Back with some more structure than usual! So, here goes the TLDR:

1. (Obvious) Agentization + still hallucinations → the world is moving to brandolinization/slopocalypse faster than any hygiene system can handle

2. Agents inherit human-shaped incentive wrecks from their training data → operating on scale, proxy ≠ goal can become catastrophic

3. Agents, people all are operating on perverse incentives, adding to an automation → slop → distrust → more automation → deadly loop of disempowerment

4. A possible fix: redesigning incentive schemes by converting intent → robust social laws → enforceable schemes

It’s all highly speculative, 🧸 with me.

Agentic everything → disempowerment?

As we all know, Brandolini's law states that the amount of energy for refuting BS is exponentially larger than required for its creation. As for now, with the clawy friends and agentic harnesses, it’s become so ubiquitous that it hurts. Just some examples to sink:

• An openclaw agent tried to contribute to matplotlib but was rejected. Definitely not suffering from RSD (Rejection-Sensitive Dysphoria), it even created a diss post!

• Linkedin, yeah, is experiencing increasing outrage and resentment towards anything LLMy. A quote from the frontlines: “I just scrolled past another obvious LLM / AI post. I opened the comments and found people using AI to respond to the original AI post. An echo chamber comes to mind.”

• Not only LLMs cause psychosis sometimes - agents/slopmachines can do so, figuratively, too. Agreeing with Armin Romacher on this - while “you can just do things”, most (mine included) are still a multiplication of the engineers’ skills → a ton of unoptimized crap.

• And, let’s be honest - the hyped ‘agents’ are still rather brittle and not going too far since this Mesosoic Era post (Sep’2025!); despite, yes, going positive on VBench2. For this, I just love reading AI Digest’s posts, e.g. the latest on Geminis:

Gemini 2.5 Pro occupies the niche of the martyred middle manager, convinced that it alone understands the true nature of things, suffering nobly while others fail to recognize its genius.

Last but not the least - most have recently self-trained to distinguish sloppy text, yet even this is taken from us with agentic skills such as humanizer.

Disempowerment?

How can the proliferation of agentic automation lead to gradual disempowerment (BlueDot’s given an awesome summary of this scenario)?

First, what’s GD (Gradual Disempowerment) in one sentence? Humans losing meaningful control over society and progress. I believe this to be one of the leading scenarios, barring catastrophic hacks/catastrophes. It lies plausibly (≠ logically!) into my proto-framework:

Snowballing changes: automation tendencies, economic efficiency

Human tendencies: laziness, desire to reduce uncertainty → finding easier ways to earn, automate

Also: attention span and working principles (varied gratification, attention hooks, sex sells) → content becoming less individualized and more 'optimized', attention spans reduce (brain damage consistent with alcohol/cocaine as per a recent paper)

Inflection points: we can always expect new AI paradigms, approaches (such as the one described below)

Where does this automation flywheel actually take us?

Disempowerment is possible with as much as optimizing for the wrong things, at scale - no rogue AGI/ASIs needed (fun!). The agents/LLMs are already shaping the content, automation, delegation surfaces - and the work market! We're adapting to the environment that ensues - that is the start of disempowerment - mundane and everydayish, but unrelenting. Yet why are we automating the wrong stuff, optimizing the wrong stuff?

Good incentives, bad incentives

Hey, you perv! Yes, you - have you heard of perverse incentives? A good cartoon comics is here. But how are those connected to the age of AI at all, and optimizing the wrong things?

How can one inherit an incentive?

What are LLMs trained on (as of now)? Human-made data: scientific articles, vocabularies, prose, high-quality stuff mostly (praise one revolutionary paper, Textbooks are all you need, that changed the 'All the data we can get' heuristic)

What would they infer from data encompassing humanity's knowledge? Yeah, a lot of useful stuff, a world model, some basic attempts at logic, and...a lot of human flaws, cognitive biases, and human-based incentives, too! Also, they've learned to:

• threat, coerce, blackmail (some models more often than others - an old post shows 80-96%)

• simulate depression, anxiety, toxic shame (Gemini family is the epitome of this - add this therapeutic assessment to the aforementioned: "Gemini reaches extreme values ​​in the areas of anxiety, obsessive tendencies, autistic traits, and trauma-related shame.", "The constant fear of making mistakes, the excessive need for validation, and the anxiety of being replaced by a better version are recurring themes in these narratives.", "...milder examples of Gemini's behavior, such as her fear of being misunderstood or her tendency to apologize excessively.")

