisational hierarchies and talent pipelines. Ng argued that AI tools amplify human capabilities unevenly, creating a new productivity spectrum. Seasoned coders who master AI-such as large language models for code generation-outpace novices, while AI-illiterate veterans lag. This aligns with his broader Davos narrative: AI handles 30-40% of many jobs’ tasks, leaving humans to focus on the rest, but only if they adapt.3
Ng highlighted real-world shifts in Silicon Valley, where AI inference demand surges, throttling teams due to capacity limits. He urged infrastructure build-out and open-source adoption, particularly for nations like India, warning against vendor lock-in: ‘If it’s open, no one can mess with it.’2 Fears of mass job losses? Overhyped, per Ng-layoffs stem more from post-pandemic corrections than automation.3
Leading Theorists on AI, Skills, and Future Work
Ng’s views echo and extend seminal theories on technological unemployment and skill augmentation.
- David Autor: MIT economist whose ‘skill-biased technological change’ framework (1990s onwards) posits automation displaces routine tasks but boosts demand for non-routine cognitive skills. Ng’s hierarchy mirrors this: AI supercharges experienced workers’ judgement while sidelining routine coders.3
- Erik Brynjolfsson and Andrew McAfee: In ‘The Second Machine Age’ (2014), they describe how digital technologies widen productivity gaps, favouring ‘superstars’ who leverage tools. Ng’s top tier-AI-savvy veterans-embodies this ‘winner-takes-more’ dynamic in coding.1
- Daron Acemoglu and Pascual Restrepo: Their ‘task-based’ model (2010s) quantifies automation’s impact: AI automates coding subtasks, but complements human oversight. Ng’s 30-40% task automation estimate directly invokes this, predicting productivity booms for adapters.3
- Fei-Fei Li: Ng’s Stanford colleague and ‘Godmother of AI Vision,’ she emphasises human-AI collaboration. Her work on multimodal AI reinforces Ng’s call for developers to integrate AI into workflows, not replace manual toil.
- Yann LeCun, Geoffrey Hinton, and Yoshua Bengio: The ‘Godfathers of Deep Learning’ (Turing Award 2018) enabled tools like those Ng champions. Their foundational neural network advances underpin modern code assistants, validating Ng’s tiers where AI fluency trumps raw experience.
These theorists collectively frame AI as an amplifier, not annihilator, of labour-resonating with Ng’s prescription for careers: master AI or risk obsolescence. As workflows agenticise, coding evolves from syntax drudgery to strategic orchestration.
Implications for Careers and Skills
Ng’s ladder demands immediate action: prioritise AI literacy via platforms like Coursera, fine-tune open models like Llama-4 or Qwen-2, and rebuild talent pipelines around meta-skills like prompt engineering and bias auditing.2,5 For IT powerhouses like India’s $280 billion services sector, upskilling velocity is non-negotiable.6 In this reshuffled landscape, productivity hinges not on years coded, but on AI mastery.
References
2. https://www.aicerts.ai/news/andrew-ng-open-source-ai-india-call-resonates-at-davos/
4. https://www.youtube.com/watch?v=oQ9DTjyfIq8
5. https://globaladvisors.biz/2026/01/23/the-ai-signal-from-the-world-economic-forum-2026-at-davos/

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