Technology
By Sakamoto Nashi
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What happened. The promise of artificial intelligence has often been equated with seamless efficiencies and rapid…
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The historical context. The dot-com bubble is the obvious comparison, and it's probably the right one.
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Why it matters. Companies are figuring out, sometimes the hard way, that dropping AI into an engineering workflow…
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What to watch. Oracle's earnings on June 16, 2026 are the clearest near-term signal.
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The promise of artificial intelligence has often been equated with seamless efficiencies and rapid returns. But the numbers tell a messier story.
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A survey of 2,444 companies by Entelligence AI found that up to 82% of enterprise AI spending gets swallowed before anything useful ships. Bug fixes. Code rewrites. Review delays.
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Lightrun's 2026 State of AI-Powered Engineering Report adds more weight to that picture. Nearly half of AI-generated code still needs manual debugging even after clearing quality…
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Meanwhile Oracle has been making aggressive bets on AI infrastructure, and the debt load it's carrying is staggering — $108 billion in total debt, with additional capital raised…
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And OKX, the crypto exchange, is reworking how it evaluates talent. AI proficiency is now a factor in employee assessments.
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The dot-com bubble is the obvious comparison, and it's probably the right one. In the late '90s, capital flooded into internet infrastructure and unproven business models at a…
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The AI moment feels similar. Oracle's financial posture — massive debt, aggressive infrastructure buildout, reliance on a loss-making anchor client — mirrors the kind of…
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Read also: Ripple Moves $79 Million in XRP to Mystery Wallet as Price Cracks $1.30
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The code quality problem has its own historical echo. Early commercial software was famously unreliable.
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Companies are figuring out, sometimes the hard way, that dropping AI into an engineering workflow isn't a plug-and-play upgrade. It's a strategic overhaul.
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Winners here are probably the companies that went in with realistic expectations — that built error-correction capacity alongside AI deployment, rather than assuming the tools…
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