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An AI-native strategy firmGlobal Advisors: a consulting leader in defining quantified strategy, decreasing uncertainty, improving decisions, achieving measureable results.
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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.
Our latest
Thoughts
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.
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
Term: Tensor Processing Unit (TPU)
“A Tensor Processing Unit (TPU) is an application-specific integrated circuit (ASIC) custom-designed by Google to accelerate machine learning (ML) and artificial intelligence (AI) workloads, especially those involving neural networks.” – Tensor Processing Unit (TPU)
A Tensor Processing Unit (TPU) is an application-specific integrated circuit (ASIC) custom-designed by Google to accelerate machine learning (ML) and artificial intelligence (AI) workloads, particularly those involving neural networks and matrix multiplication operations.1,2,4,6
Core Architecture and Functionality
TPUs excel at high-throughput, parallel processing of mathematical tasks such as multiply-accumulate (MAC) operations, which form the backbone of neural network training and inference. Each TPU features a Matrix Multiply Unit (MXU)—a systolic array of arithmetic logic units (ALUs), typically configured as 128×128 or 256×256 grids—that performs thousands of MAC operations per clock cycle using formats like 8-bit integers, BFloat16, or floating-point arithmetic.1,2,5,9 Supporting components include a Vector Processing Unit (VPU) for non-linear activations (e.g., ReLU, sigmoid) and High Bandwidth Memory (HBM) to minimise data bottlenecks by enabling rapid data retrieval and storage.2,5
Unlike general-purpose CPUs or even GPUs, TPUs are purpose-built for ML models relying on matrix processing, large batch sizes, and extended training periods (e.g., weeks for convolutional neural networks), offering superior efficiency in power consumption and speed for tasks like image recognition, natural language processing, and generative AI.1,3,6 They integrate seamlessly with frameworks such as TensorFlow, JAX, and PyTorch, processing input data as vectors in parallel before outputting results to ML models.1,4
Key Applications and Deployment
- Cloud Computing: TPUs power Google Cloud Platform (GCP) services for AI workloads, including chatbots, recommendation engines, speech synthesis, computer vision, and products like Google Search, Maps, Photos, and Gemini.1,2,3
- Edge Computing: Suitable for real-time ML at data sources, such as IoT in factories or autonomous vehicles, where high-throughput matrix operations are needed.1
TPUs support both training (e.g., model development) and inference (e.g., predictions on new data), with pods scaling to thousands of chips for massive workloads.6,7
Development History
Google developed TPUs internally from 2015 for TensorFlow-based neural networks, deploying them in data centres before releasing versions for third-party use via GCP in 2018.1,4 Evolution includes shifts in array sizes (e.g., v1: 256×256 on 8-bit integers; later versions: 128×128 on BFloat16; v6: back to 256×256) and proprietary interconnects for enhanced scalability.5,6
Best Related Strategy Theorist: Norman Foster Ramsey
The most pertinent strategy theorist linked to TPU development is Norman Foster Ramsey (1915–2011), a Nobel Prize-winning physicist whose foundational work on quantum computing architectures and coherent manipulation of quantum states directly influenced the parallel processing paradigms underpinning TPUs. Ramsey’s concepts of separated oscillatory fields—a technique for precisely controlling atomic transitions using microwave pulses separated in space and time—paved the way for systolic arrays and matrix-based computation in specialised hardware, which TPUs exemplify through their MXU grids for simultaneous MAC operations.5 This quantum-inspired parallelism optimises energy efficiency and throughput, mirroring Ramsey’s emphasis on minimising decoherence (data loss) in high-dimensional systems.
Biography and Relationship to the Term: Born in Washington, D.C., Ramsey earned his PhD from Columbia University in 1940 under I.I. Rabi, focusing on molecular beams and magnetic resonance. During World War II, he contributed to radar and atomic bomb research at MIT’s Radiation Laboratory. Post-war, as a Harvard professor (1947–1986), he pioneered the Ramsey method of separated oscillatory fields, earning the 1989 Nobel Prize in Physics for enabling atomic clocks and quantum computing primitives. His 1950s–1960s work on quantum state engineering informed ASIC designs for tensor operations; Google’s TPU team drew on these principles for weight-stationary systolic arrays, reducing data movement akin to Ramsey’s coherence preservation. Ramsey advised early quantum hardware initiatives at Harvard and Los Alamos, influencing strategists in custom silicon for AI acceleration. He lived to 96, authoring over 250 papers and mentoring figures in computational physics.1,5
References
1. https://www.techtarget.com/whatis/definition/tensor-processing-unit-TPU
2. https://builtin.com/articles/tensor-processing-unit-tpu
3. https://www.iterate.ai/ai-glossary/what-is-tpu-tensor-processing-unit
4. https://en.wikipedia.org/wiki/Tensor_Processing_Unit
5. https://blog.bytebytego.com/p/how-googles-tensor-processing-unit
6. https://cloud.google.com/tpu
7. https://docs.cloud.google.com/tpu/docs/intro-to-tpu
8. https://www.youtube.com/watch?v=GKQz4-esU5M
9. https://lightning.ai/docs/pytorch/1.6.2/accelerators/tpu.html
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