Due Diligence

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

Strategy Tools

Strategy tools: Effective transfer pricing

So much has been written about transfer pricing. Yet it remains a bone of contention in almost every organisation. Transfer pricing is not merely a rational challenge – it often raises the emotions of internal service users and providers who argue regarding scope, quality, price and value.

We have found that effective transfer pricing relies on some fairly simple best practices and critical success factors.

Many organisations recover costs as a regular ‘below-the-line’ deduction from operating division income statements. In our experience, charge out is almost always preferable. This results in internal value judgements and negotiation regarding delivery happening closer to time of use.

Internal prices / cost recovery plays a crucial role within an organisation: it ‘price signals’ to the buyer and the supplier of the service. Buyers make economic use decisions and suppliers make resource and capacity decisions. This fundamental function and consequence governs the optimal implementation of internal pricing / cost recovery.

We have typically seen that the realisation that internal pricing plays this role and the consequences of poor implementation are not well understood.

Results of poor transfer pricing implementation

Sub-optimal economic use decisions

Where costs / prices are higher than they should be, buyers pass this on as an inflated cost to their customers, experience margin squeeze, or utilise less of the service than they might have.
Strategically this can lead to incorrect decisions regarding the provision of services to the market and loss of market share.
Where costs / prices are lower than they should be, this can lead to overuse of a product or service and poor cost recovery from external customers.
Strategically this can result in the over promotion and sales of products and services that are achieving lower margins than thought, or that might even be making losses.

Sub-optimal investment and resourcing decisions

Incorrect pricing can lead to over- or under-investment in capacity and product or service quality. Further, the resourcing decisions will be incorrect should the price signal to the supplier be incorrect.

Political and emotional argument

Where buyers are unable to obtain assurance that an internal price is correct, there is typically resentment regarding the cost of the internal product and service and the sheltered position employees of the internal service provider occupy – in the buyer’s eyes free from commercial pressures.
Buyers and suppliers typically also argue regarding the quality of the service or product relative to the price paid.
Suppliers may react to criticism claiming their product or service is strategic in nature and refute its availability in the external markets.

Poor product / service quality

Poor price signals will result in lack of comparable product and service quality benchmarks. This can result in ‘gold-plating’ or poor-quality product and service provision.

Fast Facts

Fast Fact: The rate of technology adoption exploded in the 1990s

The 1990s were an inflection point in the adoption of new technologies. While radio showed fast adoption in the 1920s, new technologies introduced post 2010 had reached penetrations of more than 30% of the United States population within 3 years from launch. PCs...

Selected News

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