Day: January 8, 2025

Dry January is an opportunity to evaluate your relationship to alcohol. The idea is to abstain from alcohol for the entire month, then decide if you want to go back to your regular drinking habits, drink less, or not at all for the remainder of the year. With the recent discussion in the U.S. of adding cancer warning labels to alcoholic beverages and increased interest in non-alcoholic alternatives to drinking, many Americans may be moved to reevaluate their existing drinking habits more closely this year.

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While many participate in Dry January for health reasons or as a reset after the boozy holidays, some might find it impacts their social interactions in unexpected ways—namely, it impacts how they date.

It might feel like a shock to the system at first. So much of dating is linked to drinking culture, from meeting for happy hour “getting to know you” cocktails to using alcohol as a social lubricant at parties and easing the nerves of a hookup. It can be jarring to not have alcohol as a go-to option. But for the 25% of Americans who participate in Dry January annually, it might be a chance to reset the drinking expectations for yourself and your dating life.

Most of us think of alcohol as a social activity that loosens inhibitions, making it an easy wingman for potentially awkward dates. Research has shown that alcohol has a “tendency to free individuals from preoccupation with social rejection, allowing them to access social rewards.” That can come in handy when navigating fear of rejection and taking risks like asking someone out or inviting them to stay the night. That can come in handy when navigating fear of rejection and taking risks like asking someone out.

Moderate drinking is also thought to create a more comfortable mood for sex. Think about any James Bond movie where a love interest is invited over for a tryst: They are almost always offered an alcoholic beverage as part of the romantic unfolding of events.

The truth is, we have woven drinking into so many social aspects of our lives that we often don’t even question why we do it. Sober dating can give you the chance to ask yourself if alcohol is actually helpful (or unhelpful) when fostering relationships.

Read More: 3 Dating New Year’s Resolutions You Can Actually Feel Good About

In fact, how do you know when you’re relying too heavily on alcohol to help numb or overcome some of the common feelings associated with dating? One way is to notice how hard it is for you to go about dating without using alcohol to smooth over the emotionally rougher parts.

It can be surprising what feelings surface when alcohol isn’t part of the dating equation. You might feel more anxious, insecure, or even scared because alcohol isn’t there to tamp down these feelings. All of this is normal and worth going through to see how you can manage these emotions in other ways, perhaps through meditation, grounding exercises, and self care.

When I work with clients who are either sober, or choosing to abstain from alcohol for a period of time, they tell me how difficult it is to navigate dating. They feel uneasy telling prospective dates that they don’t drink or suggesting a non-alcohol based date. We work together to strategize ways for them to move more freely throughout the dating process. Whether that’s stating up-front in their dating profile that they’re sober or sharing that early on with their dates. We also discuss what they really want from dating and how that might be different from what they were looking for when they drank alcohol. For instance, some clients have found that as a sober dater, they are more interested in connecting with people they date on a much deeper level, regardless of whether it’s casual or long-term. They’re also less comfortable with intense, brief encounters and notice that without alcohol, they need more time with someone to open up and feel like they’ve established trust.

Some of my sober clients fear that their sobriety will limit the people who will want to date them, and I always encourage them to think of this as a positive, not a negative. If you’re exploring sober dating and considering making sobriety (or even reducing your alcohol consumption) a long-term plan, you will want to date people who understand that choice no matter why you’re making it. It’s okay to screen for other sober people. In fact, I have seen my sober clients get along better with others who were either never that into drinking alcohol, are sober, or sober-curious, like those who participate in Dry January.

Taking stock of your drinking and how it impacts your love life isn’t easy, but it’s much easier these days due to Gen Z’s interest in the non-alcoholic (NA) lifestyle. Over the last few years, there has been an increase in demand for low ABV and NA drinks to the point where bars have had to expand their non-alcoholic offerings. This trend helps reduce the stigma associated with sobriety and limiting one’s alcohol consumption. It also means there are more options for non-drinkers at places that serve alcohol.

Dry January is the perfect time to look at how your dating life has been shaped by alcohol and decide whether or not you’d like that to change. As you explore how sober dating feels, consider the many ways you can either engage with alcohol differently in the future, or add in other ways to cope with the stress and anxiety of dating. It is human to want to limit the amount of awkwardness we feel in social situations, but so much can be gained by experiencing life’s imperfect moments with a clear head.

Whether you decide to change your drinking habits or not after the month is up, sober dating will allow you to experience how dating feels without the buffer of alcohol—and whether that cocktail is really the wingman we thought it to be.

OpenAI co-founder Ilya Sutskever made waves in November when he suggested that advancements in AI are slowing down, explaining that simply scaling up AI models was no longer delivering proportional performance gains.

Sutskever’s comments came on the heels of reports in The Information and Bloomberg that Google and Anthropic were also experiencing similar slowdowns. This led to a wave of articles declaring that AI progress has hit a wall, lending further credence to an increasingly widespread feeling that chatbot capabilities haven’t improved significantly since OpenAI released GPT-4 in March 2023.

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On Dec. 20, OpenAI announced o3, its latest model, and reported new state-of-the-art performance on a number of the most challenging technical benchmarks out there, in many cases improving on the previous high score by double-digit percentage points. I believe that o3 signals that we are in a new paradigm of AI progress. And François Chollet a co-creator of the prominent ARC-AGI benchmark, who some consider to be an AI scaling skeptic, writes that the model represents a “genuine breakthrough.”

