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Due Diligence

Your due diligence is probably wrong

Global Advisors: a consulting leader in defining quantified strategy, decreasing uncertainty, improving decisions, achieving measureable results.

Learn MoreThe Global Advisors due diligence practice

Our latest perspective - What's behind under-performing listed companies?

Outperform through the downturn

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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.

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Thoughts

Global Advisors’ Thoughts: Outperforming through the downturn AND the cost of ignoring full potential

Global Advisors’ Thoughts: Outperforming through the downturn AND the cost of ignoring full potential

Press drew attention last year to a slew of JSE-listed companies whose share prices had collapsed over the past few years. Some were previous investor darlings. Analysis pointed to a toxic combination of decreasing earnings growth and increased leverage. While this might be a warning to investors of a company in trouble, what fundamentals drive this combination?

In our analysis, company expansion driven by the need to compensate for poor performance in their core business is a typical driver of exactly this outcome.

This article was written in January 2020 but publication was delayed due to the outbreak of Covid-19. Five months after South Africa’s first case, we update our analysis and show that core-based companies outperformed diverse peers by 29% over the period.

Management should always seek to reach full potential in their core business. Attempts to expand should be to a clearly logical set of adjacencies to which they can apply their capabilities using a repeatable business model.

In the article “Steinhoff, Tongaat, Omnia… Here’s the dead giveaway that you should have avoided these companies, says an asset manager,” (Business Insider SA, Jun 11, 2019) Helena Wasserman lists a number of Johannesburg Stock Exchange (JSE) listed shares that have plummeted in recent years.

In many cases these companies’ corresponding sectors have been declining. However, in most of the sectors there is at least one company that has outperformed the rest. What is it about these outperformers that distinguishes them from the rest?

The outperformers have typically shown strong financial performance – be that Growth, ROE, ROA, RONA or Asset Turnover – and varying degrees of leverage. However, performance against these metrics is by no means consistent – see our analysis.

What is consistent is that the outperformers all show clearly delineated core businesses and ongoing growth towards full potential in these businesses alongside growth into clear adjacencies that protect, enhance and leverage the core. In some cases, the core may have been or is currently being redefined, typically through gradual, step-wise extension along logical adjacencies. Redefinition is particularly important in light of the digital transformation seen in many industries. The outperformers are very seldom diversified across unrelated business segments – although isolated examples such as Bidvest clearly exist in other sectors.

Analysis of the over- and underperformers in the sectors highlighted in the article shows that those following a clear core-based strategy have typically outperformed peers through the initial months of the downturn caused by the Covid-19 outbreak.

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Strategy Tools

PODCAST: Effective Transfer Pricing

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.

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Fast Facts

Fast Fact: Great returns aren’t enough

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.

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Selected News

Quote: Andrej Karpathy – Ex-OpenAI, Ex-Tesla AI

Quote: Andrej Karpathy – Ex-OpenAI, Ex-Tesla AI

“AI is so wonderful because there have been a number of seismic shifts where the entire field has suddenly looked a different way. I’ve maybe lived through two or three of those. I still think there will continue to be some because they come with almost surprising regularity.” – Andrej Karpathy – Ex-OpenAI, Ex-Tesla AI

Andrej Karpathy, one of the most recognisable figures in artificial intelligence, has spent his career at the epicentre of the field’s defining moments in both research and large-scale industry deployment.

Karpathy’s background is defined by deep technical expertise and a front-row seat to AI’s rapid evolution. Having completed his PhD at Stanford and held pivotal research positions, he worked alongside Geoffrey Hinton at the University of Toronto during the early surge of deep learning. His career encompasses key roles at Tesla, where he led the Autopilot vision team, and at OpenAI, contributing to some of the world’s most prominent large language models and generative AI systems. This vantage point has allowed him to participate in, and reflect upon, the discipline’s “seismic shifts”.

Karpathy’s narrative has been shaped by three inflection points:

  • The emergence of deep neural networks from a niche field to mainstream AI, spearheaded by the success of AlexNet and the subsequent shift of the research community toward neural architectures.
  • The drive towards agent-based systems, with early enthusiasm for reinforcement learning (RL) and game-based environments (such as Atari and Go). Karpathy himself was cautious about the utility of games as the true path to intelligence, focusing instead on agents acting within the real digital world.
  • The rise of large language models (LLMs)—transformers trained on vast internet datasets, shifting the locus of AI from task-specific systems to general-purpose models with the ability to perform a broad suite of tasks, and in-context learning.

