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​I don't think that's right. AI did change the economics of software, but not evenly. It did not suddenly make every category of SaaS irrational, and it did not erase the advantages of vendor scale, compliance programs or integrated platforms.

​What it changed most was the cost of building the layer around a business: the dashboards, internal tools, workflow interfaces and decision-support systems teams live in every day. The core system underneath those workflows is still often worth buying. The workflow wrapped around it increasingly is not.

​The old answer was usually 'buy.'

​For a long time, the buy-versus-build decision was mostly settled by cost. Even when off-the-shelf software fit badly, most companies still bought it because the alternative was too expensive to justify. That logic made sense. SaaS vendors spread development cost across many customers, and buyers accepted awkward workflows as the price of avoiding a custom build.

​What AI changed was not the desirability of fit. Companies have always wanted software that matched how they actually work. What changed was the price of getting closer to that fit. From what I see, the cost of building useful software at the edges of the business has dropped enough that old assumptions deserve a second look.

​The edge moved faster than the core.

​There is a meaningful difference between replacing a core system of record and replacing the rough workflow layer sitting on top of it. Systems of record still carry real weight. They hold the historical data, sit inside dense integration webs, support audit requirements and often serve as the operational source of truth for multiple teams. Replacing them is still expensive, risky and, in many cases, unnecessary.

​The friction I see is in the layers teams touch all day: the extra screens, awkward handoffs, narrow tasks buried inside broad software and workflows that almost fit but never quite do. That is where business-specific nuance matters most, and where vendor generalization often fits least.

​AI changed the cost of building that layer much faster than it changed the cost of replacing the core beneath it.

​That distinction is already showing up in the market. In April 2026, The Wall Street Journal reported that large companies were not ripping out platforms like Salesforce, SAP and Workday. They were negotiating harder, customizing more and building smaller AI-driven tools around those systems instead. That is much closer to what I expect than a full-scale replacement cycle.

​Breadth used to be a moat. Now it can be a tax.

​For years, buyers tolerated mismatch. If a team used a narrow slice of a large product, but that slice solved a necessary problem, the waste was annoying but acceptable. Building a custom alternative would have cost too much.

​Now the question is harder to avoid. If your team only needs a narrow slice of capability, and the rest of the product mostly introduces complexity, you are no longer just buying software. You are subsidizing breadth you do not need in order to access the part that matters.

​That does not mean every narrow use case should become an internal build. It does mean feature breadth is becoming harder to defend as an automatic advantage.

​What should still be bought?

​If the platform is your source of truth, carries real regulatory burden or sits at the center of a dense and valuable integration ecosystem, it probably still belongs in the buy column.

​Even recent research pushing back on the broad "SaaSocalypse" thesis lands in roughly the same place. A 2026 paper on agentic AI and enterprise software economics argues that the collapse story is overstated, while also finding that building is most compelling for commodity utilities and differentiating custom applications, and much less compelling for regulated and mission-critical systems.

​That tracks with what I see. If the software is the ledger, the governed system or the operational backbone, you probably still want to rent it.

​What should now be questioned?

​The more interesting category is the software layer your team fights with every day: workflow overlays, internal dashboards, task-specific interfaces, narrow decision-support flows and process tools that are highly local to how your business actually runs. These are often the places where a company has adapted its behavior to fit the software, instead of the software fitting the business.

​If the real frustration is not in the data model underneath but in the experience wrapped around it, owning that layer may now create more leverage than continuing to subscribe to a generic version of it. In the right cases, teams feel that benefit quickly in speed, usability and process fit.

​This is why I think leaders should stop treating buy-versus-build as a philosophical preference and start treating it as a layer question.

Here's a better test for the next renewal.

​The next time a software renewal comes up, the useful question is not whether your company should become anti-SaaS. It is whether you are renting the right layer.

​Is this platform your system of record, or is it mostly a workflow wrapper around work you already understand well? Is the pain sitting in the core, or in the interface around the core? Would owning this layer let you operate in a way that generic software never quite will?

​I think the companies that benefit most from AI will not be the ones that try to rebuild everything. They will be the ones that finally separate what must be rented from what should be owned.

