
Three charts that show you're paying too much for gold
New analysis shows the precious metal has never been so expensive. Unlike other investments, gold produces no income, and its industrial uses are limited, which makes it difficult to assess its 'fair value'.
But Russ Mould, of stockbroker AJ Bell, said one crude way of doing so is to measure how much metal a pay packet can buy. He explains: 'If bullion moves beyond the reach of the worker, that could at least crimp jewellery demand and one source of incremental buying.'
By this measure, gold is at its most expensive level on record. Today, a blue collar worker in the US would have to work for 105 hours to buy one ounce of gold, according to AJ Bell's analysis of US Federal Reserve data.
This compares to just 12 hours in the early 1970s, before president Richard Nixon broke up the Bretton Woods agreement that pegged the US dollar to the precious metal.
Even when the price of gold spiked in the 1980s, one ounce peaked at just under 99 hours of earnings.
Mr Mould said: 'The current score of 105 hours could be seen as ominous for gold affordability, since the best cure for high prices is high prices – they stoke supply, depress demand, prompt searches for substitutes, or all three.'
Gold is also far more expensive compared to other commodities than it has been in the past.
Since 1970, one ounce of gold has bought an average of 17 to 18 barrels of oil, but today it would buy 49 barrels, while one ounce of gold would buy 91 ounces of silver today, up from an average of 60.
Since 1976, gold and platinum prices have been equally matched. But today, one ounce of gold buys 2.4 ounces of platinum, despite a recent surge in the price of platinum.
Gold is a go-to asset during times of economic turmoil thanks to its reputation as a store of value. The price hit a record high of over £2,500 per Troy ounce in April, as investors sought safe havens from market volatility.
Investors are usually advised against buying when prices are at the top, but in the case of gold, it seems newcomers have been unable to resist.
In the second quarter of the year, UK buyers of the precious metal outnumbered sellers by the widest margin in four years, according to precious metals marketplace, BullionVault.
Adrian Ash, of BullionVault, said: 'After taking profit on this year's earlier surge in prices, UK investors are now buying into gold's bull market.
'They're joining central banks and Asian wealth managers in building their holdings as the geopolitical shock of Trump's return to the White House persists and the UK's economic gloom worsens under Labour.'
Some experts believe gold can only rise further because of geopolitical instability and inflationary pressures, but others are more apprehensive.
Jock Henderson, investment analyst at Capital Gearing Asset Management, said the firm was 'cautious' about being overly exposed at current prices. The investment trust Capital Gearing has only 1pc in gold, despite its defensive positioning.
Mr Henderson said: 'While gold investors have been rewarded for holding gold, there are complicated underlying dynamics which make its fundamental value hard to determine.'
Advisers generally recommend that investors should not hold more than 10pc in gold.
Over time, the allocations in your portfolio typically drift in favour of the highest-performing asset, so some investors may need to trim their gold exposure in order to reduce volatility across their investments.
Hashtags

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles


Reuters
28 minutes ago
- Reuters
Ingram Micro says identified ransomware on certain of its internal systems
July 5 (Reuters) - Ingram Micro (INGM.N), opens new tab said on Saturday it recently identified ransomware on certain of its internal systems. The information technology company took steps to secure the relevant environment, including taking certain systems offline, it said in a statement. The Irvine, California-based company also launched an investigation with the assistance of leading cybersecurity experts and notified law enforcement, it added.

