IDC analyst Francisco Jeronimo said something interesting following the iPhone Duo introduction on Wednesday: “Apple showed up years after everyone else, yet it walks in and sets the rules.”

He’s right. From the IP resistance to the little touches that set the new foldable apart from most of the competition, Apple has set expectations for the entire market, and while competitor Samsung is trash talking the foldable, even it stands to benefit from the millions Apple is about to sell. Apple is, after all, allegedly paying Samsung $250 for each iPhone Duo display.

But it is not just the rules concerning foldables the company has now defined; it has also set the standard competitors must follow in chip design with its A20 Pro, a super-powered processor that delivers noticeable performance improvements even compared to last year’s iPhone 17. 

What’s new in the chip?

With six CPU cores, seven GPU cores and a new Dual 16-core Neural Engine, the new chip boasts numerous improvements compared to the A19 Pro, not the least of which is that it’s the first 2nm chip Apple has put inside a smartphone. These enhancements bestow it with a series of superlatives:

• Memory bandwidth is 50% higher than last year’s chip.
• GPU graphics performance increases up to 40% over the A19 Pro.
• AI performance is expected to double.

These are all significant improvements that make for noticeable experience boosts for anyone using one of these phones. Prosaically, I anticipate first reviews will celebrate how fast and responsive these devices are, and this will be most evidenced in tasks such as video capture and photography. Pictures will capture more swiftly, open faster, and edit features will become (and already are) more advanced and sophisticated than before. Apple spent ample time celebrating the iPhone 18 Pro series for the new frontiers in digital photography they open up, and not just because of the new reflexive lens.

The point is that the devices have the horsepower to get through computationally intensive tasks, including AI, thanks to the Neural Engines and GPU.

Thermal management is the icing on the quake

What those tasks have in common is heat, which Apple’s product designers have addressed in two critical ways: liquid cooling and processor design.

The processor design is the important thing, because in this case Apple has moved away from the InFO POP packaging of the A19 Pro in which the DRAM is stacked above the chip. It’s now embraced a side-by-side design it described as “inspired by” M-series Apple Silicon.

That works because placing both components alongside each other, rather than in a perpetual embrace, reduces the overall temperature of both by putting a little space between them. As Apple explained it, this Wafer-level Multi-Chip Module (WMCM) approach means the memory gets out of the way of the processor, allowing the latter to make direct contact with the vapor chamber cooling system.

In layman’s terms, it means both memory and chip can perform more intensively while being more efficiently cooled, a move that helps Apple’s iPhone meet those astonishing performance achievements. The vapor chamber is also bigger, made of new material with three times the surface area of before, making it even better at cooling the device.

It also means Apple has achieved a lot of things competitors have not, most particularly in packaging, the use of 2nm chips in a mass market product, and overall design.

That’s where it gets interesting. Both Qualcomm and MediaTek now plan to follow in Apple’s footsteps with the adoption of 2nm chips and side-by-side packaging.

Digitimes suggests it is a move others will follow, as vendors work furiously to ensure their hardware can run AI effectively on device, which iPhones already do. Apple, of course, already knows its Macs, iPhones, and iPads have been designed from the ground-up to support on-device AI as it works to provide the world’s best mass market platforms for secure and private use of the tech.

Friends don’t let friends bet against Apple

All the same, even at the microprocessor level, it’s hard to ignore that where Apple goes, others follow — even while where others go, Apple frequently follows later, but seems to do it better. It has never been a company to bet against, after all. People really should stop doing that.

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Qualcomm is taking an unusual approach to the launch of its next-generation premium Snapdragon mobile platform this year. Instead of holding everything for its annual Snapdragon Summit in Maui happening later this month, the company has been methodically disclosing the architecture that will power the next wave of flagship Android phones over the past couple of weeks.

First, the company disclosed its new Oryon CPU architecture, followed by a major overhaul of its Adreno GPU. Today, Qualcomm is detailing the next-generation Hexagon NPU that will serve as the platform’s primary AI engine.

Taken together, the disclosures give us a pretty good picture of where Qualcomm is going before we even know the chip’s official name.

There is obviously a strong performance story here, anchored by a 5GHz CPU, but the more interesting theme running through the architecture is memory locality and specialized AI acceleration. Qualcomm is trying to keep more data close to the compute resources that need it, while distributing AI workloads across the CPU, GPU and NPU.