​AI did not eliminate the need to buy software. It made the old boundary lazy. Keep the systems that earn their centrality. Rebuild the friction your team lives inside. That is where the economics moved.​

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On busy mornings, I love having a stash of these oatmeal bars on hand. They’re a perfect grab-and-go breakfast, packed with hearty oats, walnuts, dried cranberries, and eggs to hold them together. They have a delicious soft-baked texture, and brown sugar and cinnamon fill them with sweet, spiced flavor. With two kiddos in the house, I rarely have time to make actual oatmeal on a weekday, but lucky for me, these oatmeal breakfast bars are even better!

I adapted this oatmeal bars recipe from my baked oatmeal. When I started working on them, my goal was for them to have one key difference from the baked oatmeal recipe: I wanted them to be cohesive enough to eat by hand. It took four tests to get there, but I couldn’t be happier with the results.

These oatmeal breakfast bars are chewy, flavorful, and easy to make in one bowl. And best of all, they keep in the fridge for the better part of a week. If you need meal prep breakfast ideas for back-to-school season, these yummy bars are for you!

Oatmeal Bars Ingredients

Here’s what you’ll need to make this oatmeal bars recipe:

• Whole rolled oats, of course! Also called old fashioned rolled oats, they make up the base of these bars. Be sure to use certified gluten-free oats if you need the bars to be gluten-free.
• Eggs – A key ingredient in this recipe, they bind the oats together. Fun fact: I originally tested these bars with 2 eggs, but I found they needed 3 to hold together!
• Brown sugar and unsweetened applesauce – For sweetness. I’ve also made these bars with pure maple syrup instead of brown sugar for a 100% naturally sweetened option. They just need to bake for an extra 5 minutes to account for the extra liquid.
• Milk – For moisture. Feel free to use whatever kind you keep on hand. Regular milk, oat milk, and almond milk all work well.
• Melted coconut oil – It adds richness and helps the bars brown in the oven. If you don’t keep coconut oil on hand, feel free to substitute another cooking fat. I’ve successfully used avocado oil in this recipe, and I think vegetable oil and melted butter would work too.
• Baking powder – It helps the oatmeal bars rise.
• Cinnamon and vanilla extract – For warm depth of flavor.
• Dried cranberries and walnuts – My go-to mix-ins! Any combination of dried fruit and nuts would work well in their place. Try adding dried cherries, dried blueberries, or raisins, or use pecans or pepitas in place of the walnuts. You could even add mini chocolate chips.
• And sea salt – To make all the flavors pop!

Find the complete recipe with measurements below.

How to Make Oatmeal Breakfast Bars

This baked oatmeal bars recipe is easy to make in one bowl:

Start by whisking together the wet ingredients—the eggs, applesauce, brown sugar, milk, coconut oil, and vanilla.

Then, add the baking powder, cinnamon, and salt. Sprinkle them over the egg mixture and whisk to combine.

Next, add the oats, chopped walnuts, and cranberries. Stir to coat in the egg mixture.

Let the oat mixture stand for 20 minutes. Don’t skip this step! It’s crucial for hydrating the oats, giving the bars an even moisture level throughout. It’s also a great opportunity to grease an 8×8-inch pan, line it with parchment paper, and preheat the oven.

After 20 minutes, you’re ready to bake the bars. Transfer the oat mixture to the prepared pan and spread it in an even layer. Bake at 350°F for about 35 minutes, or until the center is set.

Let cool completely in the pan before cutting into 16 bars or 9 bars, as desired. Enjoy!

Recipe Tips

• Chop up the mix-ins. In my early tests, I found that whole dried cranberries and large walnut pieces were difficult to cut through when I was slicing the bars, which made parts of the bars crumble off. Now, I always chop the cranberries and nuts into small pieces to help the bars hold together.
• Let the batter stand for 20 minutes. I skipped this step the first two times I tested this recipe, and I found that the bars had an overly moist, gummy layer underneath. Letting the batter rest for 20 minutes gives the oats a chance to soak up some of the moisture before baking so that they don’t end up with that too-wet bottom layer.
• Let the bars cool completely before slicing and serving. These bars hold together best if you let them cool to room temperature before serving. If you serve them sooner, they’ll have a soft, crumbly texture similar to this baked oatmeal.

