To wrap up the 2026 predictions mini-series, I wanted to include three themes that I believe will not come fully to fruition by the end of next year. Expectations often outpace technological reality and/or the speed at which humans can adapt to it. After examining six developments poised to transform enterprise AI in 2026, we must confront three interesting absences—capabilities widely anticipated but unlikely to materialise within twelve months. Let’s explore them now.

No, We Still Won’t Be Wearing AR Glasses Next Year

Mass-market augmented reality wearables will remain commercially unviable throughout 2026. Despite accelerated advances in computer vision, on-device AI processing, and miniaturised optics, fundamental hardware constraints remain unsolved.

Battery density represents the primary limitation. All-day wearability demands power systems current materials science cannot deliver in a way consumer would accept it. Displays, compute modules, and optical components remain too bulky for comfortable extended wear. Thermal management compounds these challenges—processors capable of sophisticated AR rendering generate heat incompatible with face-worn devices.

Consumer willingness to adopt face-worn technology remains tepid. Google Glass demonstrated that social acceptability barriers persist even when technical capabilities improve. Privacy concerns, aesthetic resistance, and unclear value propositions limit mainstream adoption regardless of hardware refinement.

On the other hand, 2027 and beyond align with expected breakthroughs in silicon photonics, advanced battery chemistries, and microelectromechanical systems that could unlock practical AR wearables delivering outstanding added value, breaking down adoption and social barriers. But 2026 sits firmly on the early side of these technological transitions. Enterprise applications—warehouse operations, field service, specialised training—will continue adoption, but consumer-grade AR glasses will not achieve the ubiquity some proponents predict.

Strategic marketing leaders should resist allocating resources to consumer AR initiatives in 2026. The technology isn’t ready, the market isn’t prepared, and the opportunity cost of premature investment remains high. Monitor developments, certainly, but recognize that this capability sits beyond the twelve-month horizon.

AGI Remains Out of Reach in 2026

Artificial General Intelligence—systems capable of generalized, autonomous reasoning across domains without human supervision—will not materialize by year’s end. The gap between current capabilities and AGI remains substantial, constrained by fundamental limitations in architecture, compute, and alignment.

Classical computing approaches appear to have reached a scaling plateau. Throwing more parameters and training data at transformer architectures yields diminishing returns. Absent fundamental algorithmic breakthroughs—which remain speculative—we cannot engineer the cognitive flexibility AGI demands through iterative refinement of existing methods.

Quantum computing, which I believe will be a critical pathway to AGI-enabling compute capacity, remains experimental. Coherence times, error correction, and qubit stability challenges keep quantum systems confined to laboratory environments. The timeline from experimental quantum computing to AGI-capable quantum architectures extends well beyond 2026.

Alignment and reliability issues compound technical limitations. Current systems exhibit brittleness, unpredictability, and susceptibility to adversarial inputs that preclude autonomous general intelligence deployment. We haven’t solved the control problem—how to ensure AGI systems reliably pursue intended objectives without harmful deviation. Until we demonstrate robust alignment at narrow AI scale, AGI remains both technically and ethically premature.

The industry trajectory favours specialised, domain-optimised intelligence over generalised systems. This represents pragmatic recognition that narrow AI delivers commercial value whilst AGI remains elusive. Enterprises should invest in targeted AI capabilities solving specific business problems rather than betting on AGI emergence.

I remain sceptical of timelines placing AGI within the next twenty-four months. The challenges aren’t merely engineering problems requiring additional resources—they represent fundamental knowledge gaps in how to construct generally intelligent systems. Prudent leaders will distinguish between aspirational vision and actionable strategy.

Robots Won’t Be in Every Home Yet—The 2027 Revolution Comes Later

Consumer robotics will not experience a breakthrough year in 2026. Whilst advances in manipulators, dexterous end effectors, and humanoid prototypes will demonstrate technical progress, the consumer market won’t achieve the affordability, safety certification, and reliability necessary for mass adoption.

Cost structures remain prohibitive. Actuators, sensors, and precision mechanical components required for capable home robotics far exceed consumer price tolerance. Manufacturing scale and component commoditisation—prerequisites for affordable consumer electronics—haven’t materialised for sophisticated robotics.

Reliability and safety standards for home environments demand validation current systems cannot demonstrate. Domestic settings present unstructured complexity: children, pets, fragile objects, unpredictable scenarios. Robots must navigate these environments without causing harm or requiring constant human intervention. We’re not there yet, as 2025 demonstrated with the most cutting edge robots being remotely controlled by humans to perform tasks as simple as dusting off a shelf.

Enterprise and industrial robotics will advance more rapidly than consumer versions. Controlled environments, task specificity, and higher price tolerance accelerate adoption in manufacturing, logistics, and specialised commercial applications. These deployments will inform eventual consumer offerings, but the timeline extends beyond 2026.

Consumer robotics follows a familiar technology adoption pattern: industrial application precedes domestic availability by years. The infrastructure—supply chains, manufacturing capacity, regulatory frameworks—develops incrementally, not overnight. Expectations of imminent home robotics breakthrough reflect enthusiasm disconnected from development realities.

Recalibrating Expectations

These three “non-predictions” share a common characteristic: they represent capabilities constrained by fundamental rather than incremental challenges. AR wearables need materials science breakthroughs. AGI demands algorithmic paradigm shifts. Consumer robotics requires manufacturing and safety infrastructure that develops gradually.

Distinguishing between imminent and aspirational capabilities determines strategic resource allocation. Leaders who accurately calibrate technology timelines avoid premature investments whilst positioning organisations to capitalise once capabilities mature.

The question for 2026: are your transformation roadmaps anchored in realistic assessments of what technology can deliver, or inflated by aspirational narratives disconnected from developmental constraints?

Tempering enthusiasm with pragmatism will sharpen execution.

Happy 2026 everyone, what an exciting time to be alive!

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Disclaimer: The views and opinions expressed in Chronicles of Change and on my social media accounts are my own and do not necessarily reflect the official policy or position of S&P Global.