These architectural changes are important as the company continues to drive on-device AI beyond relatively simple generative features toward more persistent, agentic workloads.

Oryon hits 5GHz, bolstered by FlexCache

Qualcomm disclosed in August that its next Oryon CPU will be the first mobile CPU to reach 5GHz. The eight-core design includes two 5GHz Prime cores and six Performance cores, with Qualcomm attributing the frequency gains to its fully custom Oryon microarchitecture, an in-house Arm-based CPU design that gives the company full control over the cores, cache and memory hierarchy, branch prediction and CPU subsystem.

The 5GHz headline is certainly going to get attention. However, Qualcomm’s new FlexCache architecture may ultimately have a larger impact on sustained performance. FlexCache allows Oryon’s Prime and Performance cores to tap into the same shared cache pool, with capacity dynamically allocated based on the workload. So rather than a demanding core being limited to a fixed slice of cache, it can draw on more of the available pool when needed. This keeps larger working data sets close to the CPU cores and reduces high-latency trips out to system memory.

This approach should benefit everything from gaming and multitasking to content creation, but it also aligns well with agentic AI workloads, where tasks may move through several stages of processing and across different CPU cores. The basic goal is straightforward: keep the CPU fed and avoid expensive trips to external memory.

Qualcomm’s next-gen Adreno GPU adds dedicated AI engines

Qualcomm’s next disclosure focused on graphics, where the company is making one of the more significant changes to its Adreno GPU engine in recent years. The new GPU adds dedicated Adreno Matrix Cores for running AI models directly inside the graphics pipeline. They’re paired with 18MB of Adreno High-Performance Memory, or HPM, which provides low-latency local storage for tile-based rendering, frame buffers and GPU compute.

Qualcomm claims HPM provides a 12% power improvement compared to its previous-gen Snapdragon 8 Elite Gen 5.

The more visible feature for users, however, may be Adreno Neural Fusion, which combines AI super resolution, neural processing and frame generation in a unified graphics pipeline. There are obvious parallels here to what NVIDIA, AMD and others are doing with neural rendering on the PC, though Qualcomm’s next-gen Snapdragon SoC has to operate within a much tighter mobile power envelope.

Qualcomm claims enabling Neural Fusion reduces power consumption by up to 40% in its internal testing using the company’s Dragon Alley graphics demo. That’s an impressive number, though as always, we’ll want to see how the technology behaves across actual shipping games and devices before drawing any definitive conclusions.

Qualcomm has also helped integrate Neural Fusion technology into Unity and Unreal Engine, which may ultimately be just as important as the underlying hardware. Developer adoption determines whether features like this become meaningful platform advantages or just another tick box on a spec sheet.

Qualcomm’s Hexagon NPU is being reworked for agentic AI

The most recent disclosure centers on Qualcomm’s Hexagon NPU, where the broader architecture starts to come together. The company’s next-generation Hexagon introduces a new Element Accelerator designed for transformer workloads, alongside its existing vector and scalar processing resources. Qualcomm says the new block is optimized for fast action loops, key-value (KV) cache acceleration and context lengths of up to 32K.

Hexagon is also getting 50% more shared memory, and again, that memory complement is important. Model state, context and KV-cache data can generate substantial memory traffic, particularly with larger language models. Keeping more of that data close to the NPU can reduce latency and power consumption. Qualcomm says the architecture is designed for long-context reasoning, multimodal models, concurrent agents and low-latency action loops.

For INT4 quantized models, the company claims up to a 50% improvement in pre-fill performance versus its previous-generation Snapdragon 8 Elite Gen 5. Qualcomm is also targeting Mixture-of-Experts, or MoE, models as large as 30 billion parameters. In its example, the company points to a 30B model that activates only about 3 billion parameters during a given token-generation step. That selective approach is a natural fit for the tight power and memory constraints of a handset.

Qualcomm isn’t suggesting that a smartphone will simply run a 30B dense model entirely from DRAM, however. By activating only the experts needed for a particular task, MoE architectures can dramatically reduce active compute and memory bandwidth requirements, potentially making much larger classes of AI models practical on mobile hardware.