How to Store and Freeze Oatmeal Breakfast Bars

To store: Allow the bars to cool to room temperature. Seal them in an airtight container and store in the refrigerator for up to 4 days.

• Make sure to let the bars cool fully first. They’ll fall apart if you seal them in a container while they’re still warm.
• They’re best kept in the fridge. They get even firmer and chewier. By contrast, they soften if you store them at room temp.

To freeze: Let the bars cool completely, then seal in an airtight container and freeze for up to 3 months. To thaw, quickly defrost one bar at a time in the microwave, or transfer them to the fridge to thaw overnight.

More Oatmeal Recipes to Try

If you love these oatmeal breakfast bars, try one of these delicious oatmeal recipes next:

• Baked Oatmeal
• Oatmeal Breakfast Cookies
• Overnight Oats
• Homemade Granola Bars

Looking for more healthy breakfast inspiration? Check out these 60 Healthy Breakfast Ideas!

rate this recipe:

5 from 1 vote

Prep Time: 30 minutes

Cook Time: 35 minutes

Total Time: 1 hour 5 minutes

Serves 9 or 16

This oatmeal bars recipe is a great on-the-go breakfast or snack! The bars are soft, chewy, and flavorful, packed with dried fruit and nuts. They keep well for 4 days in the fridge, so they're perfect for meal prep.

• 3 large eggs
• ½ cup brown sugar*
• ½ cup unsweetened applesauce
• ⅓ cup milk
• ¼ cup melted coconut oil
• ½ teaspoon vanilla extract
• 1½ teaspoons baking powder
• 1½ teaspoons cinnamon
• ½ teaspoon sea salt
• 2 cups whole rolled oats
• ½ cup chopped dried cranberries or cherries
• ½ cup chopped walnuts

• In a large bowl, whisk together the eggs, brown sugar, applesauce, milk, coconut oil, and vanilla. Sprinkle the baking powder, cinnamon, and salt on top and whisk until smooth. Add the oats, cranberries, and walnuts and mix with a spatula to combine.

• Set the mixture aside for 20 minutes to hydrate the oats. Meanwhile, preheat the oven to 350°F and grease an 8x8-inch square baking dish. Line the bottom and two sides with a sheet of parchment paper.

• Spread the oat mixture evenly in the prepared pan. Bake for 32 to 37 minutes, or until the middle is set.

• Allow to cool completely in the pan before slicing and serving.

• Store cooled bars in an airtight container in the refrigerator for up to 4 days.

*Brown sugar substitute: I've also made these bars with maple syrup for a naturally sweetened option. Bake for an extra 5 minutes to account for the extra moisture.

Nutrition facts calculated for 1/9 recipe

Nutrition Facts

Oatmeal Breakfast Bars

Amount Per Serving (1 bar)

Calories 262 Calories from Fat 117

% Daily Value*

Fat 13g20%

Saturated Fat 6g38%

Trans Fat 0.01g

Polyunsaturated Fat 4g

Monounsaturated Fat 2g

Cholesterol 55mg18%

Sodium 230mg10%

Potassium 158mg5%

Carbohydrates 33g11%

Fiber 3g13%

Sugar 19g21%

Protein 6g12%

Vitamin A 94IU2%

Vitamin C 0.3mg0%

Calcium 88mg9%

Iron 1mg6%

* Percent Daily Values are based on a 2000 calorie diet.

“We thought if we could remove the Y chromosomes immediately after fertilisation, we can change the sex,” says Takashi Ishiuchi at the University of Yamanashi, Japan. “It is essentially sex reversal.”

The method may become useful for conserving animals that are so close to extinction they are down to just one male or only a few.

In animals that reproduce sexually, running out of males or females can mean the end of the line for a population or species. However, some species can turn to asexual reproduction, through budding off new organisms, such as jellyfish-like Hydra, or by parthenogenesis, in which unfertilised eggs of animals including lizards, stick insects and sharks can develop into a new animal. This produces offspring that are identical clones of the parent and the same sex. Clones created in labs, such as Dolly the sheep, are made through a similar process.