However, in the weeks after OpenAI announced o3, many mainstream news sites made no mention of the new model. Around the time of the announcement, readers would find headlines at the Wall Street Journal, WIRED, and the New York Times suggesting AI was actually slowing down. The muted media response suggests that there is a growing gulf between what AI insiders are seeing and what the public is told.

Indeed, AI progress hasn’t stalled—it’s just become invisible to most people.

Automating behind-the-scenes research

First, AI models are getting better at answering complex questions. For example, in June 2023, the best AI model barely scored better than chance on the hardest set of “Google-proof” PhD-level science questions. In September, OpenAI’s o1 model became the first AI system to surpass the scores of human domain experts. And in December, OpenAI’s o3 model improved on those scores by another 10%. 

However, the vast majority of people won’t notice this kind of improvement because they aren’t doing graduate-level science work. But it will be a huge deal if AI starts meaningfully accelerating research and development in scientific fields, and there is some evidence that such an acceleration is already happening. A groundbreaking paper by Aidan Toner-Rodgers at MIT recently found that material scientists assisted by AI systems “discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation.” Still, 82% of scientists report that the AI tools reduced their job satisfaction, mainly citing “skill underutilization and reduced creativity.”

But the Holy Grail for AI companies is a system that can automate AI research itself, theoretically enabling an explosion in capabilities that drives progress across every other domain. The recent improvements made on this front may be even more dramatic than those made on hard sciences. 

In an attempt to provide more realistic tests of AI programming capabilities, researchers developed SWE-Bench, a benchmark that evaluates how well AI agents can fix actual open problems in popular open-source software. The top score on the verified benchmark a year ago was 4.4%. The top score today is closer to 72%, achieved by OpenAI’s o3 model.

This remarkable improvement—from struggling with even the simplest fixes to successfully handling nearly three-quarters of the set of real-world coding tasks—suggests AI systems are rapidly gaining the ability to understand and modify complex software projects. This marks a crucial step toward automating significant portions of software research and development. And this process appears to be well underway. Google’s CEO recently told investors that “more than a quarter of all new code at Google is generated by AI.”

Much of this progress has been driven by improvements to the “scaffolding” built around AI models like GPT-4o, which increase their autonomy and ability to interact with the world. Even without further improvements to base models, better scaffolding can make AI significantly more capable and agentic: a word researchers use to describe an AI model that can act autonomously, make decisions, and adapt to changing circumstances. AI agents are often given the ability to use tools and take multi-step actions on a user’s behalf. Transforming passive chatbots into agents has only become a core focus of the industry in the last year, and progress has been swift. 

Perhaps the best head-to-head matchup of elite engineers and AI agents was published in November by METR, a leading AI evaluations group. The researchers created novel, realistic, challenging, and unconventional machine learning tasks to compare human experts and AI agents. While the AI agents beat human experts at two hours of equivalent work, the median engineer won at longer time scales.

But even at eight hours, the best AI agents still managed to beat well over one-third of the human experts. The METR researchers emphasized that there was a “relatively limited effort to set up AI agents to succeed at the tasks, and we strongly expect better elicitation to result in much better performance on these tasks.” They also highlighted how much cheaper the AI agents were than their human counterparts.

The problem with invisible innovation

The hidden improvements in AI over the last year may not represent as big a leap in overall performance as the jump between GPT-3.5 and GPT-4. And it is possible we don’t see a jump that big ever again. But the narrative that there hasn’t been much progress since then is undermined by significant under-the-radar advancements. And this invisible progress could leave us dangerously unprepared for what is to come. 

The big risk is that policymakers and the public tune out this progress because they can’t see the improvements first-hand. Everyday users will still encounter frequent hallucinations and basic reasoning failures, which also get triumphantly amplified by AI skeptics. These obvious errors make it easy to dismiss AI’s rapid advancement in more specialized domains. 

There’s a common view in the AI world, shared by both proponents and opponents of regulation, that the U.S. federal government won’t mandate guardrails on the technology unless there’s a major galvanizing incident. Such an incident, often called a “warning shot,” could be innocuous, like a credible demonstration of dangerous AI capabilities that doesn’t harm anyone. But it could also take the form of a major disaster caused or enabled by an AI system, or a society upended by devastating labor automation. 

The worst-case scenario is that AI systems become scary powerful but no warning shots are fired (or heeded) before a system permanently escapes human control and acts decisively against us.

Last month, Apollo Research, an evaluations group that works with top AI companies, published evidence that, under the right conditions, the most capable AI models were able to scheme against their developers and users. When given instructions to strongly follow a goal, the systems sometimes attempted to subvert oversight, fake alignment, and hide their true capabilities. In rare cases, systems engaged in deceptive behavior without nudging from the evaluators. When the researchers inspected the models’ reasoning, they found that the chatbots knew what they were doing, using language like “sabotage, lying, manipulation.”

This is not to say that these models are imminently about to conspire against humanity. But there has been a disturbing trend: as AI models get smarter, they get better at following instructions and understanding the intent behind their guidelines, but they also get better at deception. Smarter models may also be more likely to engage in dangerous behavior. For instance one of the world’s most capable models, OpenAI’s o1, was far more likely to double down on a lie after being caught by the Apollo evaluators. 

I fear that the gap between AI’s public face and its true capabilities is widening. While consumers see chatbots that still can’t count the letters in “strawberry,” researchers are documenting systems that can match PhD-level expertise and engage in sophisticated deception. This growing disconnect makes it harder for the public and policymakers to gauge AI’s real progress—progress they’ll need to understand to govern it appropriately. The risk isn’t that AI development has stalled; it’s that we’re losing our ability to track where it’s headed.