We Just Discovered the Sounds of Spacetime. Let's Keep Listening
Initially, scientists expected LIGO might detect just a few of these collisions. But now, nearing the first detection's 10th anniversary, we have already observed more than 300 gravitational-wave events, uncovering entirely unexpected populations of black holes. Just lately, on July 14, LIGO scientists announced the discovery of the most massive merger of two black holes ever seen.
Gravitational-wave astronomy has become a global enterprise. Spearheaded by LIGO's two cutting-edge detectors in the U.S. and strengthened through collaboration with detectors in Italy (Virgo) and Japan (KAGRA), the field has become one of the most data-rich and exciting frontiers in astrophysics. It tests fundamental aspects of general relativity, measures the expansion of the universe and challenges our models of how stars live and die.
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LIGO has also spurred the design and development of technologies beyond astronomy. For example advances in quantum technologies, which reduce the noise and thereby improve LIGO's detector sensitivity, have promising applications to both microelectronics and quantum computing.
Given all this, it comes as no surprise that the Nobel Prize in Physics was awarded to LIGO's founders in 2017.
Yet despite this extraordinary success story, the field now faces an existential threat. The Trump administration has proposed slashing the total National Science Foundation (NSF) budget by more than half: a move so severe that one of the two LIGO detectors would be forced to shut down. Constructing and upgrading the two LIGO detectors required a public investment of approximately $1.4 billion as of 2022, so abandoning half this project now would constitute a gigantic waste. A U.S. Senate committee in mid-July pushed back against hobbling LIGO, but Congress has lately folded against administration budget cut demands, leaving it still on the table.
The proposed $19 million cut to the LIGO operations budget (a reduction from 2024 of some 40 percent) would be an act of stunning shortsightedness. With only one LIGO detector running, we will detect just 10 to 20 percent of the events we would have seen with both detectors operating. As a result, the U.S. will rapidly lose its leadership position in one of the most groundbreaking areas of modern science. Gravitational-wave astronomy, apart from being a technical success, is a fundamental shift in how we observe the universe. Walking away now would be like inventing the microscope, then tossing it aside before we had a good chance to look through the lens.
Here's why losing one detector has such a devastating impact: The number of gravitational-wave events we expect to detect depends on how far our detectors can 'see.' Currently, they can spot a binary black hole merger (like the one detected in 2015) out to a distance of seven billion light-years! With just one of the two LIGO detectors operating, the volume we can probe is reduced to just 35 percent of its original size, slashing the expected detection rate by the same fraction.
Moreover, distinguishing real gravitational-wave signals from noise is extremely challenging. Only when the same signal is observed in multiple detectors can we confidently identify it as a true gravitational-wave event, rather than, say, the vibrations of a passing truck. As a result, with just one detector operating, we can confirm only the most vanilla, unambiguous signals. This means we will miss extraordinary events like the one announced in mid-July.
Accounting for both the reduced detection volume and the fact that we can only confirm the vanilla events, we get to the dreaded 10 to 20 percent of the expected gravitational wave detections.
Lastly, we will also lose the ability to follow up on gravitational-wave events with traditional telescopes. Multiple detectors are necessary to triangulate an event's position in the sky. This triangulation was essential for the follow-up of the first detection of a binary neutron star merger. By pinpointing the merger's location in the sky, telescopes around the world could be called into action to capture an image of the explosion that accompanied the gravitational waves. This led to a cascade of new discoveries, including the realization in 2017 that such mergers comprise one of the main sources of gold in the universe.
Beyond LIGO, the proposed budget also terminates U.S. support for the European-led space-based gravitational-wave mission LISA and all but guarantees the cancellation of the next-generation gravitational wave detector Cosmic Explorer. The U.S. is thus poised to lose its global leadership position. As Europe and China move forward with ambitious projects like the Einstein Telescope, LISA and TianQin, this could result not only in missing the next wave of breakthroughs but also in a significant brain drain.
We cannot predict what discoveries still lie ahead. After all, when Heinrich Hertz first confirmed the existence of radio waves in 1887, no one could have imagined they would one day carry the Internet signal you used to load this article. This underscores a vital point: while cuts to science may appear to have only minor effects in the short term, systematic defunding of the fundamental sciences undermines the foundation of innovation and discovery that has long driven progress in the modern world and fueled our economies.
The detection of gravitational waves is a breakthrough on par with the first detections of x-rays or radio waves, but even more profound. Unlike those forms of light, which are part of the electromagnetic spectrum, gravitational waves arise from an entirely different force of nature. In a way, we have unlocked a new sense for observing the cosmos. It is as if before, we could only see the universe. With gravitational waves, we can hear all the sounds that come with it.
Choosing to stop listening now would be foolish.
