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With Apple shares trading near all-time highs around $326, fresh speculation about a potential stock split has resurfaced. While the company has not announced any plans, the upcoming leadership transition adds a new dimension to the discussion.
Apple announced in April 2026 that Tim Cook will step down as CEO on August 31 and transition to Executive Chairman, with John Ternus (currently Senior Vice President of Hardware Engineering) taking over as CEO effective September 1, 2026. This marks the most significant leadership change at Apple in over 15 years.
Could the CEO Transition Influence a Split?
Analysts and investors are debating whether the smooth handover could accelerate or delay a split:
• Potential catalyst: A new CEO often seeks early wins to build momentum. A stock split would be a low-risk, high-visibility move that signals confidence and appeals to retail investors — especially as Ternus begins his tenure.
• Likely timing: Most observers expect any decision to wait until after the transition settles (late 2026 or 2027), once Ternus has established his priorities around AI, hardware innovation, and Mac refreshes.
• Continuity advantage: With Cook remaining as Executive Chairman, the board is expected to maintain strong oversight, reducing any perceived risk around major shareholder-friendly actions.
Apple has not split its stock since the 4-for-1 in August 2020. With fractional-share trading now widespread, the urgency is lower than in the past, but a split could still broaden accessibility and create psychological tailwinds.
Apple’s stock has performed strongly in 2026, rebounding from a weaker 2025 and recently approaching record levels. Solid services growth, ongoing buybacks, and AI/hardware momentum (including upcoming Mac lineup overhauls) have supported the rally — all without a split.
Historically, Apple splits (1987, 2000, 2005, 2014, 2020) have often coincided with periods of optimism and growth. A future split under Ternus could similarly project confidence in the company’s long-term trajectory.
MacDailyNews Take: The Cook-to-Ternus transition is unlikely to hinder a stock split and could even encourage one as an early signal of confidence under the new leadership. That said, Apple tends to be deliberate, so any move would likely prioritize substance over optics.
While a split (even a modest 2-for-1 or 3-for-1) could project optimism and attract more retail interest, Apple’s fundamentals (robust cash reserves, ongoing innovation, and execution on AI/hardware roadmaps) appear to be driving the stock’s current momentum without one. Splits are cosmetic in terms of intrinsic value but have historically aligned with growth phases. Investors should watch for any board signals or SEC filings, but the lack of a split hasn’t held back the rebound in 2026.
Apple’s split history:
• June 16, 1987: 2-for-1
• June 21, 2000: 2-for-1
• February 28, 2005: 2-for-1
• June 9, 2014: 7-for-1
• August 31, 2020: 4-for-1
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Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on the same thing: a weather forecast.
While these forecasts are something that most people glance at for two seconds, weather predictions influence major strategic decisions in many industries, with real money, livelihoods, and even actual lives at stake. Farmers use them to determine which crop variety to sow, when to fertilize, how much to invest in irrigation infrastructure, and how long livestock should graze. Utilities use them to decide where to build solar and wind farms, as well as how to price wholesale electricity. Predictions are used to warn people about extreme weather and to trigger emergency response measures. More recently, weather predictions have become relevant for an emerging industry: prediction markets, where people bet money on all kinds of real-world events, including the weather.
However, the temptation to manipulate weather data to get an edge in these markets, combined with a collective move toward data-driven AI weather forecasting, is starting to put the accuracy of weather predictions at risk. These risks are relatively manageable for now, but as experts in the field, we can foresee scenarios where they snowball into far bigger, more systemic problems.
To develop weather predictions, we need accurate observations of current conditions. These are collected from several sources, including weather stations at airports, utilities, or transport services. Traditional operational systems like the Weather Research and Forecasting model or the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecasting System combine these observations with numerical approximations in order to estimate future weather patterns.
Sometimes, weather stations have issues because of, for example, instrument failures or upgrades in equipment. These can be caught either in real time (through checking and correction) or retroactively. Traditional forecasting systems also have a built-in safeguard called data assimilation: Every incoming measurement is weighed against what the physical model says should be happening and against readings from nearby stations.