Finextra
an hour ago
- Finextra
Context Engineering for Financial Services: By Steve Wilcockson
The hottest discussion in AI right now, at least the one not about Agentic AI, is about how "context engineering" is more important than prompt engineering, how you give AI the data and information it needs to make decisions, and it cannot (and must not) be a solely technical function. "'Context' is actually how your company operates; the ideal versions of your reports, documents & processes that the AI can use as a model; the tone & voice of your organization. It is a cross-functional problem.' So says renowned Tech Influencer and Associate Professor at Wharton School, Ethan Molick. He in turn cites fellow Tech Influencer Andrej Karpathy on X, who in turn cites Tobi Lutke, CEO of Shopify: "It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM. " The three together - Molick, Karpathy and Lutke - make for a powerful triumvirate of Tech-influencers. Karpathy consolidates the subject nicely. He emphasizes that in real-world, industrial-strength LLM applications, the challenge entails filling the model's context window with just the right mix of information. He thinks about context engineering as both a science—because it involves structured systems and system-level thinking, data pipelines, and optimization —and an art, because it requires intuition about how LLMs interpret and prioritize information. His analysis reflects two of my predictions for 2025 one highlighting the increasing impact of uncertainty and another a growing appreciation of knowledge. Tech mortals offered further useful comments on the threads, two of my favorites being: 'Owning knowledge no longer sets anyone apart; what matters is pattern literacy—the ability to frame a goal, spot exactly what you don't know, and pull in just the right strands of information while an AI loom weaves those strands into coherent solutions.' weaves those strands into coherent solutions.' 'It also feels like 'leadership' Tobi. How to give enough information, goal and then empower.' I love the AI loom analogy, in part because it corresponds with one of my favorite data descriptors, the "Contextual Fabric". I like the leadership positivity too, because the AI looms and contextual fabrics, are led by and empowered by humanity. Here's my spin, to take or leave. Knowledge, based on data, isn't singular, it's contingent, contextual. Knowledge and thus the contextual fabric of data on which it is embedded is ever changing, constantly shifting, dependent on situations and needs. I believe knowledge is shaped by who speaks, who listens, and what about. That is, to a large extent, led by power and the powerful. Whether in Latin, science, religious education, finance and now AI, what counts as 'truth' is often a function of who gets to tell the story. It's not just about what you know, but how, why, and where you know it, and who told you it. But of course it's not that simple; agency matters - the peasant can become an abbot, the council house schoolgirl can become a Nobel prize-winning scientist, a frontier barbarian can become a Roman emperor. For AI, truth to power is held by the big tech firms and grounded on bias, but on the other it's democratizing in that all of us and our experiences help train and ground AI, in theory at least. I digress. For AI-informed decision intelligence, context will likely be the new computation that makes GenAI tooling more useful than simply being an oft-hallucinating stochastic parrot, while enhancing traditional AI - predictive machine learning, for example - to be increasingly relevant and affordable for the enterprise. Context Engineering for FinTech Context engineering—the art of shaping the data, metadata, and relationships that feed AI—may become the most critical discipline in tech. This is like gold for those of us in the FinTech data engineering space, because we're the dudes helping you create your own context. I'll explore how five different contextual approaches, all representing data engineering-relevant vendors I have worked for —technical computing, vector-based, time-series, graph and geospatial platforms—can support context engineering. Parameterizing with Technical Computing Technical computing tools – think R, Julia, MATLAB and Python's SciPy stack - can integrate domain-specific data directly into the model's environment through structured inputs, simulations, and real-time sensor data, normally as vectors, tables or matrices. For example, in engineering or robotics applications, an AI model can be fed with contextual information such as system dynamics, environmental parameters, or control constraints. Thus the model can make decisions that are not just statistically sound but also physically meaningful within the modeled system. They can dynamically update the context window of an AI model, for example in scenarios like predictive maintenance or adaptive control, where AI must continuously adapt to new data. By embedding contextual cues, like historical trends, operational thresholds, or user-defined rules, such tools help ground the model's outputs in the specific realities of the task or domain. Financial Services Use Cases Quantitative Strategy Simulation Simulate trading strategies and feed results into an LLM for interpretation or optimization. Stress Testing Financial Models Run Monte Carlo simulations or scenario analyses and use the outputs to inform LLMs about potential systemic risks. Vectors and the Semantics of Similarity Vector embeddings are closely related to the linear algebra of technical computing, but they bring semantic context to the table. Typically stored in so-called vector databases, they encode meaning into high-dimensional space, allowing AI to retrieve through search not just exact matches, but conceptual neighbors. They thus allow for multiple stochastically arranged answers, not just one. Until recently, vector embeddings and vector databases have been primary providers of enterprise context to LLMs, shoehorning all types of data as searchable mathematical vectors. Their downside is their brute force and compute-intensive approach to storing and searching data. That said, they use similar transfer learning approaches – and deep neural nets – to those that drive LLMs. As