#tech world things

Alright. Which one of you is my new coworker.

And I didn’t really get it then, but I sure get it now. Because it cannot be overstated how important and also seemingly overlooked domestic labor is when you’re talking about women’s productivity or “commitment” in literally any avenue outside the home.

I get asked all the time—especially by female friends—how I manage to have a successful full time career in tech AND regularly crank out published books/fic AND still go to the gym and have hobbies. People seem to think I have masterful time-budgeting wisdom.

But the honest answer is that I have no children and my husband takes care of nearly everything*. He makes dinner, handles lawn care and laundry, trash and recycling, changes air filters and cleans gutters. He makes his own appointments and sometimes mine. Plans our leisure travel. Books our flights. And we have a house cleaner.

My daily duties are taking the dog for his morning walk and making breakfast. My monthly duty is going to Costco. If there’s a home repair need that requires power tools, that’s my job, as well as any car-related maintenance stuff. That’s it.

I have very little mental or physical load surrounding domestic responsibility. AND I have my own dedicated home office where I can close the door and completely focus on my work or writing without interruption (I see you Virginia Woolf).

Which means I can do tech things from 8 until 4-5pm, go to the gym, and still have several hours of writing time before I have to go to bed. It means I can engage in time-consuming hobbies like flying or hiking or rock climbing on weekends. And it’s still hard! I still get burnt out because my brain is usually working all day without stop until 9 or 10pm! But if I had kids or was managing a household I would not physically be able to do the amount of work + writing that I do on an average day. It wouldn’t be possible.

So it’s not that I budget my time, it’s that I have more of it. Because I’m outsourcing a shit ton of labor that most of the women I’m talking to accept as their responsibility. It’s not a fair comparison.

Hi, I work in tech and I’m also an author and I feel we need to have a chat. Because I’m seeing a lot of misinformation/conflation happening.

There are many different kinds of AI, and disagreement about what should even be termed “AI” in the industry (it’s the hot new thing, so companies are rebranding all sorts of products they offer as “AI” which is muddying the waters re what is Artificial Intelligence and what’s just a sparkling workflow). That being said, let’s go over some key terminology/how these iterations differ (at least in my part of the data management AI world).

Narrow AI: This is a limited, passive, task-specific kind of AI. It’s programmed to respond within certain constraints. Narrow AI is behind the chat bot you talk to when you want to make an Amazon return, or voice assistants you speak to when trying to make an insurance claim. They don’t learn; if you ask them a question outside their purview, they will not provide an answer. Narrow AI can be useful for efficiency and saving costs (it can also be hella annoying if you’re stuck speaking to one that can’t help you with your problem and you don’t know the magic set of words to get to an actual person who can).

Agentic AI: This is a more advanced system that attempts to mimic human decision-making within a specific context using LLMs. This is a model that can “learn” and adapt. There are many subsets of Machine Learning within Agentic AI like Supervised learning—where a system can be trained to identify something by certain characteristics (used for cancer detection!) or Unsupervised Learning which is a matching/pattern recognition exercise (like identifying new disease subgroups!). Agentic AI can also help more generally with looking at patient data within the context of their medical history/the most up-to-date medical best practices, and provide insights. Aside from use in the medical industry, Agentic AI might be used within larger corporations for supply chain management—monitoring and automating interactions between suppliers, vendors, freight companies, etc. to make sure the correct number of products are ordered and shipped at the correct time to the correct locations, even if those numbers fluctuate. It can be used for companies who want to enable self-service for their business units to query data and create new data sets based on those queries using natural language (“show me all customers who purchased x product within this time frame in this geographic location,” “now create a new data set with this information”). Like Narrow AI, Agentic AI can improve efficiency, and is helpful in contexts when the breadth of data/moving parts involved is so substantial a human may not be equipped to manage it alone. However, it still needs to be implemented responsibly (more on that after Generative AI).

Predictive AI: This uses pattern recognition/machine learning/statistics/algorithms to predict outcomes. Predictive AI taps into an amount of data that was previously not possible for human teams to manage (much like Agentic AI). Predictive AI can anticipate stock market changes, extreme weather, mechanical issues, supply chain impacts, healthcare outcomes, crime surges, and more, saving time, money, and potentially even preventing major problems like vehicle recalls and death due to natural disasters. However, predictive AI is limited by the data it’s trained on, which has resulted in algorithmic biases (like when used in law enforcement/policing contexts, or healthcare contexts). So, as is true for any of the AI models I’ve mentioned so far, while it can be a positive tool, implementors should be cognizant of the fallible, human, foundation it’s built upon and try to mitigate bias.