Qualcomm’s next-gen Snapdragon mobile impact

There is a larger competitive story here as well. For years, flagship smartphone competition largely came down to CPU, GPU, modem performance and power efficiency. AI is now another major area of differentiation, and Qualcomm is clearly designing its next-gen Snapdragon mobile platform around this need.

Apple has the advantage of controlling its silicon, operating system and software stack end-to-end. MediaTek continues to push aggressively into premium Android devices as well. Qualcomm’s response is to make its custom Oryon CPU, Adreno GPU and Hexagon NPU work more like a fully integrated compute platform, tuned for modern AI and agentic workloads.

This is a solid advantage for Android handset OEMs because many don’t have the resources to engineer this level of silicon integration themselves. Qualcomm can effectively provide its Android OEM partners, including Samsung, Xiaomi, Honor and others, with a common hardware foundation for on-device AI, advanced graphics and agentic computing. It also gives Qualcomm another way to defend its premium Snapdragon position.

The company’s custom Oryon architecture now spans smartphones and Windows PCs, and soon servers, while AI acceleration is distributed across its CPU, GPU and NPU. For business users, that could mean more AI processing happens locally, reducing cloud and network dependence, improving response times and keeping more potentially sensitive data on-device. And for IT organizations, Qualcomm now has an opportunity to provide a more consistent AI compute foundation across Windows PCs with Snapdragon X and premium Android handsets.

There is still plenty we don’t know, however. Qualcomm hasn’t disclosed the complete SoC specifications, final performance or power characteristics, or which devices will ship with it initially. Many of these AI experiences will also depend heavily on Android, app developers and the models themselves.

The new 5GHz mobile CPU may generate the biggest headline at Snapdragon Summit, but the more important developments may be what sit around it: more local memory, specialized AI acceleration and tighter integration between the CPU, GPU and NPU.

If Qualcomm can translate this architecture into better sustained performance, enhanced power efficiency and useful on-device AI experiences, it could strengthen its position in premium Android phones while putting more competitive pressure on Apple. For business users, the payoff could be more capable local AI without sacrificing the performance and battery life expected of a flagship device.

That is the part I’ll be watching most closely when the complete silicon picture is unveiled at Snapdragon Summit later this month. And I may even get some hands-on time with it in some benchmarks, as we have in years past, so we shall see.

The memory crisis is getting worse for Apple, which is struggling to get enough memory chips together for its upcoming iPhone 18 Pro series smartphones. That’s according to tech journalist Tim Culpan.

As he details it, Apple and its assembly partners are still attempting to secure sufficient quantities of memory, and though they’re confident they can meet initial demand once the devices are introduced, they apparently remain concerned that wait times could rapidly extend as retail inventory evaporates.

Apple’s manufacturing process compounds the problem. The A20 processor is packaged with memory using a new TSMC process, meaning processors are reportedly piling up while manufacturers wait for memory chips. You should read Culpan’s report to get the full picture.

What’s the frequency?

You don’t need to read between the lines to see the challenge. Despite demand for TSMC’s new processors, Apple seems to have been able to secure the supply it needs. But when it comes to churning out the final packaged chips memory, supply constraints have created significant obstacles to producing in quantity.

This is bad for Apple, particularly as the challenge doesn’t appear to be confined to iPhones; customers are experiencing delays getting new Macs. “Many new orders are now not arriving until September,” Bloomberg’s Mark Gurman wrote earlier this week. 

It isn’t just Apple that will be impacted by the AI-driven memory drought. The scale of Apple’s orders is among the greatest in the industry, and if its product plans are feeling the pain, every other manufacturer will be feeling it as well.

Conscious uncoupling

This is certainly in tune with expectations voiced at the beginning of the year when Ranjit Atwal, senior director analyst at Gartner, warned: “This is the steepest contraction in device shipments witnessed in over a decade. Higher prices will narrow the range of devices available, prompting buyers to hold on to devices for longer, fundamentally altering upgrade cycles.”

Gartner in February predicted a 10.4% decline in global PC shipments and an 8.4% drop in smartphone shipments as a result. The analyst also predicted a 130% surge in combined memory and SSD storage prices by the end of this year, with steep product price increases to follow. Recent data from IDC, Gartner, Counterpoint, and Omdia confirm PC market declines, but only at around 4% (the estimates vary).

Today’s report from Culpan suggests we’ve not yet experienced the full extent of this decline — hinting that while the initial fall reflected price, the next impact will be defined by lack of supply. While this hurts big brands like Apple, smaller entities could be left high and dry. 

