CBS host frets Trump admin plans for moon base could usher in new 'age of colonialism' in space
On "CBS Mornings Plus" on Wednesday, Duthiers and co-host Adriana Diaz discussed the White House calling for more human space exploration and administration plans to build a nuclear reactor on the moon to precede an eventual U.S. lunar base with astrophysicist Neil deGrasse Tyson.
Diaz asked if it was "inevitable that we're going to have to go to the moon and try to colonize the Moon?"
Tyson said that the United States is being "reactive" in a race to the moon with China, and stated, to the laughter of the panel, that he does not want to "live on the moon."
But Duthiers questioned if colonizing the moon was a good idea.
"We know how the age of colonialism worked on this planet," the host said. "Should we be trying to colonize and saying that there's a keep-out zone that no other countries can participate in having?"
Tyson replied by pointing out that it would be difficult to colonize an area that does not have people.
"Well, the — the real problem with the colonization history in Western civilization is that there were people already there," Tyson said.
Duthiers and Diaz agreed, and Tyson added that "there are no moon beings that were displaced as far as we know."
Tyson later criticized administration plans to decrease funding to NASA.
"What's not on brand is to cut science programs, not only in NASA, but across the board, and then say, we want to excel in this one spot," Tyson said.
"Well, in the 1960s, science was a major investment profile of the United States," he continued. "And by the way, it's not on brand even for Republicans, because Republican administrations since the Second World War have had a higher annual increase, average annual increase, in the science budget than even the Democrats."
"So Trump's decision to cut science is not on brand for even being a Republican," Tyson added.
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Again, it isn't pinnacle AI by any stretch of imagination. Also, if you were eagerly expecting that this might be a harbinger of the next step, artificial superintelligence (ASI), sorry, but that's a no-go. There isn't anything of an evidentiary nature that proves this to be the case. ASI remains elusive, beyond our reach, and maybe, someday, we will consider GPT-5 to be a part of the historical path there. Or we will realize that GPT-5 was merely one of many AIs that happened to be devised and released from time and time but had little to do with attaining actual ASI. Let's talk further about GPT-5, including what it is and what it is not. This analysis of AI breakthroughs is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here). Official Launch Of GPT-5 In addition to a live video-streamed presentation with Sam Altman and his team touting GPT-5's release, there are various technical docs posted at the OpenAI website describing GPT-5. I'd suggest that any AI developer or similar technical specialist would benefit from exploring the GPT-5 System Card, a semi-technical depiction of the overall nuts-and-bolts of GPT-5. One key aspect is that GPT-5 is essentially a wraparound of several new GPT-5 submodels that are reflective of prior versions of OpenAI's line of products. Allow me to explain this since it is a crucial point. You might know that there has been an organic expansion of OpenAI's prior models in the sense that there have been GPT-4o, GPT-4o-mini, OpenAI o3, OpenAI o4-mini, GPT-4.1.-nano, and so on. When you wanted to use OpenAI's AI capabilities, especially from an AI developer's or AI devotee's perspective, you had to select which of those available models you wanted to utilize. It all depended on what you were looking to do. Some were faster, some were slower. Some were deeper at certain classes of problems, others were shallower. It was a smorgasbord that required you to pick the right one as suitable for your task at hand. The onus was on you to know which of the models were particularly applicable to whatever you were trying to do. It could be a veritable hit-and-miss process of selection and tryouts. GPT-5 now has uplifted those prior versions into new GPT-5 submodels, and the overarching GPT-5 model makes the choice of which GPT-5 submodel might be best for whatever problem or question you happen to ask. I'll comment on this umbrella or wraparound approach in a moment, so hang in there. According to the official OpenAI blog posting of August 7, 2025, entitled 'Introducing GPT-5,' here is what GPT-5 is principally about (excerpts): Upside And Downside Of Wraparound Consider the heralded wraparound aspect and what it foretells. Let's use an analogy to provide insight. When you drive a car, most people prefer to have an automatic transmission that selects which gear the car should be in. It used to be that having a manual transmission was the norm. Gradually, automatic transmissions became the new norm. You could liken the GPT-5 model to an automatic transmission that determines which GPT-5 submodel (a gear, per my analogy) is the right one to use. I would wager that everyday users of GPT-5 will be generally satisfied with that approach. The thing is, just as an automatic transmission might sometimes make a choice that isn't the best selection, the same can happen with the GPT-5 model choosing a GPT-5 submodel. The issue at hand is that if a GPT-5 submodel is selected and run in this automatic transmission mode, but it turns out not to be the best choice, it is going to run anyway. If you are paying for your AI usage, you'll have to pay for the run-time that might not produce as solid an answer as you hoped for. Furthermore, you might not even realize that you were inadvertently shortchanged by the fact that a less-than-best choice was made of which submodel to run. The odds are that you'll just assume that the answer produced was the best possible achievement. Meanwhile, if you perchance tried one of the GPT-5 submodels directly that you thought might do a better job, similar to selecting a specific gear when your car is going up a hill or on a straightaway, a better result might have arisen. It will be interesting to see how the auto-switching feature of GPT-5 progresses over time. The upside right now is that you no longer need to make the arduous or agonizing choice of which submodel to use. The downside is that the auto-switcher could make a suboptimal choice on your behalf. So-Called Thinking Time Aspects Another vital aspect of using generative AI and LLMs is trying to decide how much run-time you want the AI to use when doing its processing. I've previously discussed that this is cringingly referred to as 'thinking time' by much of the AI industry. It is cringey because the word 'thinking' implies human thoughts and mental processing. That's an unfortunate and illegitimate form of anthropomorphizing AI. All that is happening is that you are allowing more computational processing time to occur. See my coverage at the link here. I don't equate that to the vaunted nature of 'thinking,' but it's what has become a popular way to express the matter. Sad face. I had all along said that asking users to decide how much run-time ought to occur is a tough consideration since we usually have no semblance of what amount of time is going to be suitable. It is often a purely wild guess. 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