A few animals, like Okinawa rubble gobies (Trimma okinawae), can change sex to ensure sexual reproduction can continue. Inspired by these colourful marine fish, Ishiuchi and his colleagues came up with a method to change the sex of a male clone embryo, so sexual reproduction between individuals could take place – albeit exceedingly closely related ones.

Some attempts at cloning male mice have, by chance, produced a female when the male-determining Y sex chromosome was deleted, leaving the specimen with just one X chromosome (known as XO), rather than an XY pair.

Ishiuchi and his colleagues developed a CRISPR-based tool called Y-CUT to reliably eliminate the Y chromosome from male mouse embryos.

First, they inserted DNA from the blood cells of a male mouse into mouse egg cells that had already had their own DNA removed – a technique called somatic cell nuclear transfer. They treated some of these embryos with Y-CUT and transferred them to surrogate female mice. 

All of the mice born after the use of Y-CUT were female clones – genetically identical to the father apart from lacking the Y chromosome. Other offspring, which hadn’t undergone Y-CUT, were all male clones.

“We call this dual-sex cloning,” says team member Shogo Matoba at the RIKEN BioResource Research Center in Tsukuba, Japan.

Introducing the father mouse to his clones was a very special moment, says Ishiuchi. “He could see the female version of himself, which is like sci-fi.”

The clones were fertile, and when they mated together, they produced offspring that seemed healthy. “This means sexual reproduction was initiated from a single male genome,” says Matoba. “The kids have survived over a year so far with no clear defects.”

Ishiuchi hopes that the Y-CUT technique could help revive species that are highly endangered or extinct, and for which only frozen tissue remains in cell banks.

“It would be incredibly valuable if this were successful in species other than mice,” says Teruhiko Wakayama, also at the University of Yamanashi, who wasn’t involved in the study.

Ben Novak at wildlife conservation organisation Revive & Restore, which has been involved in cloning efforts with endangered species including Przewalski’s horse and the black-footed ferret, thinks similarly. “They are laying the foundations for a future of manipulating chromosomes that is probably going to be important in many cases for conservation,” he says.

Novak agrees the technique may be useful for frozen tissue samples from endangered or extinct species, where we have samples from only one or two individuals.

A previous study generated both male and female clones from a male mouse embryonic stem cell line, but it used a method that is unlikely to work in other species, unlike somatic cell nuclear transfer, which has been successfully applied to many mammal species and works with living or stored donor material.

One issue is that, though an XO female mouse is healthy and fertile, that isn’t the case in many other species.

“Y-deleted offspring are not fertile in humans and horses, and therefore this is likely to be the case in multiple wildlife species,” says Linda Penfold of SEZARC, a group of zoos and conservation centres based in the US. She also says there could be ethical concerns with producing Y-deleted animals.

Ishiuchi accepts this, but notes that there is research showing that a drug called Reversine can make XO cells gain a second X chromosome, so it might be possible to do this in combination with Y-CUT to make resulting females fertile in a wider range of species.

Penfold also warns that breeding from clones of a single animal would create genetic issues. “There is a lack of genetic diversity, which is critical for healthy populations,” she says.

Novak is more positive about that aspect. “Sexual reproduction with one individual is certainly better than no sexual reproduction,” he says, but he adds that we should do everything we can now to never have to use such tools. “While they’re awesome, their use indicates that you’ve gotten to a situation that’s so dire that you need sex reversal,” he says.

Habitat protection and reducing hunting will always be much more effective at conserving species, says Penfold. %!s()

It is understandable, then, that employees and executives are still building trust. According to recent survey from my company, Prosper Insights & Analytics, 80% of employees surveyed don’t think agentic AI is a good idea, while global employment company G-P’s AI at Work report found that around half of global executives believe the AI bubble could burst in the next year.

But skepticism doesn’t signal a lack of opportunity, it raises the bar and is a normal sign of technology maturing. Preparing for market adjustments isn’t just about being cautious. It’s a smart growth strategy. Establishing strong practices today protects your business from market swings, moving faster and capitalizing on new opportunities.