His reflection on these ‘regular’ paradigm shifts arises from lived experience: “I’ve maybe lived through two or three of those. I still think there will continue to be some because they come with almost surprising regularity.” These moments recalibrate assumptions, redirect research priorities, and set new benchmarks for capability. Karpathy’s practical orientation—building “useful things” rather than targeting biological intelligence or pure AGI—shapes his approach to both innovation and scepticism about hype.

Context of the Quote
In his conversation with podcaster Dwarkesh Patel, Karpathy elaborates on the recurring nature of breakthroughs. He contrasts AI’s rapid, transformative leaps with other scientific fields, noting that in machine learning, scaling up data, compute, and novel architectures can yield abrupt improvements—yet each wave often triggers both excessive optimism and later recalibration. A major point he raises is the lack of linearity: the field does not “smoothly” approach AGI, but rather proceeds via discontinuities, often catalysed by new ideas or techniques that were previously out of favour or overlooked.

Karpathy relates how, early in his career, neural networks were a marginal interest and large-scale “representation learning” was only beginning to be considered viable by a minority in the community. With the advent of AlexNet, the landscape shifted overnight, rapidly making previous assumptions obsolete. Later, the pursuit of RL-driven agents led to a phase where entire research agendas were oriented toward gameplay and synthetic environments—another phase later superseded by the transformer revolution and language models. Karpathy reflects candidly on earlier missteps, as well as the discipline’s collective tendency to over- or under-predict the timetable and trajectory of progress.

Leading Theorists and Intellectual Heritage
The AI revolutions Karpathy describes are inseparable from the influential figures and ideas that have shaped each phase:

  • Geoffrey Hinton: Hailed as the “godfather of AI”, Hinton was instrumental in deep learning’s breakthrough, advancing techniques for training multilayered neural networks and championing representation learning against prevailing orthodoxy.
  • Yann LeCun: Developed convolutional neural networks (CNNs), foundational for computer vision and the 2010s wave of deep learning success.
  • Yoshua Bengio: Co-architect of the deep learning movement and a key figure in developing unsupervised and generative models.
  • Richard Sutton: Principal proponent of reinforcement learning, Sutton articulated the value of “animal-like” intelligence: learning from direct interaction with environments, reward, and adaptation. Sutton’s perspective frequently informs debates about the relationship between model architectures and living intelligence, encouraging a focus on agents and lifelong learning.

Karpathy’s own stance is partly a pragmatic response to this heritage: rather than pursuing analogues of biological brains, he views the productive path as building digital “ghosts”—entities that learn by imitation and are shaped by patterns in data, rather than evolutionary processes.

Beyond individual theorists, the field’s quantum leaps are rooted in a culture of intellectual rivalry and rapid intellectual cross-pollination:

  • The convolutional and recurrent networks of the 2010s pushed the boundaries of what neural networks could do.
  • The development and scaling of transformer-based architectures (as in Google’s “Attention is All You Need”) dramatically changed both natural language processing and the structure of the field itself.
  • The introduction of algorithms for in-context learning and large-scale unsupervised pre-training marked a break with hand-crafted representation engineering.

The Architecture of Progress: Seismic Shifts and Pragmatic Tension
Karpathy’s insight is that these shifts are not just about faster hardware or bigger datasets; they reflect the field’s unique ecology—where new methods can rapidly become dominant and overturn accumulated orthodoxy. The combination of open scientific exchange, rapid deployment, and intense commercialisation creates fertile ground for frequent realignment.

His observation on the “regularity” of shifts also signals a strategic realism: each wave brings both opportunity and risk. New architectures (such as transformers or large reinforcement learning agents) frequently overshoot expectations before their real limitations become clear. Karpathy remains measured on both promise and limitation—anticipating continued progress, but cautioning against overpredictions and hype cycles that fail to reckon with the “march of nines” needed to reach true reliability and impact.

Closing Perspective
The context of Karpathy’s quote is an AI ecosystem that advances not through steady accretion, but in leaps—each driven by conceptual, technical, and organisational realignments. As such, understanding progress in AI demands both technical literacy and historical awareness: the sharp pivots that have marked past decades are likely to recur, with equally profound effects on how intelligence is conceived, built, and deployed.

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