{"id":10298,"date":"2026-07-14T01:23:26","date_gmt":"2026-07-13T23:23:26","guid":{"rendered":"https:\/\/idbokx.tech\/?p=10298"},"modified":"2026-07-16T16:56:08","modified_gmt":"2026-07-16T14:56:08","slug":"from-turing-to-today-why-ai-different","status":"publish","type":"post","link":"https:\/\/idbokx.tech\/index.php\/2026\/07\/14\/from-turing-to-today-why-ai-different\/","title":{"rendered":"From Turing to today: why AI feels different this time"},"content":{"rendered":"

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From Turing to today \u2014
\nwhy AI feels different this time<\/em><\/h1>\n

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Investors have seen tech hype cycles rise and fall before. To separate real structural change from marketing noise, you must understand the fundamental shift in how software is now being made.<\/p>\n

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Start here: The paradigm shift<\/h2>\n

For seven decades, artificial intelligence was deterministic<\/strong>. Software engineers wrote explicit, rigid rules: “If X happens, do Y.” This required humans to anticipate every possible scenario and write code for it. While this worked for basic databases, it made software highly fragile, expensive to maintain, and completely unable to handle messy, real-world data.<\/p>\n

Today, AI is probabilistic<\/strong>. Instead of writing instructions, we feed algorithms massive datasets and let them figure out the patterns themselves. When you interact with a modern AI system, it is not executing a pre-written rule; it is predicting the most likely next step based on everything it has analyzed. This allows software to understand context, reason through complex problems, and adapt without being reprogrammed.<\/p>\n<\/div>\n

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“Legacy software was a machine built from gears we had to design by hand. Modern AI is an engine that grows its own parts. It fundamentally changes the capital required to scale a business.”<\/p><\/blockquote>\n<\/div>\n<\/div>\n

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\n

Why this shift changes your underwriting models<\/h2>\n

For decades, software margins were highly predictable: high gross margins (80%+), offset by significant R&D and Sales & Marketing costs. The new paradigm alters both sides of this equation. R&D productivity is skyrocketing as AI tools help engineers write code 30-40% faster.<\/p>\n

However, if an asset relies heavily on external API calls to power its intelligence, gross margins can degrade quickly under heavy usage. When modeling a target’s future EBITDA, you must stress-test their “AI unit economics.” If their gross margin doesn’t improve as they scale, they are not leveraging the structural advantages of the technology.<\/p>\n<\/div>\n

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\n

The single signal that separates builders from bubble-riders<\/h2>\n

In our due diligence work at idbokx, the most reliable indicator of a company that actually understands this paradigm shift is Workflow Re-engineering<\/strong>.<\/p>\n

Laggards simply bolt an AI chat interface onto their old legacy software and call it a day. Leaders completely redesign their underlying business processes. They look at where humans are slowing down decision-making and use AI to automate the entire pipeline from input to action. Look for companies that are shrinking cycle times, not just adding shiny features.<\/p>\n<\/div>\n

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\n
Go Deeper<\/div>\n

Assessing an asset’s AI maturity and defensibility?<\/h3>\n

Our AI Opportunity & Risk Assessment Framework helps deal teams cut through the generative AI hype, evaluate the defensibility of a target’s data moats, and identify immediate operational efficiency plays.<\/p>\n

Download the checklist<\/a>
\nBrowse all Insights<\/a><\/div>\n<\/div>\n

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Investors have survived multiple “AI winters” and hype cycles. But the shift from rigid, rule-based programming to probabilistic, self-learning models represents a structural break in technology economics. Here is why this AI wave is fundamentally different for software margins, operational scale, and deal valuations.<\/p>\n","protected":false},"author":1,"featured_media":10368,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[83],"tags":[123,85,119,106,108],"technology-dimension":[143,140,139],"class_list":["post-10298","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-cheat-sheets","tag-alan-turing","tag-artificial-intelligence","tag-history","tag-innovation","tag-skills","technology-dimension-architecture","technology-dimension-data","technology-dimension-strategy"],"acf":[],"yoast_head":"\nFrom Turing to today: why AI feels different this time - idbokx.tech<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/idbokx.tech\/index.php\/2026\/07\/14\/from-turing-to-today-why-ai-different\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"From Turing to today: why AI feels different this time - idbokx.tech\" \/>\n<meta property=\"og:description\" content=\"Investors have survived multiple "AI winters" and hype cycles. 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