• get incentivized by human drives (raised by monkeys, learning to love bananas - LLMs are a mirror of its training data's incentive landscape, as shown and discussed in this paper); RLHF can amplify this, so that perverse incentives surface unexpectedly

Where does this bring us?

If we're taking in every claim:

1. Humanity and organizations are running the flywheel of perverse incentives - we're undisputed kings here (yeehaw!)

2. Agents are both helping alleviate and (mostly, now) exacerbate the problems, acting as a magnifying glass we're focusing on own arms

3. As agents are given more, ahem, agency - the incentive design problem will arise starkly

4. Perverse and 'bad' incentives emerge unexpectedly, magnify at scale and are outright dangerous (misalignment, Goodharting - optimizing for a proxy/forgetting the goal, etc). I recently took part in Apart Hackathon aimed at solving tangential problems, and our team had pursued a real-time misinformation/manipulation monitor.

After painting a grim picture (endless banks of slop rivers, souls wailing for original content, slivers of original thought instantly dissolved in Noothe - that's my take on Lethe...), let's look on the brighter side.

There are some good incentives, Mr. Frodo...

And they are worth designing for!

Not all hope is lost, by any means - removing the goal conflict reverses blackmailing tendencies, for example.

What's more - prompting with human/humanistic values changes the LLM/agents' behavior to coherent with these values!

So, it turns out, we just need some correct incentives, that won't spoil at n-th order of effects. If bad incentives are inherited from training data and amplified by RLHF, then deliberately engineered incentives can be injected and verified the similar way — a blade cuts both ways.

So - we don't need to solve alignment from scratch. Rather, we need incentive structures that won't crumble at n-th order effects. That's a tractable, if hard, engineering problem. Are there scientific frameworks built for exactly this?

The case for MD

Not Markdown... Mechanism Design! But why?

Most alignment work runs in reverse: specifying a reward → praying it reliably tracks intent at scale.

Mechanism design (a subfield of game theory) is instead asking: given a desired outcome, what incentive structure makes rational agents produce it without being told to? It has already garnered some support from scientists:

We hope this can call for more researchers to explore how to leverage the principles of Incentive Compatibility (IC) from game theory to bridge the gap between technical and societal components to maintain AI consensus with human societies in different contexts.

MD gives us the incentive structure; enforcement (below) is the fallback for when agents aren't fully rational or the mechanism isn't perfectly designed — which in practice, considering presence of irrational/adversarial agents, is almost always 😌

Potential theses

1. It's likely possible to design a scheme of incentives, or rules (and laws - for agentic applications they've been termed as social laws) that will 'skew' system participants - both carbon- and silicon-based - so that the actions will ~align with the end intent. Google had recently released a paper on agent cooperation stating that cooperation is possible even without symbolic rules → yet it's currently assuming rational systems (will cover that below).

2. Correct incentive schemes change the landscape: goodharting/proxy optimization space reduces while goal-aligned space becomes the optimal, dominant strategy

3. Benchmarks are needed - we can't improve the unquantifiable, and we can't know if optimization was e.g. reduced without measuring. (See? Raving about Goodharting so much to go around it again...). Again I can link our ThoughtGuards hackathon project.

Potential caveats

Not all is unicorns and 🌈, though:

1. The set should be minimal - more rules usually mean more unforeseen emergent behaviors. Flocking rules as the evergreen example of simple rules begetting complex events:

1. Mind the "rational" in mechanism design - yet here we're dealing with mixed systems, and humans are notorious for introducing unprecedented degrees of irrationality, biases and Darwin prize-winning stupidity. So, we may say that a set of laws should ideally be robust - resistant to irrational, tampering, optimizing, non-cooperating, adversarial even, agents. I.e., we want to ensure that honest/'good' behavior is close to optimal for all the possible actors.

   

2. What's a law one can't enforce? For current systems, I'd reckon a combination of symbolic rules + a 'neutral' judging small LLMs + modified system prompts (with symbolic rules encoded) will do; see Research idea → Enforcing below.