From AI Hype to Utility

Deloitte research shows that while many companies are experiencing productivity gains from AI, the extent to which they’ve transformed their businesses with AI varies. More than one-third (37%) of projects were classified as “surface level," with little or no change to existing processes, 30% “redesigning key processes” around AI but keeping their business models intact and 34% “deeply transforming” creating new products and services, reinventing core processes, or even fundamentally changing their business models. A broad “pilot-everything” approach is no longer effective. Instead, companies should work on targeted rollouts that solve specific operational bottlenecks.

MORE FOR YOU

“Every AI deployment should answer one simple question: ‘Does this solve a real business problem?’” says Nat Natarajan, COO at G-P. “Even when the answer is yes, AI isn’t ‘set it and forget it’.”

He continued: “Continuous refinement isn’t a sign of failure - it’s how you improve and increase AI’s value. It also shows your employees you’re focused on solving real pain points, not just chasing trends.”

Strategic Reallocation, Not Failure

Goldman Sachs Research’s Jim Covello recently said: “The economics of artificial intelligence are more questionable today than two years ago.” As AI costs rise and expectations for returns grow, companies may be tempted to pull back if early investments fall short. G-P’s 2026 AI at Work report found that 70% of executives are likely to scale back AI budgets if profit goals are not met this year. But scaling back investment across the board risks putting companies at a strategic disadvantage.

The better approach is thoughtful reallocation: pressure test AI initiatives, moving resources away from tools that aren’t delivering and investing more deeply in those that demonstrate value. That kind of recalibration isn’t a retreat; it’s a more mature AI strategy.

Accuracy over Speed

Employee trust relies heavily on accuracy. Prosper Insights & Analytics research found that just 9.7% of surveyed employees would trust agentic AI enough to use it in their jobs, even as usage is expected to continue to rise in the next two years.

Both executives and employees listed the top two concerns being the need for human oversight and inaccurate or hallucinatory outputs.

Similarly, G-P’s AI at Work report found that only 23% of executives globally have complete confidence in the accuracy of responses or documents produced by their organization's AI tools, and 69% of executives report the time employees spend monitoring, reviewing or updating work performed by AI has increased over the past year.

“Mitigating risks like AI hallucinations is critical to using AI effectively,” Natarajan adds. “A human-in-the-loop approach can accelerate workflows while ensuring the right level of oversight and accuracy. For example, AI can draft a contract quickly, but an experienced HR or legal professional should review the final document. In a high-stakes area like HR or legal, it’s far better to be thorough and accurate than fast and wrong. Accuracy builds trust, and once you have trust, speed follows.”

Turning Challenges into Opportunities

Whether the AI market cools or stabilizes, businesses are entering a more practical phase of adoption. The initial rush to embrace AI for its own sake is giving way to a healthier mandate – proving tangible business value. By pairing the scale and speed of AI with human judgement and oversight, organizations can build trust among their workforce, maintain quality and focus investment on tools that deliver real results.

A high-return, low-risk AI strategy isn’t about moving slower, it's about moving smarter. Companies that shift from trend-chasing to problem-solving will be better positioned to adapt, scale and realize AI’s long-term business value.

Disclosure: The consumer sentiment study referenced above was conducted by my company, Prosper Insights & Analytics. This is the same dataset used by the National Retail Federation, and available from Amazon Web Services, Databricks, and the London Stock Exchange Group for economic benchmarking. %!s()

I've rounded up six for your consideration, with the cheapest being this Logitech G321 Lightspeed for just $49. Our James declared it "light on the ears, heavy on value" in his review, and at this price it offers even more bang for your buck.

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Wireless gaming headset deals

Keep up to date with the most important stories and the best deals, as picked by the PC Gamer team.

Andy built his first gaming PC at the tender age of 12, when IDE cables were a thing and high resolution wasn't. 26 years later (yes he's getting old), he now spends his days writing about and reviewing graphics cards, CPUs, keyboards, mice, gaming headsets and much, much more. You name it, if it's PC gaming hardware he'll write words about it, with opinions and everything. %!s()