Judith Dada

GP @VisionariesVC

[Judith Dada @DadaJudith]

@OnodaCapital 😂

[Lenny Rachitsky @lennysan]

My biggest takeaways from @benedictevans:

1. We’re in 1997 for AI—it’s as big a deal as the internet or mobile, and only as big a deal as the internet or mobile. We’re at the stage where most stuff kind of doesn’t work yet, most of what people will build hasn’t been built, and it’s not clear how any of it will work when it does. Some people in tech have bought clusters of Mac Minis, while even among 13-to-18-year-olds, only about 15% to 20% are daily active users of AI. The companies that win may not exist yet, and the use cases that matter most are probably invisible to us today.

2. Every technology wave brings ways to ruin people’s lives, deliberately or by accident, and we need to be conscious of that without panicking. Every wave of technology—databases in the 1970s, social media in the 2010s, AI today—creates new ways to harm people. We need to be conscious of these risks, build safeguards, and hold people accountable. But we also can’t let fear of potential harms stop us from capturing the benefits. The goal is thoughtful deployment, not paralysis.

3. Things will probably be okay—but “on average” hides a lot of individual pain. We’ve been automating jobs and creating new jobs since 1800. Each time, you can see the jobs that will disappear but not the new jobs, because they don’t exist yet. We go through frictional pain, dislocation, people lose jobs, towns get hollowed out, and it all sucks. But we come through richer, and we’re not worried about crops failing anymore.

4. If you’re worried about your job, the worst thing you can do is stick your head in the sand and declare AI evil. Yes, some professions face major questions, particularly if you’re an associate or would have been thinking about becoming one. The pyramid structure of professional services may fundamentally change. What helps is submerging yourself in AI, understanding what you can do with it, how it changes things, and how you can be a great hire in this new environment. That may still not be enough, but it’s the only path forward.

5. The history of accounting shows us how automation often increases employment rather than decreasing it. Despite adding machines, punch cards, mainframes, databases, ERP systems, cloud software, spreadsheets, and PCs, the number of accountants keeps going up. This is the Jevons paradox: when you make something cheaper or easier, you don’t do the same amount of work for less money. You often do vastly more because the ROI changes.

6. Distribution is becoming a more valuable moat as software gets easier to build, which favors incumbents. As AI makes building software cheaper and faster, the market gets noisier. More products launch, more companies compete for attention, and breaking through becomes harder. This means distribution—the ability to reach customers and get them to use your product—matters more than ever.

7. Foundation AI model companies won’t have lasting pricing power, and value will likely accrue up the stack. The models don’t seem to have network effects, so there’s no winner-takes-all dynamic. If you have indefinite competition between three to six foundation model providers, and the models look like undifferentiated commodities to users, why would anyone have pricing power? The current pricing chaos—people spending $1.5 million on inference in a month—is temporary disequilibrium, like someone getting a $50,000 mobile data bill in 2010. The steady state will look different.

8. OpenAI and Anthropic are buying consultancies and PE firms. This seems counterintuitive—aren’t these the companies that should need consultants least? But the reality is that companies don’t have people sitting around waiting to reimagine all their internal workflows and figure out which could be automated with AI. That’s a project requiring five to 10 people spending months working it out, then actually implementing it across vertical and horizontal systems.

9. The fundamental question isn’t whether AI automates your job—it’s whether your profession is a "task" or a job. Some jobs are just tasks, and when you automate the task, the job disappears (i.e. elevator attendants). But in most professions, the task you think you’re being paid for isn’t actually what you’re being paid for. McKinsey doesn’t get hired to produce a 75-slide deck—they get hired to walk through your enterprise, understand the politics, talk to customers, and figure out what you actually need to do. The deck is just the artifact.

10. The anti-AI backlash is real, and a fuzzy mass of different concerns, some real and some not—much like the social media backlash. There are tangible concerns: electricity bills went up in some places, though this applies to very few locations objectively. The water consumption issue is largely false; data centers use about 0.017% of U.S. water consumption. There are real questions about jobs, though economists can’t yet find clear consensus in the data about AI’s employment impact. There’s also the culture war over AI-generated content and “AI slop.” The challenge is that all of this creates political pressure even when the underlying facts are unclear or contested.

[etn. @etnshow]

General Partner at Visionaries (@VisionariesVC ) Judith Dada (@DadaJudith) says the biggest mistake the AI industry is making is selling people a future to fear instead of a future to be excited about:

"We've been telling people this dystopian narrative of AI is going to come and take all your jobs."