expensive, powerful brute force vehicles of Retrieval-Augmented Generation (RAG), vector databases don't simply just store documents but understand them, and have an increasingly proven place for enabling LLMs to ground their outputs in relevant, contextualized knowledge. Financial Services Use Cases Customer Support Automation Retrieve similar past queries, regulatory documents, or product FAQs to inform LLM responses in real-time. Fraud Pattern Matching Embed transaction descriptions and retrieve similar fraud cases to help the model assess risk or flag suspicious behavior. Time-Series, Temporal and Streaming Context Time-series database and analytics providers, and in-memory and columnar databases that can organize their data structures by time, specialize in knowing about the when. They can ensure temporal context—the heartbeat of many use cases in financial markets as well as IoT, and edge computing- grounds AI at the right time with time-denominated sequential accuracy. Streaming systems, like Kafka, Flink, et al can also facilitate the real-time central nervous systems of financial event-based systems. It's not just about having access to time-stamped data, but analyzing it in motion, enabling AI to detect patterns, anomalies, and causality, as close as possible to real time. In context engineering, this is gold. Whether it's fraud that happens in milliseconds or sensor data populating insurance telematics, temporal granularity can be the difference between insight and noise, with context stored and delivered by what some might see as a data timehouse. Financial Services Use Cases Market Anomaly Detection Injecting real-time price, volume, and volatility data into an LLM's context allows it to detect and explain unusual market behavior. High-Frequency Trading Insights Feed LLMs with microsecond-level trade data to analyze execution quality or latency arbitrage. Graphs That Know Who's Who Graph and relationship-focussed providers play a powerful role in context engineering by structuring and surfacing relationships between entities that are otherwise hidden in raw data. In the context of large language models (LLMs), graph platforms can dynamically populate the model's context window with relevant, interconnected knowledge—such as relationships between people, organizations, events, or transactions. They enable the model to reason more effectively, disambiguate entities, and generate responses that are grounded in a rich, structured understanding of the domain. Graphs can act as a contextual memory layer through GraphRAG and Contextual RAG, ensuring that the LLM operates with awareness of the most relevant and trustworthy information. For example, graph databases - or other environments, e.g. Spark, that can store graph data types as accessible files, e.g. Parquet, HDFS - can be used to retrieve a subgraph of relevant nodes and edges based on a user query, which can then be serialized into natural language or structured prompts for the LLM. Platforms that focus graph context around entity resolution and contextual decision intelligence can enrich the model's context with high-confidence, real-world connections—especially useful in domains like fraud detection, anti-money laundering, or customer intelligence. Think of them as like Shakespeare's Comedy of Errors meets Netflix's Department Q. Two Antipholuses and two Dromios rather than 1 of each in Comedy of Errors? Only 1 Jennings brother to investigate in Department Q's case, and where does Kelly MacDonald fit into anything? Entity resolution and graph context can help resolve and connect them in a way that more standard data repositories and analytics tools struggle with. LLMs cannot function without correct and contingent knowledge of people, places, things and the relationships between them, though to be sure many types of AI can also help discover the connections and resolve entities in the first place. Financial Services Use Cases AML and KYC Investigations Surface hidden connections between accounts, transactions, and entities to inform LLMs during risk assessments. Credit Risk Analysis Use relationship graphs to understand borrower affiliations, guarantors, and exposure networks. Seeing the World in Geospatial Layers Geospatial platforms support context engineering by embedding spatial awareness into AI systems, enabling them to reason about location, proximity, movement, and environmental context. They can provide rich, structured data layers (e.g., terrain, infrastructure, demographics, weather) that can be dynamically retrieved and injected into an LLM's context window. This allows the model to generate responses that are not only linguistically coherent but also geographically grounded. For example, in disaster response, a geospatial platform can provide real-time satellite imagery, flood zones, and population density maps. This data can be translated into structured prompts or visual inputs for an AI model tasked with coordinating relief efforts or summarizing risk. Similarly, in urban planning or logistics, geospatial context helps the model understand constraints like traffic patterns, zoning laws, or accessibility. In essence, geospatial platforms act as a spatial memory layer, enriching the model's understanding of the physical world and enabling more accurate, context-aware decision-making. Financial Services Use Cases Branch Network Optimization Combine demographic, economic, and competitor data to help LLMs recommend new branch locations. Climate Risk Assessment Integrate flood zones, wildfire risk, or urban heat maps to evaluate the environmental exposure of mortgage and insurance portfolios. Context Engineering Beyond the Limits of Data, Knowledge & Truths Context engineering I believe recognizes that data is partial, and that knowledge and perhaps truth or truths needs to be situated, connected, and interpreted. Whether through graphs, time-series, vectors, tech computing platforms, or geospatial layering, AI depends on weaving the right contextual strands together. Where AI represents the loom, the five types of platforms I describe are like the spindles, needles, and dyes drawing on their respective contextual fabrics of ever changing data, driving threads of knowledge—contingent, contextual, and ready for action.


