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BRICS: A new Bretton Woods this summer?

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BRICS: A new Bretton Woods this summer?
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8:00 p.m. ▪
10
min read ▪ by
Nicholas T.

A new Bretton Woods on August 22? The BRICS meeting could be the biggest currency upheaval since 1971.

BRICS

BRICS alliance swells

Brazil, Russia, India, China and South Africa could unveil in Durban a new international reserve currency (or several) in order to do without the dollar. Dedollarization is accelerating.

The world is not prepared for this geopolitical and monetary shock wave which will result in more inflation in the West and particularly in the United States.

In addition, the BRICS will announce an enlargement. We will soon have to talk about the BRICS+. Eight nations have formally applied for membership. Algeria, Argentina, Bahrain, Egypt, Indonesia, Iran, Saudi Arabia and United Arab Emirates.

Seventeen others have expressed interest. Afghanistan, Bangladesh, Belarus, Kazakhstan, Mexico, Nicaragua, Nigeria, Pakistan, Senegal, Sudan, Syria, Thailand, Tunisia, Turkey, Uruguay, Venezuela and Zimbabwe.

Knowing that :

-Saudi Arabia, Iran and Russia have 30% of the oil reserves and 46% of the gas.

-Russia, China, Brazil and India represent 30% of the world’s land surface and 50% of the world’s wheat and rice production.

-China, India, Brazil and Russia have a combined population of 3.2 billion people, or 40% of the world’s population.

-China, India, Brazil, Russia and Saudi Arabia have a combined GDP of $29 trillion, or 28% of global GDP. Even more at purchasing power parity. Russia and China possess two of the three largest nuclear arsenals in the world.

BRICS currency

In every way – population, landmass, energy production, GDP, food production and nuclear weapons – the BRICS are ahead. The Middle Kingdom has also fallen behind technologically, although it remains a bit behind in terms of semiconductors. Russia, China, Iran and North Korea have hypersonic missiles, unlike the West.

By acting in concert, the BRICS constitute one of the poles of a multipolar world. If a new currency is announced in August, it will have every chance of being a success, at least within the countries mentioned.

The global desire to abandon the dollar as the international currency par excellence is not new. The difference today is that Russia went to war. And while Moscow holds Europe at bay, many realize that now is the time to emancipate.

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Dubai now accepts the yuan as payment for its oil exports. Saudi Arabia and China have discussed similar agreements, and we bet that Riyadh will accept the yuan once it joins BRICS. China and Brazil recently agreed to trade in their own currencies. And again this week, we learn that Pakistan is buying Russian oil in yuan.

Very good, but trading in national currencies has its limits. It is rare for two countries to have equal trade. Typically, one has a trade deficit and the other a surplus. This is the case of Russia and India, which has been buying a lot of Russian oil since the start of the war in Ukraine. However, Russia already does not want any more rupees.

If the dollar is no longer an option, a substitute must be found that everyone will accept. Hence this very important BRICS+ summit this summer in South Africa from August 22 to 24.

What type of currency exactly?

Some countries are considering a gold-backed stablecoin, which will make bitcoiners smile…

Others argue for a currency pegged to a basket of commodities like oil, wheat, copper and other essentials that are at the heart of globalization.

Anyway, most likely this currency will not be accessible to anyone. There will be no BRICS+ banknotes for daily transactions.

It will be a centralized digital currency, likely held by the New Development Bank (the “BRICS bank”). And just like in 1971 with the dollar, you can be sure that sooner or later this currency will end up devaluing.

Moreover, according to the latest information provided by the BRICS working groups, this basket is encountering the same problems as those encountered by John Maynard Keynes during the Bretton Woods meetings in 1944.

The idea is not new. Keynes originally proposed a basket-of-commodities approach to a global currency he called Bancor. Except that the world raw materials included in a basket are not fungible (there are more than 70 qualities of oil…).

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In the end, Keynes felt that a basket of commodities was unnecessary and that simply using gold was the easiest way to anchor a currency (gold standard).

It is therefore possible that the new BRICS+ currency will be linked to a weight of gold. And that this gold standard is doomed to the same fate as the previous one.

This solution benefits Russia and China, which are the two largest gold producers in the world. They “officially” rank sixth and seventh respectively out of the 100 nations with gold reserves.

And those who have no gold? What about Bitcoin?

A gold standard could raise teeth in countries that are not lucky enough to have gold mines. Conversely, Bitcoin can be mined by any country. All you need is electricity. This is a significant asset, in addition to traveling for free at the speed of light.