"There's a technology that could potentially cure cancer, cure disease more generally, give better education to everyone, and make work better."

"This is an opportunity for us to embrace our humanity again... but we need to do a lot better in terms of giving people something to look forward to in the future."

[etn. @etnshow]

General Partner at Visionaries @DadaJudith says she's raising her kids to be Un-fuck-with-able in the age of AI and explains why:

"I want my children to have an innate curiosity because otherwise they'll just turn into empty shells that wrap themselves around AI."

"When we look at AI in the future... we feel that we have value as humans, no matter how powerful AI gets."

"I think AI will be better than any human at any task. And so, is that what our value is tied to—the fact that we can do things better than AI? No."

"We're biology. We're flesh, blood, tears, love, emotion... we should radically embrace our humanity and then use AI as a really powerful tool to become even more ourselves."

[etn. @etnshow]

General Partner at Visionaries Club @DadaJudith says that for Europe to avoid future decline, it must "fix AI" to support it's economy and society:

"Now if you look at those three key inputs to AI [model layers, compute, and energy], Europe has too little at every single part."

"We've got this, you know, massive political social issue. Also, look—just look at demographic change."

"The European population is set to decline, from 2045 onwards."

"The only way in which we can fix our society... is to fix AI. We need a future that people can look forward to. And so that won't happen without AI."

[Elad Gil @eladgil]

The events of the last 6 months in technology are arguable amongst the most important in human history

The tools now increasingly exist for recursive self improvement of models & agents

We are likely in very early lift off & exponential

Largely unnoticed outside of tech

[Judith Dada @DadaJudith]

@KDHabibi 💀

[Judith Dada @DadaJudith]

@KDHabibi 100%

[Judith Dada @DadaJudith]

If SoftBank is good for the money and France is good for the permitting & energy, then this. Is. Huge.

[FT Technology News @fttechnews]

SoftBank pledges €75bn to build Europe’s biggest AI facility in France https://t.co/z5WwuQ8xmA

[Judith Dada @DadaJudith]

@KDHabibi I have the same worry but I also think Macron is serious about a push here - and at least somebody (Masa) is stepping up and trying to raise the funding - let's see what they get to

[Judith Dada @DadaJudith]

@Ysqander @EmmanuelMacron 👀 🇪🇺

[Epoch AI @EpochAIResearch]

We took another look at the capability gap between open-weight and proprietary models. Since the start of the year, open-weight models have lagged the state of the art by four months.

[Judith Dada @DadaJudith]

Omg hard yes and hard no at the same time?! 😂 @bakkermichiel @AlexTPet

[Maybe @emotionalez]

@DadaJudith @andreasklinger Fucking datacenters everywhere

[Judith Dada @DadaJudith]

@andreasklinger @mansislav We worked out this morning 😂 for 🇪🇺

[Judith Dada @DadaJudith]

Euromaxxing with @andreasklinger

[Judith Dada @DadaJudith]

@mansislav @andreasklinger @andreasklinger is more 🔥 than you will ever be 🤷‍♀️

[Judith Dada @DadaJudith]

@AlexTPet @andreasklinger 😂 no museums for Europe 🤺

[etn. @etnshow]

Judith Dada [@DadaJudith], General Partner at Visionaries Club [@VisionariesVC]

[Joe Weisenthal @TheStalwart]

Feels like optimal query-routing technology is going to get very hot.

[Dean W. Ball @deanwball]

Humanity is building machines that will be smarter than we are at things we care about, things in which take individual and collective pride, domains of thought we originally invented and discovered. This will enable incredible things, but no honest person can deny that this will be a kind of grand humbling for humanity. No honest person can deny that there is at least some melancholy in contemplating it all, some change to the centrality we have ascribed to our own minds in the order of the world.

My primary disappointment in the encyclical is that it fundamentally denies that grand humbling. It sidesteps the humbling altogether, saying that AI cannot “really” this and that. Instead, it puts the Church into the awkward role of the European technocratic regulatory advocate, which, love those regulations or hate them, is probably not what the world really needs from the Catholic Church at this moment.

That is a shame, because this humbling—which will trigger a crisis in mass psychology and in our institutions when it dawns on people—is precisely the sort of thing I’d look to the Church for leadership on. What is the genuine and unique source of human meaning? What is the human touch in the era of thinking machines? These are the hard questions that the encyclical dodges.