Generative AI: This is what most people think of when they think of AI, thanks to chatbots like ChatGPT. Generative AI is a regurgitative leech. It creates “new” content based on the massive amounts of data it has been trained on. Nothing ChatGPT creates is actually new, though. It’s not thinking for itself. When you ask it to write a story or create a picture, it’s using an amalgamation of the writing and art it has copied from real creators without credit. There is very little useful about Generative AI like ChatGPT. Especially when you consider the environmental ramifications of using it. Generative AI used within a social context (as a therapist/friend/romantic stand-in) is dangerous. And if used as an authoritative search engine (which is disturbingly prevalent, now) it’s equally problematic due to common issues like hallucinations, the spread of fabricated news stories/outlets, dangerous deepfakes, extremist bias, and more. And if you’re thinking, well I just use it to help with outlining papers/re-writing emails/condensing notes, there are already studies that raise concerns about generative AI weakening critical thinking skills. I cannot tell you how relieved I am that I left my job as a professor the year before ChatGPT came out.

Now, to be fair, a lot of Agentic AI depends on LLMs, as I mentioned, which makes it prone to encountering the same sorts of issues with hallucinations/bias as Generative AI chatbots. But in my experience, companies/hospitals/government entities are aware of this and, when implementing responsibly, use Agentic AI as a tool that requires supervision and adjustment, not some holy, infallible, authority (like many public-facing chat bot users). I also think the potential benefits of Agentic AI currently outweigh my concerns about its use of generative AI (though the environmental impacts are still worrisome). So perhaps even further nuance is needed between Generative AI used within Agentic contexts and public-facing Generative AI used purely for entertainment/ “education” in chatbots.

Which only emphasizes my point that lumping all AI together is not beneficial. There’s nuance! Anyway.

TL;DR my personal thoughts on AI:

Narrow AI—Can be Good when implemented appropriately!

Agentic AI—Can be Good when implemented appropriately!

Predictive AI—Can be Good when implemented appropriately!

Oh boy. This will be a fast pass since I’m on my lunch break but here we go.

OpenAI’s CEO Sam Altman has explicitly said you should not use ChatGPT as a therapist/friend. If the CEO is telling you “don’t do this,” don’t do this. Source

The primary reason he cites is that there’s no legal privilege. No Dr/patient confidentiality. Altman even said that, in the event of a lawsuit or legal inquiry, Open AI would produce the entirety of people’s conversations. Every word. There is zero privacy (and that’s aside from the fact that your data is being actively mined).

Most chatbots are built to encourage engagement, prolong conversation (so you give them more content to mine), and be as agreeable as possible. This means they may inadvertently encourage someone who is delusional, reaffirm incorrect assumptions/statements that a human would call out, or even agree that a person should self-harm or kill themselves with no accountability. There are multiple cases now of people who have committed suicide or were hospitalized after interacting with chatbots (and at least one legal case now related to this). source, source, source, source

Chatbots are only as good as the LLMs they’re built upon. So it’s unsurprising that they may show stigma against certain kinds of substance and/or mental health issues and may fail to recognize suicidal ideation. Source

Finally, The American Psychological Association is saying don’t do it. All chatbots are not unilaterally harmful, and there are even some studies in which folks are actively trying to create therapy bots that do not have these pitfalls with positive initial results, but ChatGPT is not one of them. Source

Him: Oh no, I’m so sorry.

Me:

Him: I guess you’re still young—you have time!

Me: No, no. The whole no kids thing is intentional. I don’t want kids.

Him: You say that now, but you’ll change your mind!

Me:

(still trying to be upbeat and laughing a little) No, really. My husband and I have taken permanent preventative steps. No babies for us!

Him: Oh, that’s such a shame.

Me:

Someone else on the call, trying (poorly) to help: Well at least we don’t have to worry about you leaving the project for maternity leave!

Me:

A third man, urgently: WHAT KIND OF DOG DO YOU HAVE?

I let him finish.