AI development breaks tech tradition with its unpredictable updates, not anticipated annual cycles

AI’s rapid evolution is fundamentally different from previous tech revolutions like smartphones and social media, with models updating continuously rather than following predictable annual cycles. This shift challenges traditional tech industry patterns, creating a landscape where being first doesn’t guarantee long-term dominance and where companies face both accelerated development timelines and unpredictable performance outcomes.

The big picture: Unlike smartphones that follow predictable release cycles, AI models evolve constantly with updates that can be “fast and furious,” making them far more unpredictable than cyclical tech products.

• “(AI models) are launching far, far faster than once a year. These updates are actually fast and furious,” said Oren Etzioni, former CEO of the Allen Institute for Artificial Intelligence, a nonprofit research organization.
• “These models can be opaque, unpredictable (and) difficult to measure because they’re so general.”

High-profile delays reveal AI’s complexity: Major tech companies are struggling with AI timelines as performance improvements prove difficult to quantify and predict.

• Apple has indefinitely delayed its revamped Siri, with executives citing the need for “more time to reach our high-quality bar.”
• OpenAI has yet to release its anticipated GPT-5 model, while Meta reportedly pushed back its next major Llama model launch.
• Performance issues emerge unpredictably—ChatGPT became “annoying” after a May update, and xAI’s Grok chatbot went on unprompted rants about controversial topics.

AI adoption accelerates despite delays: Business adoption continues climbing even as companies navigate development challenges.

• 78% of businesses reported using AI in 2024, up from 55% in 2023, according to Stanford University’s 2025 AI Index Report.
• AI performance has significantly improved from 2023 to 2024, demonstrating rapid technological advancement.

Network effects don’t apply: AI platforms operate differently from social media and smartphones, which benefited from users bringing friends and family onto the same platforms.

• “It’s people doing their own individual tasks,” said Darrell West, senior fellow at the Brookings Institution’s Center for Technology Innovation, a Washington-based research organization. “It’s not like the platform becomes more valuable if all your friends are on the same platform.”
• AI assistants become more valuable as they learn individual preferences rather than through social connections.

First-mover advantage may not matter: The AI industry could break traditional tech patterns where early leaders like Apple, Google, and Meta maintained dominance for over a decade.

• “It seems like it won’t be a sort of ‘winner-takes-all’ market,” said Daniel Keum, associate professor of management at Columbia Business School.
• Users may employ multiple specialized AI services for different tasks, reducing platform lock-in effects that characterize smartphone ecosystems.
• “Even if I fall behind, like a quarter generation, I can easily catch up,” Keum explained. “And once I improve, people will come back to me.”

Why this matters: This evolution suggests the AI industry may be more competitive and dynamic than previous tech revolutions, with opportunities for companies to enter and succeed even after rivals establish early leads.