Together, these mechanisms help keep weather observations reliable and predictions robust. However, new threats are putting observational accuracy at risk. Earlier this year, news outlets reported that the weather station at Paris Charles de Gaulle Airport (CDG) had been manipulated to record suspicious temperature spikes on April 6 and April 15, 2026. Authorities speculate that a hand-held hairdryer or lighter might have come into play. Either way, it led to some big payouts for online prediction-market gamblers who had bet it would hit 22 °C (71.6 °F) on days when the actual average was around 18°C (64.4°F). One individual won $20,000.
Fortunately, tampering with a single station like this can usually be caught by human monitoring or current statistical methods. In this case, members of a French climate nonprofit association noticed the anomalies by chance and raised the alarm.
But what if there are no human monitoring systems in place? And what about other types of manipulation? What if, instead of tampering with one station, someone remotely nudged the readings at many stations at once—making each change small enough to look plausible on its own? Existing quality controls struggle to catch this kind of coordinated manipulation. And time works against us; careful checks of data and metadata take hours or days, but forecasts have to go out on schedule, whatever the weather is doing.
The shift toward artificial intelligence in weather prediction raises the stakes. These methods are even more dependent on accurate, reliable weather observations; in fact, they are known as “data-driven models.” For example, researchers at ECMWF are exploring whether high-quality weather forecasts can be produced directly from raw observations, skipping the assimilation step that currently acts as a quality filter. Other researchers are going one step further; combining geospatial data (including weather station data) with large language models and agentic AI to support real-time, autonomous decision-making during extreme events such as storms.
Possible benefits are improvements in accuracy, efficiency, and speed. But removing humans from the equation introduces a vast range of new risks.
At the low end of the risk scale, an individual speculator manipulates a weather station for personal gain—that is the CDG Airport case. One step up: A group of traders could coordinate to bias forecasts of renewable energy output, moving wholesale electricity prices and leaving whoever is on the other side of the trade holding the loss. And at the far end, a state actor or saboteur could manipulate one or many stations to set off an early warning system or even keep one silent when it should sound. Step by step, the risk grows, from fraud to compromised disaster preparedness to a matter of national security.
As long as there are financial (or other) incentives to manipulate observational data, adversaries will search for new opportunities, and it is our task to stay one step ahead. Here are three ways.
1. Watch the stations. Data quality controls should include station security, anomaly detection and correction, and human oversight. Weather stations should be monitored continuously to deter tampering. Data homogenization methods that clean up weather records also need to get faster, with the goal of catching problems in real time. This will become increasingly important as agentic AI systems use these data to deliver real-time decisions. Finally, human oversight is needed to flag questionable data and model outcomes. After all, it was humans who caught the CDG Airport manipulation.
2. Protect the data to safeguard the AI. Data defense mechanisms must be positioned throughout the AI pipeline. AI explainability and adversarial robustness tools can help us understand the underlying data and the AI model outputs, help us identify data- or model-related issues, and potentially make us more resilient to adversarial attacks.
3. Ensure continuous accountability along the chain. Observational data passes through many hands: the operators who run the stations, the national weather services that steward the records, and the forecasting centers that turn them into predictions. No single one of them can protect data integrity alone—each guards its own link, and any anomaly needs to be communicated along the whole chain, from station operators to the people acting on the forecast.
It is fortunate that the situation at CDG Airport was caught, but it should serve as a wake-up call. As the role of observational data grows in weather forecasting, we need to adapt to evolving threats. This means protecting our data and models by strengthening existing oversight and accountability structures, and improving coordination among key partners.
This op-ed was written by:
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Author(s): Verónica Sanz
AI could make science stronger—but only if we deliberately use it to strengthen the formation of future scientists rather than to reduce their numbers.
[Physics 19, 87] Published Tue Jun 30, 2026
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