Will there be new Macs at WWDC 2026?


Despite WWDC being a software conference, there are sometimes Mac releases, and there are always predictions that there will be, but it’s very unlikely this time.

Whether it’s fuelled by wishful thinking or click bait, WWDC coverage invariably includes commentary on new Mac releases. And it invariably goes that before the event, it is rumored or certain or sometimes apparently confirmed, that there will be new Mac hardware.

Then almost always, there isn’t. And then immediately after the event, the coverage is about how there was never going to be any new Mac hardware, that’s coming in the next event.

At risk of being another piece of commentary on this same issue, here’s why 2026 will almost certainly not include any new Mac hardware.

It’s a software conference.

Plus there is history here, specifically this history. From 2015 to 2025, there were seven without Mac hardware, and four with.

The four WWDC events with new Macs were:

  • 2017: MacBook, MacBook Pro, iMac, iMac Pro
  • 2019: Mac Pro, Pro Display XDR
  • 2022: M2 MacBook Air, M2 13-inch MacBook Pro
  • 2023: 15-inch MacBook Air, M2 Max and M2 Ultra Mac Studio, M2 Ultra Mac Pro

You can make a case that 2020 featured the Apple Silicon developer kit, but there wasn’t anything for the public. Apple announced it then partly because Apple Silicon made a significant difference for app developers, but also because WWDC is a spotlight.

If Apple wants to promote something, it either puts it in WWDC or maybe holds it for the iPhone launch. In both cases, Apple is getting the maximum coverage and attention then than it does all year.

This is why the Apple Watch was launched alongside the iPhone, although originally the Apple Watch was particularly dependent on the phone. Apple is very good at choosing its moments to launch devices, and it’s really hot on how not to dilute attention by releasing too much at once.

So back in 2017, Apple wanted to show pro users that it hadn’t abandoned them, as had persistently been rumored just before then. Apple used that WWDC to reveal the iMac Pro, even though the firm was clear that it wouldn’t ship until December 2017.

In 2019, it was a similar story, since users had begun to wonder if Apple would ever update its flagship Mac Pro. Under that global spotlight, Apple revealed that it had, and that it was also back in the monitor business with the Pro Display XDR.

Top view of a sleek dark desktop setup with curved monitor back, stand, extended keyboard, large trackpad on the left, and wireless mouse on the right against black background

Apple’s iMac Pro

So Apple certainly does bring out Macs at WWDC when there’s a bigger purpose to it. But then there’s also the release of the MacBook Air at WWDC 2022.

In that case, it was a radical redesign, as this is when the MacBook Air lost its famous wedge-shape design. It’s just not so obvious a headline that Apple was looking for.

Nor, really, was 2023 with its 15-inch MacBook Air. That did bring the larger screen size, a first for that model, but it wasn’t an answer to loud and vocal public demand.

With both of these years, 2022 and 2023, there were also updates to the MacBook Pro, the Mac Pro, or the Mac Studio, but again they were just updates.

And they were also as close to being a sideshow as they ever could be, because this was the year Apple announced the Apple Vision Pro.

Heading into WWDC 2026

If Apple wants to make a lot of noise about new Macs, it does have WWDC in which to do it. But it’s true that Apple has launched Macs that weren’t headline-shaking, plus this time it didn’t save the MacBook Neo for WWDC 2026.

Consequently, despite it being statistically less likely that there will be new Macs, it could still happen. Except there’s no one calling for an improved Mac Pro, and they won’t get one if they did.

Mac Studio buyers want still faster processors, but they’re not complaining about the lack of a redesign. It’s the same with Mac mini users, you might want more and faster, but you don’t feel abandoned.

There are just no obvious gaps in the Mac lineup at the moment. Given how much of a hit the MacBook Neo is, Apple could well be looking to do more low-cost Macs, but as yet there’s no indication that will happen this year.

Open pink MacBook on a wooden desk displaying a tech news website, with external monitor, keyboard, and various gadgets in the background of a tidy home office setup

The MacBook Neo has been a big hit due to its price

Then there’s this. WWDC is a great time to release important new Macs, but 2026 is a bad time to do it because of the global chip shortage.

If Apple did launch some new Mac at WWDC 2026, it would have to be something pretty special. If it were, the odds are that it would be an enormous hit.

And even Apple would surely struggle to add another MacBook Neo-like hit to its lineup when it’s reportedly struggling to get enough processors for its current models.

It’s not impossible that Apple will release new Macs at WWDC, it’s never impossible. But WWDC remains a software conference, Apple’s Mac releases there tend to be important updates, and there’s a shortage of processors.

Don’t expect new Macs at WWDC 2026, then. But do expect the next version of macOS, and at least that can make your old Mac feel a little new.



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ZDNET’s key takeaways

  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

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  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

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McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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