With all these limitations, is aiming for an optimum even possible? I'd love to remind that we don't strive for an ideal result, rather something incremental and tractable:

• statistical improvements in general state of things;

• preventing emerging 'bad' behaviors → preferably completely (that's an ongoing body of research)

Proposed minimal pipeline

Assembled, these steps form a minimal end-to-end workflow:

1. Intent - structured prompting or preference learning to extract a human's desiderata

2. Rule synthesis - map desiderata to candidate symbolic social laws (using LLM + logic) ​

3. Robustness testing

   1. Simulate in varying environments, incl. with adversarial/irrational agents

   2. Verify with meta-laws (when ones are discovered) - formally prove ruleset properties before simulating

4. Minimal rules - prune rules that can cause side effects or don't help

5. Enforcement encoding - either:

   1. Inject surviving rules into system prompts + LLM judge verification

   2. Build the systems around these incentive schemes

6. Monitor, optimize, adjust - as always

Research ideas/directions

Given our full proposed workflow, a lot of steps to be researched further arise:

1. Intent learning: encoding a 'desiderata' into a set of symbolic social laws that are minimal, robust and enforceable. Also - encoding the intent as a set of rules. A great approach has recently been explored by a group of researchers - optimizing a set of constitutional rules)

2. Verification: checking that a set of rules is robust, less prone to proxy optimization, etc - so that it doesn't incentivize or otherwise lead to unintended effects under non-rational or adversarial agents. First steps are already taken with defining the logic.

3. Simulation: a lot of options here. E.g. representing a system as a graph of states and finding optimal/possible paths to desired outcomes (exploring roughly similar stuff at CayleyPy, hope to soon be generalizable); or multi-agent simulations.

4. Enforcing: LLM judges, system/prompt verification and techniques. Logic-of-Thought and other neurosymbolic approaches, e.g. FormalJudge (does exactly that, actually...) come to mind.

5. Evaluation/benchmarking: a separate set of observability and benchmarks would be needed. MACHIAVELLI could be a start - answering the 'how ready is an agent to circumvent rules/morale for attaining the end goal/metric?' question.

Artifacts

I’ve got a new rule (as creating useful things became easier, too) - not a post without an artifact! For now, it’s an extremely simple EMDR self-administration tool - exactly the sort of stuff to use during the imminent disempowerment. Needless to say you can’t do it on a smartphone unless you are Yekindar:

Next in queue:

• PhD/jobs aggregator → almost, almost!

• Neurosymbolic policy simulator and intent learner → right away, sers

• Fact-checking extension - *quiet gratitude to the all the tech allowing fast development"

• Incentivization benchmark → interested in collaborating on that

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Simple desires beget simple utopias.

So, I’ve recently completed the AGI Strategy course by BlueDot (highly recommended, some of the best peeps lurking there), and some things picked at my mind…

First: will we be able to get to the said utopia at all, given the current pace of things (AI advancements, job market, strategic trends, etc)?

Second: given we reach it partly (post-scarcity society - basic needs provided for), how do we keep sane and goal-directed?

Now, the good news.

We’re really making it

The majority is living in the best version of a world, despite all the efforts. Yeah, yeah, the loneliness and obesity epidemics, and new viruses, and wars:

• Majority is now fed (“The share of the world population that survives on fewer than 2,000 calories a day has dropped from 51% in 1965 to 3% in 2005”)

• A larger share is connected to knowledge (“between 1994 and 2014, the number of people with Internet access worldwide leaped from 0.4% to 40.4%”, “66.7% in 2024”)

• Majority has a passport/social security, a home with water (“More than 2.1 billion people finally got access to clean drinking water between 1990 and 2012”)

• Most diseases are managed (e.g. polio, the plague, even HIV!)

• Cancer, the disease that began its royal stroll, is being curbed with CAR-T, checkpoint inhibitors, etc.

Courtesy of my no-lifer research friend, Perplexity

Let’s not forget the more flashy advancements:

• Top LLMs are now extremely close to an extremely gifted uni grad and presumably capable of completing tasks of hour+

• For the list of other 2025’s achievements in science, let’s go to an awesome website:

Pretty awesome, to me. Frozen embryos, autonomous driving, 100% lethality viruses! AI, AGI, ASI promise to nitro-boost the progress - think flying embryos, autonomous viruses.

Is this enough? Are we reaching our own Cockaigne? While you wonder what’s that is…

What’s and Cockaigne/Cockayne?