The Guardian
2 hours ago
- The Guardian
Trump news at a glance: Elon Musk announces new political party targeting sway in Congress
The fallout between the US president, Donald Trump, and tech billionaire Elon Musk has reached a new low, with Musk declaring this weekend that he will bankroll a new political party to rival the president. Musk, the world's richest man, only departed from the White House this May but has been critical of Trump's signature policy bill, which he has described as 'utterly insane and destructive'. 'Today, the America party is formed to give you back your freedom,' Musk wrote on X on Saturday, adding that: 'By a factor of 2 to 1, you want a new political party, and you shall have it! When it comes to bankrupting our country with waste & graft, we live in a one-party system, not a democracy.' Here are the key stories: The new US political party that Elon Musk has boasted about bankrolling could initially focus on a handful of attainable House and Senate seats while striving to be the decisive vote on major issues amid the thin margins in Congress. The Tesla and SpaceX's multibillionaire CEO mused about that approach on Friday in a post on X, the social media platform he owns, as he continued feuding with Donald Trump over the spending bill that the president has signed into law. On Saturday, without immediately elaborating, the former Trump adviser announced on X that he had created the so-called America party. Read the full story Pete Hegseth, the US defense secretary, unilaterally halted an agreed shipment of military aid to Ukraine due to baseless concerns that US stockpiles of weapons have run too low, it has been reported. A batch of air defense missiles and other precision munitions were due to be sent to Ukraine to aid it in its ongoing war with Russia, which launched a full-scale invasion of its neighbor in 2022. The aid was promised by the US during Joe Biden's administration last year. Read the full story An email sent by the US Social Security Administration (SSA) that claims Donald Trump's major new spending bill has eliminated taxes on benefits for most recipients is misleading, critics have said. The reconciliation bill – which the president called the 'one big, beautiful bill' before signing it on Friday after Republicans in Congress passed it – includes provisions that will strip people of their health insurance, cut food assistance for the poor, kill off clean energy development and raise the national debt by trillions of dollars. Read the full story Bernie Sanders, the venerable democratic socialist senator from Vermont, was not in a mood to pull punches. 'Trump is undermining our democracy and rapidly moving us towards authoritarianism, and the billionaires who care more about their stock portfolios than our democracy are helping him do it,' he fumed in a statement last week. Such outbursts have been common in recent months as Sanders has taken up a leading position opposing Donald Trump's second term, and flagging his concern that the president is waging a war against the media – and winning. Read the full story A nationwide US network of dozens of far-right, men-only fraternal clubs has what members describe as 'literally hundreds' of participants who include past and currently serving military personnel, lawyers, civil servants and prominent antisemitic influencers, a Guardian investigation can reveal. The Old Glory Club (OGC) – which has at least 26 chapters in 20 US states and until now has drawn little attention – exemplifies the alarming rise of organized racist political groups in the past few years but especially during the rise of Donald Trump and his return to the White House. Read the full story Texas continues grim flood recovery with at least 32 killed, including 14 children US hit with mass shootings and fatal accidents on Fourth of July holiday Catching up? Here's what happened on 4 July 2025.