When the chips are down

It is interesting to reprise Atwal’s warning in February that, “the sub-$500 entry-level PC segment will disappear by 2028,” as this seems to be what’s happening. That’s something long-time Mac users like me find particularly ironic, given it was only this year Apple briefly offered up its superbly priced $499 MacBook Neo. The industry direction we’re seeing now suggests we’ll never see that again, though the success of that device gave Apple a phenomenal 28.7% increase in sales in its just-revealed June quarter.

Despite memory supply challenges, Apple seems to be faring fairly well, with market share increasing across its business. Counterpoint data reveals that Apple has achieved an astonishing 65% share of the premium smartphone market. In part, that’s because as an existing premium brand, Apple was able to better absorb rising memory costs through higher margins and reduced promotions. Realistically, this means we can expect an overall increase in iPhone prices when the new range is announced up to $300 more, Jeff Pu, of GF Securities, recently claimed. 

Building the moat

Once again, what’s sustainable but difficult for larger brands such as Apple is existential disaster for smaller players — and it’s only now a matter of time before we see some real blood. That’s particularly true in smart home and device markets, where manufacturers lack the margins to sustain higher memory prices while delivering products customers can afford. A recent Global Electronics Association report tells us 62% of electronics manufacturers are already experiencing constrained availability or extended lead times. It also tells us 82% expect rising prices, including 33% who cite a “significant increase.”

This is already being felt by consumer and business users, as networking equipment is experiencing significant shipping delays. So, while we may find ourselves waiting a month or more for an iPhone, the wait for new routers, external storage devices, and home automation systems could be even longer once available inventories disappear. 

This doesn’t appear to be a short-term challenge; a recent Digitimes report warns that vendors have already sold their entire allocation of memory capacity for 2027.

Don’t even get me started on the likely impact on the military and defense markets as high-performance memory, storage, and processor supplies become constrained. Just like declining river levels in Europe, lack of memory threatens severe disruption. With so much turbulence impacting the tech economy, all we need now is for one or more of theinvestor-supported AI companies that have helped create the memory shortages to default on loan payments.

Got to keep the customer satisfied

Eager to protect sales, Apple recently introduced the Apple Upgrade leasing service in the US. This should enable consumers to purchase new devices at prices more sustainable to them over time. Apple isn’t alone in taking such action, which will inevitably extend beyond America.

“OEMs are expanding financing, trade-in and buyback programs to improve affordability,” said Counterpoint’s Harshit Rastogi. “Samsung has also expanded its Galaxy Forever program to several markets to make flagship devices more accessible.” 

Such schemes are all well and good, of course. Consumers will embrace them in hopes of a better tomorrow. But the tech industry is not immune to the wider constellation of existential challenges impacting economic environments.

In the end, if Apple, the industry’s biggest buyer of advanced components, is struggling to secure memory, the rest of the electronics sector is likely to face even greater challenges. For consumers, the initial impacts will be longer waits and higher prices. But the longer term consequences could be slower innovation and increased consolidation across the industry.

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Nvidia and Microsoft this month touted the reinvention of computers with a new class of “agentic AI PCs” that will “reinvent the way PCs work.” 

That’s how Nvidia CEO Jensen Huang described the computers at the recent Computex trade show. At the event, Nvidia introduced its first AI-focused PC chip called N1X, which has an integrated CPU and GPU and will be used in agentic AI PCs.

Nvidia’s new RTX Spark PCs are the first in a major “PC reinvention for 40 years,” Huang said, likening them to AI phones. “You could talk to it, it could look at you. You could ask it to read files… [or] go help you do research.”

Not so fast, say analysts, who argue the computers are mostly repackaged AI PCs that shouldn’t necessarily drive enterprise upgrades.

“Agentic AI PCs is a strange term that should probably be deemphasized,” said Leonard Lee, principal analyst at neXt Curve. “Depending on use case, PCs of the last two generations are ‘agentic AI’-capable.”

Skeptical of the hype, analysts said many current PCs are already capable of running agents, such as those spun up on device by OpenClaw. Those agents don’t need to communicate with AI models in the cloud.

In fact, Lee said, Apple has already shown that Windows isn’t needed to run agentic workloads, with the Mac Mini among the most talked-about device for hosting personal AI agents and OpenClaw.

