Open AI Looks More Overhyped By The Day
Is Sam Altman a tech visionary or just a front man for an overhyped company called OpenAI? Evidence increasingly points to the latter. One of the biggest clues is the resort by OpenAI and other artificial intelligence (AI) companies to thinly disguised forms of vendor finance. This happens when the seller of a product lends money to the buyer to buy the product. That’s not real demand. It’s just leverage for buyers to prop up sellers and vice versa. This is now rampant in the AI sector.
Open AI has agreed to buy chips from AMD and NVIDIA. But OpenAI is getting AMD warrants and NVIDIA stock in the deals. OpenAI also has a data center deal with CoreWeave, which is a major customer of NVIDIA. And OpenAI is a major partner with Oracle, which is a strategic partner with NVIDIA. None of this is illegal, but it all appears to be a case of major computer companies propping each other up while demand for the final products languishes. OpenAI’s deals of this kind total $1 trillion, but no one is sure how OpenAI will fund them. The difficulty today is that all of these advances and the AI boom in general have been extrapolated beyond the ability of the technology to perform.
Talk of superintelligence or advanced general intelligence under which humans would be to computers what apes are to humans in terms of cognitive skills is nonsense. Computers may get faster, and robots more common, but we won’t see true superintelligence perhaps ever. The reason has to do with the difference between inductive and deductive reasoning on the one hand, which computers can do within limits, and abductive logic and semiotics, which are important human skills that computers cannot do at all. These skills are non-programmable and mark one of the key distinctions between human brain functions and computer processing. Other constraints involve functions of the law of diminishing marginal returns under which massive increases in energy inputs and processing power result in only minor increases in output.
Major tech companies (Microsoft, Meta, Google, OpenAI, Apple, Oracle and a few others) have spent over $400 billion on data centers and other AI infrastructure in the past year with higher expenditures planned. This can be considered money spent on hardware. Software development costs and costs of information input are additional expenditures. Increased processing capacity has not been met with increased output. Profits remain elusive. In fact, new applications such as GPT-5 from OpenAI have been major disappointments. This phenomena of diminishing returns is well-known to engineers in other fields but may come as a shock to AI investors driven by FOMO (Fear of Missing Out).
Another constraint that is little understood is the Law of Conservation of Information in Search Processes. This law has been rigorously demonstrated mathematically by my collaborator William A. Dembski in a recently published paper. The law posits that any search process, including the most sophisticated version of AI with the fastest processors and LLMs, cannot find new information. They can only find existing information. AI may produce faster and more extensive searches and may find correlations that human efforts could not identify in a lifetime, but that’s all still existing information.
In short, AI has no creative capacity. It cannot “think” of anything new, unlike humans who create new formulas and works of art routinely. AI is not “intelligent” or creative. It’s just fast.
Its still more intelligent than a civil servant no matter what.
So is pond life.