<\/p>\n What makes the discussion notable in hindsight is not the optimism around AI, which was common at the time, but Ellison\u2019s repeated insistence on limits.<\/p>\n You may like<\/p>\n While others described AI as a new architectural layer or even a “new species” of software, Ellison argued that intelligence should be applied sparingly, embedded deeply, and never treated as a universal solution.<\/p>\n <\/p>\n \u201cOur primary interest at Oracle is applying expert system technology to the needs of our own customer base,\u201d Ellison said. \u201cWe are a data base management system company, and our users are primary systems developers, programmers, systems analysts, and MIS directors.\u201d<\/p>\n That framing set the tone for everything that followed. Ellison was not interested in AI as an end-user novelty or as a standalone category. He saw it as an internal tool, one that should improve how systems are built rather than redefine what systems are.<\/p>\n Many vendors treated expert systems as a way to replicate human decision making wholesale. Kehler described systems that encoded experience and judgment to handle complex tasks such as underwriting or custom order processing.<\/p>\n Landry went further, arguing that AI could form the architecture for an entirely new generation of applications, built as collections of cooperating expert systems.<\/p>\n Ellison pushed back at this notion, prompting moderator Esther Dyson to ask: “Your vision of AI doesn\u2019t seem to be quite the same as Tom Kehler\u2019s, even though you have this supposed complementary relationship. He differentiates between the AI application and the data base application, whereas you see AI merely as a tool for building data bases and applications.”<\/p>\n \u201cMany expert systems are used to automate decision making,\u201d Ellison replied. \u201cBut a systems analyst is an expert, too. If you partially automate his function, that\u2019s another form of expert system.\u201d<\/p>\n You may like<\/p>\n Ellison drew a clear line between processes that genuinely require judgment and those that don’t. In doing so, he rejected what might now be called AI maximalism.<\/p>\n \u201cIn fact, not all application users are experts or even specialists,\u201d he said. \u201cFor example, an order processing application may have dozens of clerks who process simple orders. Instead of the order processing example, think about checking account processing. Now, there are no Christmas specials on that. There are no special prices. Instead, performance is all-critical, and recovery is all-critical.\u201d<\/p>\n Business God | Software Billionaire | YouTube Documentary – YouTube Watch On <\/a> When Dyson suggested a rule such as automatically transferring funds if an account balance dropped below a threshold, Ellison was blunt.<\/p>\n \u201cThat can be performed algorithmically because it\u2019s unchanging,\u201d he said. \u201cThe application won\u2019t change, and to build it as an expert system, I think, is the height of nonsense.\u201d<\/p>\n This was a striking statement in 1987, when expert systems were widely promoted as the future of enterprise software. Ellison went further, issuing a warning that sounds surprisingly modern.<\/p>\n \u201cAnd so I say that a whole generation is going to be built on nothing but expert systems technology is a misuse of expert systems. I think expert systems should be selectively employed. It is human expertise done artificially by computers, and everything we do requires expertise.\u201d<\/p>\n Rather than applying AI everywhere, Ellison wanted to focus it where it changed the economics or usability of system development itself. That led him to what he called fifth-generation tools, not as programming languages, but as higher-level systems that eliminated procedural complexity.<\/p>\n \u201cWe see enormous benefits in providing fifth-generation tools,\u201d he said. \u201cI don\u2019t want to use the word \u2018languages,\u2019 because they really aren\u2019t programming languages anymore. They are more.\u201d<\/p>\n He described an interactive, declarative approach to building applications, one where intent replaced instruction.<\/p>\n \u201cI can sit down next to you, and you can tell me what your requirements are, and rather than me documenting your requirements, I\u2019ll sit and build a system while we\u2019re talking together, and you can look over my shoulder and say, \u2018No, that\u2019s not what I meant,\u2019 and change things.\u201d<\/p>\n The promise was not just speed, but a change in who controlled software.<\/p>\n \u201cSo not only is it a productivity change, a quantitative change, it\u2019s also a qualitative change in the way you approach the problem.\u201d<\/p>\n Larry Ellison on the Race for AI – YouTube Watch On <\/a> That philosophy carried through Oracle\u2019s later product strategy, from early CASE tools to its eventual embrace of web-based architectures. A decade later, Ellison would argue just as forcefully that application logic belonged on servers, not on PCs.<\/p>\n \u201cWe\u2019re so convinced that having the application and data on the server is better, even if you\u2019ve got a PC,\u201d he told Computerworld in 1997. \u201cWe believe there will be almost no demand for client\/server as soon as this comes out.\u201d<\/p>\n By 2000, he was even more forthright.<\/p>\n \u201cPeople are taking their apps off PCs and putting them on servers,\u201d ZDNET reported<\/a> Ellison as saying. \u201cThe only things left on PCs are Office and games.\u201d<\/p>\n In retrospect, Ellison\u2019s predictions were often early and sometimes overstated. Thin clients did not replace PCs, and expert systems did not transform enterprise software overnight. Yet the direction he described proved durable.<\/p>\n Application logic moved to servers, browsers became the dominant interface, and declarative tooling became a core design goal across the industry.<\/p>\n What the 1987 roundtable captures is the philosophical foundation of that shift. While others debated how much intelligence to add to applications, Ellison questioned where intelligence belonged at all.<\/p>\n He treated AI not as a destination, but as an implementation detail, valuable only when it reduced complexity or improved leverage.<\/p>\n As AI once again dominates enterprise strategy discussions, the caution embedded in Ellison\u2019s early comments feels newly relevant.<\/p>\n His core argument was not anti-AI, but anti-abstraction for its own sake. Intelligence mattered, but only when it served a larger architectural goal.<\/p>\n In 1987, that goal was making databases the center of application development. Decades later, the same instinct underpins modern cloud platforms. The technology has changed, but the tension Ellison identified remains unresolved: how much intelligence systems need, and how much complexity users are willing to tolerate to get it.<\/p>\n Follow TechRadar on Google News<\/a> and add us as a preferred source<\/a> to get our expert news, reviews, and opinion in your feeds. Make sure to click the Follow button!<\/p>\n And of course you can also follow TechRadar on TikTok<\/a> for news, reviews, unboxings in video form, and get regular updates from us on WhatsApp<\/a> too.<\/p>\n
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\n“The height of nonsense”<\/p>\n
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\nNot anti-AI<\/p>\n