Analysts said the definition of agentic AI PCs really comes down to the hardware inside, which largely defines their capabilities. Most are really just an evolution of earlier AI PCs, which arrived a few years ago amid similar hype.

AI PCs, specifically CoPilot+ PCs, were originally designed with neural processing units (NPUs) to support the requirements of Windows Recall. “Some argue agentic AI PCs need more GPU compute, but…Qualcomm and Microsoft would counter that agentic AI PCs have been around for a while,” Lee said.

To highlight the capabilities of the RTX Spark PC, a demonstration at Computex showed how an architectural design workflow could be split between the PC and the cloud. (An MCP server managed data exchange to facilitate the cloud-PC workflow.)

Adobe has reengineered Photoshop and Premiere for RTX Spark PCs, allowing the software to communicate with AI agents on PCs and tools to run twice as fast, Huang said.

And at the company’s recent Build conference, Microsoft CEO Satya Nadella demonstrated the new Surface Laptop Ultra AI PC based on the RTX Spark design. He positioned agentic AI PCs as running a new execution layer that can act across files and devices, including generating and executing code. 

While AI PCs with agents will be capable, they still lack applications, said Jim McGregor, principal analyst at Tirias Research. “However, PCs — especially the small form factor PCs and workstations — are likely to be the home AI appliance,” he said.

For enterprises, upgrading to RTX Spark PCs at this point could pose problems. That’s because Nvidia’s N1X CPU is based on the Arm processor architecture and could run into compatibility issues with x86 applications designed for Intel and AMD chips.

“Despite Arm processors being available for a while, enterprise penetration stays small, since compatibility with all apps, drivers, and corporate systems is critical and requires extensive testing…, even though Microsoft has done a reasonably good job with Windows 11 on Arm,” said Jack Gold, principal analyst at J. Gold Associates.

Because Microsoft is moving to natively incorporate AI into Windows, those PCs will land in enterprises ultimately, Gold said.

Regardless of hype, it might be a good time for enterprises chasing an agentic AI agenda to upgrade laggard Windows 10 devices, Lee said. “Considering the considerable hype about agentic AI, early adopters will need to upgrade their fleets if they pursue a broad agentic AI agenda for their organizations,” he said.

Apple execs are now acknowledging the company is going to need to raises prices to cope with higher component costs, particularly memory. Apple has been battling to avoid doing so, but warned this week that those efforts are “unsustainable.”

The price pressure is being felt across the tech industry. Omdia predicts the average selling price (ASP) of smartphones globally will increase by around 20% this year — partly because AI industry demands have pushed up RAM prices and partly because of rapid cost increases in processor production as a direct consequence of war in the Middle East. This impacts anything that uses memory or processors.

Apple must raise prices, CEO warns

“We’re doing our best to mitigate the huge increases that are being passed to us, and we’ve been trying to shield our customers from the increases, but the situation has become unsustainable,” current Apple CEO Tim Cook told the Wall Street Journal.

OK, so prices are going to go up. But how?

If Apple must raise prices, the way it does so matters. Will it increase prices across the board, or choose a subtle, strategic approach that protects margins while also maintaining rapidly accelerating market share growth?

Until now, Apple has been benefiting from the rising tide in memory prices. While competitors have been steadily increasing prices across the board, Apple introduced its first $599 laptop even as it eats into mid-range smartphone sales. 

To grow, or to grow less?

The PC industry is not growing (outside of Cupertino), and Apple and its investors already know that the inordinately high customer satisfaction ratings its platforms enjoy mean that when people switch to them they tend to stick around. That’s why it matters that Cook highlighted record switchers and first-time platform users during the company’s most recent fiscal call. Apple is growing new customers, people who are likely to sign-up for services, accessories, and future upgrades. Winning the customer is an investment in future performance.

When it comes to increasing prices, we’ve seen some gentle experimentation in the Mac mini line, where the entry-level $599 model was effectively replaced by a more expensive $799 SKU. There have been suggestions Apple could do the same with the $599 MacBook Neo, dumping the entry-level build in favor of the (better) $699 model. I’m not certain that’s the best approach. 