Not to mention the fact that it is possible to mine ever more gold. On the contrary, the issuance of bitcoins decreases by half every four years. As Michael Saylor said at the BTCPrague conference, “the half-life of gold is 35 years”. In other words, the stock of gold doubles every 35 years.

Conversely, more than 92% of the 21 million BTC have already been issued and very few countries have them. It would be a new start on equal terms with a currency accessible to all.

The scarcity of a reserve currency is crucial. It should be understood that countries can use the payment currency of their choice. It doesn’t have to be the dollar. In contrast, a reserve currency must be a stable currency.

Reserves are essentially the savings accounts of sovereign nations that have earned them through their trade surpluses. These balances are not held in the form of notes, but in the form of financial securities (equities of multinationals and mainly sovereign debt).

When we say that the dollar is the main reserve currency, it means that central banks hold US Treasury bonds which pay interest. According to the latest IMF figures, 60% of global reserves are currently denominated in dollars (~$6,471 billion, the lowest in 25 years).

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Not so easy to replace the dollar

It is difficult to offer a reserve currency without it allowing investment in a large and open sovereign bond market. No country in the world comes close to the US Treasury market in terms of size, variety of maturities, liquidity, derivatives etc.

The BRICS+, in addition to offering a currency convertible into gold, will have to offer an alternative market yielding higher interest than those found in the United States. But does China want to open its markets to the rest of the world? Unlikely, but who knows…

Finally, it is also reasonable to think that interest rates will stay below inflation for a long time. As Michael Saylor says, “No one can stop inflation. I could put you in charge of the world you couldn’t stop inflation”.

For what ? Simply because we live on a finite planet. Two things seem increasingly certain in the absence of a technological breakthrough on energy:

1) Decline in productivity (and therefore profits) due to limits to growth (peak oil), which will weigh on the dividends of multinationals;
2) Interest rates close to 0% due to debt burden.

The time has come to temper hopes regarding the debt and the stock market. Everyone will soon be rushing to a store of absolute value, no matter that there is no “yield”.

The BRICS are unlikely to officially adopt bitcoin this summer. That said, it is rumored that the UAE is already buying Russian oil in bitcoin…

A multipolar world is a boon for bitcoin which will eventually step into the breach, like in El Salvador.

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Nicolas T. avatar

Nicholas T.

Journalist reporting on the Bitcoin revolution. My papers deal with bitcoin through geopolitical, economic, and libertarian prisms.

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The machine learning certifications tech companies want

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The machine learning certifications tech companies want
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In addition, the certification exam evaluates a candidate’s ability to implement strategies for deploying machine learning models. Finally, candidates are assessed on their ability to build monitoring solutions to detect data drift. Individuals who pass the certification exam can be expected to perform advanced machine learning engineering tasks using Databricks Machine Learning.

Google Cloud Professional Machine Learning Engineer

A professional machine learning engineer builds, evaluates, produces, and optimizes machine learning models using Google Cloud technologies and has knowledge of proven models and techniques, according to Google Cloud. The Google Cloud Professional Machine Learning Engineer certification ensures a machine learning engineer can handle large, complex datasets and create repeatable, reusable code; consider responsible AI and fairness throughout the machine learning model development process; and collaborate closely with other job roles to ensure long-term success of machine learning-based applications.

The Google Cloud Professional certified machine learning engineer also must have strong programming skills and experience with data platforms and distributed data processing tools, Google Cloud says. This professional is also expected to be proficient in the areas of model architecture, data and machine learning pipeline creation, and metrics interpretation. By training, retraining, deploying, scheduling, monitoring, and improving models, the machine learning engineer designs and creates scalable solutions.

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The cloud reaches its equilibrium point

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The cloud reaches its equilibrium point
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As such, rather than measuring plans, it’s worth checking on where workloads actually go. According to that same Barclays CIO survey, the answer is cloud.

Cloud spend and workload location 2022-2024.

Barclays CIO Survey

That’s a lot of cloud movement, but with all that interest in repatriation, it may simply signal that, as Coté posits, we’re due to settle into prolonged equilibrium, with plenty of workloads moving to the cloud but many staying on-premises. Underlying it all may be that enterprises don’t have the right people to refactor applications for the cloud. Or, rather, that the teams that got them where they are may not be the right teams to take them where they need to go. That’s a people problem, as most technology issues are, and change will take decades.

This is why, for most workloads, the answer to whether it goes to the cloud or stays on premises is, “It depends.” That is, it depends on the people involved, and not necessarily the workload itself.

