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: Leading a deliberate life
By Marc Wilson
Marc is a partner at Global Advisors and based in Johannesburg, South Africa
Download this article at https://globaladvisors.biz/blog/2018/06/26/leading-a-deliberate-life/.
Picket fences. Family of four. Management position.
Mid-life crisis. Meaning. Purpose.
Someone once said that, “At 18, I had all the answers. At 35, I realised I didn’t know the question.”
Serendipity has a lot going for it. Many people might sail through life taking what comes and enjoying the moment. Others might be open to chance and have nothing go right for them.
Some people might strive to achieve, realise rare successes and be bitterly unhappy. Others might be driven and enjoy incredible success and fulfilment.
Perhaps the majority of us become beholden to the momentum of our lives.
We might study, start a career, marry, buy a dream house, have children, send them to a top school. Those steps make up components of many of our dreams. They are steps that may define each subsequent choice. As I discussed this with a friend recently, he remarked that few of these steps had been subject of deliberations in his life – increasingly these steps were the outcome of momentum. Each will shape every step he takes for the rest of his life. He would not have things any other way, but if he knew what he knows now, he might have been more deliberate about choice and consequence…..
Read more at https://globaladvisors.biz/blog/2018/06/26/leading-a-deliberate-life/
.
Strategy Tools
PODCAST: Strategy Tools: Growth, Profit or Returns?
Our Spotify podcast explores the relationship between Return on Net Assets (RONA) and growth, arguing that both are essential for shareholder value creation. The hosts contend that focusing solely on one metric can be detrimental, and propose a framework for evaluating business portfolios based on their RONA and growth profiles. This approach involves plotting business units on a “market-cap curve” to identify value-accretive and value-destructive segments.
The podcast also addresses the impact of economic downturns on portfolio management, suggesting strategies for both offensive and defensive approaches. The core argument is that companies should aim to achieve a balance between RONA and growth, acknowledging that both are essential for long-term shareholder value creation.
Read more from the original article – https://globaladvisors.biz/2020/08/04/strategy-tools-growth-profit-or-returns/

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
Term: Recursive Language Model (RLM)
“A Recursive Language Model (RLM) is an AI inference strategy where a large language model (LLM) is granted the ability to programmatically interact with and recursively call itself or smaller helper models to solve complex tasks and process extremely long inputs.” – Recursive Language Model (RLM)
A **Recursive Language Model (RLM)** is an innovative inference strategy that empowers large language models (LLMs) to treat input contexts not as static strings but as dynamic environments they can actively explore, decompose, and recursively process.1,3,4 This approach fundamentally shifts AI from passive text processing to active problem-solving, enabling the handling of extremely long inputs, complex reasoning tasks, and structured outputs without being constrained by traditional context window limits.1,6
At its core, an RLM operates within a Python Read-Eval-Print Loop (REPL) environment where the input context is stored as a programmable variable.1,2,3 The model begins with exploration and inspection, using tools like string slicing, regular expressions, and keyword searches to scan and understand the data structure actively rather than passively reading it.1 It then performs task decomposition, breaking the problem into smaller subtasks that fit within standard context windows, with the model deciding the splits based on its discoveries.1,3
The hallmark is recursive self-calls, where the model invokes itself (or smaller helper models) on each subtask, forming a tree of reasoning that aggregates partial results into variables within the REPL.1,4 This is followed by aggregation and synthesis, combining outputs programmatically into lists, tables, or documents, and verification and self-checking through re-runs or cross-checks for reliability.1 Unlike traditional LLMs that process a single forward pass on tokenised input, RLMs grant the model ‘hands and eyes’ to query itself programmatically, such as result = rlm_query(sub_prompt), transforming context from ‘input’ to ‘environment’.1,3
RLMs address key limitations like ‘context rot’-degradation in long-context performance-and scale to effectively unlimited lengths (over 10 million tokens tested), outperforming baselines by up to 114% on benchmarks without fine-tuning, via prompt engineering alone.3,6,2 They differ from agentic systems by decomposing context adaptively rather than predefined tasks, and from reasoning models by scaling through recursive decomposition.6
Key Theorist: Alex L. Zhang and the MIT Origins
The primary theorist behind RLMs is **Alex L. Zhang**, a researcher affiliated with MIT, who co-authored the seminal work proposing RLMs as a general inference framework.3,4,8 In his detailed blog and the arXiv paper ‘Recursive Language Models’ (published around late 2025), Zhang articulates the vision: enabling LLMs to ‘recursively call themselves or other LLMs’ to process unbounded contexts and mitigate degradation.3,4 His implementation uses GPT-5 or GPT-5-mini in a Python REPL, allowing adaptive chunking and recursion at test time.3
Alex L. Zhang’s biography reflects a deep expertise in AI scaling and inference innovations. Active in 2025 through platforms like his GitHub blog (alexzhang13.github.io), he focuses on practical advancements in language model capabilities, particularly long-context handling.3 While specific early career details are sparse in available sources, his work builds on MIT’s disruptive ethos-echoed in proposals like ‘why not let the model read itself?’-positioning him as a key figure in the 2026 paradigm shift towards recursive AI architectures.1,8 Zhang’s contributions emphasise test-time compute scaling, distinguishing RLMs from mere architectural changes by framing them as a ‘thin wrapper’ around standard LLMs that reframes them as stateful programmes.5
Experimental validations in Zhang’s framework demonstrate RLMs’ superiority, such as dramatically improved accuracy on pairwise comparison tasks (from near-zero to over 58%) and spam classification in massive prompts.2,4 His ideas have sparked widespread discussion, with sources hailing RLMs as ‘the ultimate evolution of AI’ and a ‘game-changer for 2026’.1,2,7
References
1. https://gaodalie.substack.com/p/rlm-the-ultimate-evolution-of-ai
3. https://alexzhang13.github.io/blog/2025/rlm/
4. https://arxiv.org/html/2512.24601v1
5. https://datasciencedojo.com/blog/what-are-recursive-language-models/
7. https://www.primeintellect.ai/blog/rlm

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