A matter of choice

I’d argue that the $599 price point is psychologically the best bit of marketing Apple has ever done as it draws new users to look at its platforms. I also think substantial numbers of people already shift up to the $699 version, if they can, making that entry-level price an incredibly effective marketing tool with which to woo customers. It looks like that approach is working; the MacBook Neo has led the Amazon Laptop Sales chart ever since it was introduced and is selling in huge quantities globally.

Francisco Jeronimo, IDC vice president for data and analytics, sees this, writing: “It will be interesting to see whether Apple raises prices across its entire portfolio or plays strategically by subsidizing its more affordable products to gain market share and grow its installed base.”

If Apple raises prices at the price sensitive, entry-level end of its market, it runs the risk of slowing its own resurgence. Given that a 10% price hike here is only worth around $60, the revenue generated might not justify blunting Apple’s offer to new users.

Pricing and strategy

But Apple isn’t defined by its entry-level markets. Its biggest earning opportunities continue to be generated by the aspirational middle class, who, while feeling the pinch of declining living standards in many markets, can still handle the price of a top-of-the-range iPhone Pro. A 10% price increase at that end of the market will generate a much more significant chunk of revenue – and since rival vendors are raising their prices at this end of the market, Apple can follow suit while still seeming affordable. It’s an approach that maintains growth while optimizing margins.

Apple generates roughly half its revenue from iPhone sales, with the Pro and Pro Max devices accounting for a significant chunk of that money. Consumers looking to buy one of Apple’s high-end devices are likely to be less price-sensitive than those making purchases at the low- and mid-points of the company’s market range. 

With that in mind, it makes more sense for the company to raise prices at the top, while working to maintain the cost of entry at the lower end. It could, of course, raise prices universally, but doing so makes less sense.

What next?

Whatever strategy Apple adopts, company leadership is clear that component pricing is hitting hard. “We definitely need memory pricing and supply to return to reasonable levels for consumer products. That’s the bottom line,” Cook said.

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Intel is invading the physical AI space with a reentry into the robotics market it quit many years ago amid financial struggles.

The robotics strategy is part of the company’s larger plan to establish AI on the “edge,” in which devices have the computing capability to run AI locally. Many devices lack AI capabilities and have to offload processing to the cloud.

The chipmaker said its Intel Series 3 processors are now in 130 edge AI and robotics designs. It also had a design win with SensoryAI, which provides technology for robots that include Ella, a robotic barista made by Crown Digital.

The company’s Core Ultra Series 3 processors are derivatives of chip designs intended for laptops. But Intel has achieved a level of power efficiency for long battery life that allows those chips to be adapted for handheld devices and laptops.

Intel also said it can build advanced robotics chips thanks to its latest manufacturing technologies.

For example, many robotic functions, such as computer vision and real-time controls, can be integrated into a single chip. Previously, functions like graphics and movement and control were distributed among different cores in a chip.

SensoryAI, for example, has a chip architecture that provides the robotic barista — which is more like a robotic arm — with AI capabilities, Intel said.

The main “Avatar” agent handles customers as the main “Ella” agent reasons and executes the task. If Ella encounters errors, it passes on the issue to a Guardian agent, which helps with the recovery. Some issues could include making sense of an order, or cups that might be stuck. 

The three agents are embedded in a single piece of Core Ultra Series 3 silicon.

Intel is displaying some of those robots at the Computex trade show in Taiwan. The company shared a video of a humanoid-style robot from the floor in a X.com post  

This is not Intel’s first attempt at the robotics market. Intel sold robotics chips and kits when it was a dominant chip player in the field, but curtailed efforts in 2021 after Pat Gelsinger took over as CEO and restructured the company to focus on manufacturing.

Robotics is now back on the menu under new Intel CEO Lip-Bu Tan, who replaced Gelsinger last year. He has restructured to company to focus on high-growth areas that can generate high returns.

A Morgan Stanley study last year indicated the robotics market could be worth $5 trillion by 2050 — and more than 1 billion humanoid robots could be in operation. 

Robots are seen to improve human productivity and manufacturing output. For example, they could help factories that are facing labor shortages or be used to complete tasks that are dangerous. 

However, challenges remain. There isn’t yet enough real-world data to train robots to do targeted work. And the AI models — generally called world models — they will need are still under development. 

Training robots to do a specific job requires a sequence of events to happen in succession without any errors. Companies are still training robots to spot and understand errors, analyze possible resolutions, and take the right corrective action.