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Overcoming data inconsistency with a universal semantic layer

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Overcoming data inconsistency with a universal semantic layer
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Once the underlying data and semantics are established, the universal semantic layer must be integrated with data consumers, such as generative AI, BI, spreadsheets, and embedded analytics. Cube Cloud is a universal semantic layer platform offering numerous prebuilt integrations and a robust API suite so enterprises can model data once and deliver it anywhere. It also offers a host of developer tools to make it easier to collaborate and build data models, set up caching and pre-aggregations, and maintain data access controls.

Benefits of a universal semantic layer

With a universal semantic layer, data teams have more governance and control, and — if implemented correctly — end users get more value from data and fewer misunderstandings among teams. This enhances efficiency and ensures that all data consumption places are working with the same, accurate data. So, no matter if the data is being used by a person looking at a dashboard, or a large language model that is giving someone answers to questions, the data is consistent.

All of this makes it easier for data teams to quickly deliver data to the various consumers they work with internally and externally. Data teams can easily update or define new metrics, design domain-specific views of data, and incorporate new sources of raw data. They also can enforce governance policies, including access control, definitions, and performance.

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Google releases differential privacy software for JVM

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Google releases differential privacy software for JVM
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Google has released PipelineDP4j, a differential privacy solution that allows developers to execute highly parallelizable computations using Java as the baseline language, while providing guarantees that personal information is kept private and secure.

PipelineDP4j opens the door for new applications of differential privacy by reducing the barrier of entry for developers already working in Java, Google said. The software executes on the JVM, can be used with Java, Kotlin, or Scala, and supports distributed data processing frameworks such as Apache Beam and, coming soon, Apache Spark.

 PipelineDP4j is intended for use by all developers, regardless of differential privacy expertise, Google said. Differential privacy is a PET (privacy-enhancing technology) serving as a framework for analysis of data sets in a privacy-preserving way to ensure that personal information is never released, according to Google.

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Overlooked cloud sustainability issues | InfoWorld

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Overlooked cloud sustainability issues | InfoWorld
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Cloud sustainability is a funny topic. It’s like dental care; everyone agrees it’s important, but who wants to discuss the particulars? It’s much more fun to talk about the new “shiny keys” of IT: AI and its endless possibilities. Cloud sustainability is so five minutes ago. However, like dental care, ignoring the issues will not make them go away.

AI growth was limited this year by the AI chip shortage. With the chip issue resolved, AI will accelerate the sustainability crisis due to its demands on our power grids that will outpace supply. Fortunately, we’re not at that tipping point—yet.

Looking in the wrong places

In my podcast discussions and videos on cloud and sustainability, which are becoming more frequent due to the rise of generative AI in the cloud, people tend to focus primarily on data center topics such as power and server efficiency. We need to understand that the power production and consumption side of things is not where the most improvements can be made.

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Download the AI in the Enterprise (for Real) Spotlight

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Download the AI in the Enterprise (for Real) Spotlight
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Download the November 2024 issue of the Enterprise Spotlight from the editors of CIO, Computerworld, CSO, InfoWorld, and Network World.

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Python is the most popular language on GitHub

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Python is the most popular language on GitHub
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October was an extra busy month in the world of Python, as all signs point to its unstoppable ascent. As reported on InfoWorld, Python is not only the language of choice for AI, but recently beat out JavaScript as the most used language on GitHub. That is no small feat. In more technical news, if you haven’t yet checked out the free-threaded, “no-GIL” build of Python, we encourage it—and we have the guide to get you started. We’ve also updated our introduction to parallelism in Python, which now covers Python threads, subprocesses, the new free-threaded build in Python 3.13, and subinterpreters.

Top picks for Python readers on InfoWorld

Python overtakes JavaScript on GitHub
Another rising star is Jupyter Notebook, also gaining traction on everyone’s favorite code-sharing site.

Python threading and subprocesses explained
Learn how to choose between threads or processes for parallel operations in Python, and find out what the free-threaded build in Python 3.13 brings to the table.

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What is cloud computing? Everything you need to know

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What is cloud computing? Everything you need to know
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Edge computing considerations

You often see edge computing incorrectly described as an alternative to cloud computing. Edge computing is about moving compute to local devices in a highly distributed system, typically as a layer around a cloud computing core. There is typically a cloud involved to orchestrate all of the devices and take in their data, then analyze it or otherwise act on it. 

To the cloud and back – why repatriation is real

While public cloud offers scalability and flexibility, some enterprises are opting to return to on-premises infrastructure due to rising costs, data security concerns, performance issues, vendor lock-in, and regulatory compliance challenges. While the public cloud offers scalability and flexibility, on-premises infrastructure provides greater control, customization, and potential cost savings in certain scenarios leading some technology decision-makers to consider repatriation. However, a hybrid cloud approach, combining public and private cloud, often offers the best balance of benefits.

