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Why Apps Might Be Slower in the Summer

Andrew Levy

Managing your app's performance is a never-ending challenge. With constantly changing network conditions and new devices frequently entering the market, maintaining a high-quality user experience takes knowledge and diligence. Adding to the list of factors to watch out for when it comes to app performance is something you might not expect — the weather.

According to a recent study, on average, mobile apps will run about 15% slower during warm summer months. Why? It all has to do with the propagation of radio waves. Extra humidity in the air during summer causes waves to lose intensity, especially at higher frequencies. This means, the water vapor and heat summer bring will cause an overall degradation in signal strength, as well as delays in data delivery.

The chart below highlights the difference in latency of apps in summer versus the winter.


Another factor that contributes to lags in data during the summer has less to do with applications, and more to do with devices becoming too hot. Because processors are heat sensitive, they slow down when they get too hot. Dealing with an overheated device can quickly become a downward spiral — devices use more battery when they're working harder in a hot environment, and batteries charge more slowly when they're overheated, in turn, heating up the phone as they charge.

To keep apps at optimal performance, it's essential to keep your device cool. Users can assist their device in warm climates by not keeping their phone in pockets, and removing cases when the phone gets too hot.

Andrew Levy is the Co-Founder and Chief Strategy Officer of Apteligent.

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Why Apps Might Be Slower in the Summer

Andrew Levy

Managing your app's performance is a never-ending challenge. With constantly changing network conditions and new devices frequently entering the market, maintaining a high-quality user experience takes knowledge and diligence. Adding to the list of factors to watch out for when it comes to app performance is something you might not expect — the weather.

According to a recent study, on average, mobile apps will run about 15% slower during warm summer months. Why? It all has to do with the propagation of radio waves. Extra humidity in the air during summer causes waves to lose intensity, especially at higher frequencies. This means, the water vapor and heat summer bring will cause an overall degradation in signal strength, as well as delays in data delivery.

The chart below highlights the difference in latency of apps in summer versus the winter.


Another factor that contributes to lags in data during the summer has less to do with applications, and more to do with devices becoming too hot. Because processors are heat sensitive, they slow down when they get too hot. Dealing with an overheated device can quickly become a downward spiral — devices use more battery when they're working harder in a hot environment, and batteries charge more slowly when they're overheated, in turn, heating up the phone as they charge.

To keep apps at optimal performance, it's essential to keep your device cool. Users can assist their device in warm climates by not keeping their phone in pockets, and removing cases when the phone gets too hot.

Andrew Levy is the Co-Founder and Chief Strategy Officer of Apteligent.

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.