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We don't want our AI models blackmailing us — and this experiment shows that Claude is capable of such unethical behaviors when its 'survival' is threatened. Anthropic says it's 'unclear how much of this behavior was caused by an inherent desire for self-preservation.' If Claude has such an inherent desire, that raises worries about what it might do. But does that mean we should all be terrified that our chatbots are about to blackmail us? No. To understand why, we need to understand the difference between an AI's capabilities and its propensities. Why claims of 'scheming' AI may be exaggerated As Summerfield and his co-authors note, there's a big difference between saying that an AI model has the capability to scheme and saying that it has a propensity to scheme. A capability means it's technically possible, but not necessarily something you need to spend lots of time worrying about, because scheming would only arise under certain extreme conditions. But a propensity suggests that there's something inherent to the AI that makes it likely to start scheming of its own accord — which, if true, really should keep you up at night. The trouble is that research has often failed to distinguish between capability and propensity. In the case of AI models' blackmailing behavior, the authors note that 'it tells us relatively little about their propensity to do so, or the expected prevalence of this type of activity in the real world, because we do not know whether the same behavior would have occurred in a less contrived scenario.' In other words, if you put an AI in a cartoon-villain scenario and it responds in a cartoon-villain way, that doesn't tell you how likely it is that the AI will behave harmfully in a non-cartoonish situation. In fact, trying to extrapolate what the AI is really like by watching how it behaves in highly artificial scenarios is kind of like extrapolating that Ralph Fiennes, the actor who plays Voldemort in the Harry Potter movies, is an evil person in real life because he plays an evil character onscreen. We would never make that mistake, yet many of us forget that AI systems are very much like actors playing characters in a movie. They're usually playing the role of 'helpful assistant' for us, but they can also be nudged into the role of malicious schemer. Of course, it matters if humans can nudge an AI to act badly, and we should pay attention to that in AI safety planning. But our challenge is to not confuse the character's malicious activity (like blackmail) for the propensity of the model itself. If you really wanted to get at a model's propensity, Summerfield and his co-authors suggest, you'd have to quantify a few things. How often does the model behave maliciously when in an uninstructed state? How often does it behave maliciously when it's instructed to? And how often does it refuse to be malicious even when it's instructed to? You'd also need to establish a baseline estimate of how often malicious behaviors should be expected by chance — not just cherry-pick anecdotes like the ape researchers did. Why have AI researchers largely not done this yet? One of the things that might be contributing to the problem is the tendency to use mentalistic language — like 'the AI thinks this' or 'the AI wants that' — which implies that the systems have beliefs and preferences just like humans do. Now, it may be that an AI really does have something like an underlying personality, including a somewhat stable set of preferences, based on how it was trained. For example, when you let two copies of Claude talk to each other about any topic, they'll often end up talking about the wonders of consciousness — a phenomenon that's been dubbed the 'spiritual bliss attractor state.' In such cases, it may be warranted to say something like, 'Claude likes talking about spiritual themes.' But researchers often unconsciously overextend this mentalistic language, using it in cases where they're talking not about the actor but about the character being played. That slippage can lead them — and us — to think an AI is maliciously scheming, when it's really just playing a role we've set for it. It can trick us into forgetting our own agency in the matter. The other lesson we should draw from chimps A key message of the 'Lessons from a Chimp' paper is that we should be humble about what we can really know about our AI systems. We're not completely in the dark. We can look what an AI says in its chain of thought — the little summary it provides of what it's doing at each stage in its reasoning — which gives us some useful insight (though not total transparency) into what's going on under the hood. And we can run experiments that will help us understand the AI's capabilities and — if we adopt more rigorous methods — its propensities. But we should always be on our guard against the tendency to overattribute human-like traits to systems that are different from us in fundamental ways. What 'Lessons from a Chimp' does not point out, however, is that that carefulness should cut both ways. Paradoxically, even as we humans have a documented tendency to overattribute human-like traits, we also have a long history of underattributing them to non-human animals. The chimp research of the '60s and '70s was trying to correct for the prior generations' tendency to dismiss any chance of advanced cognition in animals. Yes, the ape researchers overcorrected. But the right lesson to draw from their research program is not that apes are dumb; it's that their intelligence is really pretty impressive — it's just different from ours. Because instead of being adapted to and suited for the life of a human being, it's adapted to and suited for the life of a chimp. Similarly, while we don't want to attribute human-like traits to AI where it's not warranted, we also don't want to underattribute them where it is. State-of-the-art AI models have 'jagged intelligence,' meaning they can achieve extremely impressive feats on some tasks (like complex math problems) while simultaneously flubbing some tasks that we would consider incredibly easy. Instead of assuming that there's a one-to-one match between the way human cognition shows up and the way AI's cognition shows up, we need to evaluate each on its own terms. Appreciating AI for what it is and isn't will give us the most accurate sense of when it really does pose risks that should worry us — and when we're just unconsciously aping the excesses of the last century's ape researchers.