More specific reasons to repatriate including the following:

  • Unanticipated costs, such as data transfer fees, storage charges, and egress fees, can quickly escalate, especially for large-scale cloud deployments.  
  • Inaccurate resource provisioning or underutilization can lead to higher-than-expected costs.
  • Stricter data privacy regulations require organizations to store and process data within specific geographic boundaries.  
  • For highly sensitive data, companies may prefer to maintain greater control over security measures and access permissions. 
  • On-premises infrastructure can offer lower latency, particularly for applications requiring real-time processing or high-performance computing.  
  • Overreliance on a single cloud provider can limit flexibility and increase costs. Repatriation allows organizations to diversify their infrastructure and reduce vendor dependency.  
  • Industries with stringent compliance requirements may find it easier to meet standards with on-premises infrastructure.  
  • On-premises environments offer greater control over hardware, software, and network configurations, allowing for customized solutions.  
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Benefits of cloud computing

The cloud’s main appeal is to reduce the time to market of applications that need to scale dynamically. Increasingly, however, developers are drawn to the cloud by the abundance of advanced new services that can be incorporated into applications, from machine learning to internet of things (IoT) connectivity.

Although businesses sometimes migrate legacy applications to the cloud to reduce data center resource requirements, the real benefits accrue to new applications that take advantage of cloud services and “cloud native” attributes. The latter include microservices architecture, Linux containers to enhance application portability, and container management solutions such as Kubernetes that orchestrate container-based services. Cloud-native approaches and solutions can be part of either public or private clouds and help enable highly efficient devops workflows.

Cloud computing, be it public or private or hybrid or multicloud, has become the platform of choice for large applications, particularly customer-facing ones that need to change frequently or scale dynamically. More significantly, the major public clouds now lead the way in enterprise technology development, debuting new advances before they appear anywhere else. Workload by workload, enterprises are opting for the cloud, where an endless parade of exciting new technologies invite innovative use.

SaaS has its roots in the ASP (application service provider) trend of the early 2000s, when providers would run applications for business customers in the provider’s data center, with dedicated instances for each customer. The ASP model was a spectacular failure because it quickly became impossible for providers to maintain so many separate instances, particularly as customers demanded customizations and updates.

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Salesforce is widely considered the first company to launch a highly successful SaaS application using multitenancy — a defining characteristic of the SaaS model. Rather than each Salesforce customer getting its own application instance, customers who subscribe to the company’s salesforce automation software share a single, large, dynamically scaled instance of an application (like tenants sharing an apartment building), while storing their data in separate, secure repositories on the SaaS provider’s servers. Fixes can be rolled out behind the scenes with zero downtime and customers can receive UX or functionality improvements as they become available.

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Grounding with Google Search available in Google AI Studio, Gemini API

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Grounding with Google Search available in Google AI Studio, Gemini API
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Google Cloud has made Grounding with Google Search available in the Google AI Studio and in the Gemini API. The new feature enables developers to get more accurate and fresh responses from Gemini generative AI models, the company said.

In addition to more accurate responses, the model returns links to the grounding sources and search suggestions that point users to search results that correspond to the grounded responses.

Grounding with Google Search is supported for all generally available versions of Gemini 1.5 models, Google Cloud said. It can be accessed in Google AI Studio under the “Tools” section or in the Gemini API by enabling the google_search_retrieval tool. Users can test grounding for free in Google AI Studio. With the API, developers can access the tool with the paid tier of $35 per 1,000 grounded queries.

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Is now the right time to invest in implementing agentic AI?

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Is now the right time to invest in implementing agentic AI?
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What makes agentic AI autonomous or able to take actions independently is its ability to interpret data, predict outcomes, and make decisions, learning from new data — unlike traditional RPA, which falters when encountering unexpected data, said Cameron Marsh, senior analyst at Nucleus research.

This adaptive nature of agentic AI, according to Chada, can help enterprises increase efficiency by handling complex, variable tasks that traditional RPA can’t manage, such as the roles of a claims adjuster, a loan officer, or a case worker, provided that it has access to the necessary data, workflows, and tools required to complete the task.

Software vendors are already touting agentic AI offerings with access to those resources, including the likes of Salesforce’s Agentforce, Microsoft’s Copilot-based autonomous Agents, ServiceNow’s AI Agents, Google’s Vertex AI Agent Builder, Amazon Bedrock Agents, and IBM’s watsonx Agent Builder, with more are likely to follow.

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