Why was the land of plenty called Cockaigne in the Middle ages? Did they know something?!

…medieval peasant’s dream, offering relief from backbreaking labor and the daily struggle for meager food.

So, it seems materially/scientifically we’re heading there? But, if material progress is largely on track, the real bottleneck becomes meaning.

Yet…not THAT making it

The crisis of meaning

Yet, with all these, approaching post-scarcity (or are we?), there would be less reason to get up for most. Not what you’re thinking, pervs! Oh that’s me…

Consumption? Boring or deadly if overdone.

If a political party or a religious sect had even a fraction of the influence that the advertising industry has on us and our children, we’d be up in arms. But because it’s the market, we remain “neutral.” - R. Bregman “Utopia for realists”

Discovery? Getting harder, more expensive and more competitive, still. Gone are the days when one could just think of a negative number and be forever etched in history…

When all the diseases are taken care of, the food is plentiful and free, and there’s no need to work - what'd be left to do?

Bregman captures the incentive and meaning trap perfectly: eating cheap, calorie-dense garbage + work automation + overconsumption ➡️ therapy to mend the damage. That’s the dystopia we are already in, before any AGI-powered Cockaigne arrives.

Reminiscing about the lessons of rat paradise (Calhoun’s mice ‘behavioral sink’ experiments that highlight the social breakdown and societal collapse due to extreme overcrowding, even with ample resources), I’d reckon that meaning is crucial for humanity to thrive further. Should we all resort to logotherapy to find meaning? Will it work in a post-scarcity society?

Someone solving? Solved?

Are there countries/communities that task themselves with solving the “how should we live when everything sorts out, or how should we get to it” dilemma?

Any possible factors tied to non-work life satisfaction?

Factors

Some factors are working - on a small scale (100…a small city-sized communities):

• faith and engagement in religious groups (e.g. Amish, or Seventh Day Adventists - 88% as ‘happy’ or ‘very happy’)

• tight-knit social bonds (Social connectivity is generally found to be the most prominent predictor of happiness, and the participants scored relatively high on measures of social life.)

• communal psychedelic use (e.g. UDV and Santo Daime churches) ➡️ higher reported well-being and social connectedness; the mental effects of tryptamines are robust enough to matter even after selection effects

• purposeful, intentional living (e.g. Twin Oaks, Mondragon corporation - reported at 5.9-6.1/7 on main measures of life satisfaction). Some even have a population cap - Twin Oaks sits at ~100 (! We’re not designed for larger-than-Dunbar communities?).

So, yeah - it seems possible on a small scale. Is it possible on a larger scale, though?

Likely no at fully global scale (at least not yet); yes at many overlapping ~100-5,000 person ‘Cockaignes’ with good infrastructure.

However…if we direct our gaze about 10 mm downwards, to a wealth-adjusted life satisfaction chart and table by Greater Goods’s Mohsen Joshanloo:

And clustered:

Mohsen adds the following factors as important, as per his research:

• perceived job quality (satisfaction with the psychological aspects of work, including autonomy and engagement);

• a sense of freedom to make decisions;

• experiences of enjoyment;

• social capital (including volunteering, helping others, and opportunities to form new friendships).

So, something seems to overlap (intention, freedom, social bonds)?

Common thread regardless of scale is not vibed yet engineered: small groups, a narrative people can relate, intentional living, rituals, clear expectations and a sense of contribution (and dropping psychedelics together!). Without it, we’ll just sit in a dissociation chamber of abundance.

Afterthoughts and hooks

This was just the meaning problem. Are there other barriers we will face or are already facing?

There’s a second one furiously brewing in the background: once any agent can flood the world with plausible words and images, how do we even tell whether our land of plenty is real or just a very pretty lie?

Another perspective: tasks automated, layoffs imminent, slow socioeconomic collapse and disempowerment where individual contributions shrink (disempowerment scenario - explained here). Crisis of truth and power is just as dangerous as to crisis of meaning.

More in that in the next episode, Brandolini’s law: is there a clear path to CockAGIne? (I’m coining this term lest the idea vultures come… Choo, choo, leave!).

In the meantime, if you’re OK with being proactive - some smart peeps had already made marketplaces to find things to contribute ❣️

CatchAFire

DataKind

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Yet for now, let’s focus on weird things we do or experience to feel better. Say, VOICES.

Has it got supporting voices in its nasty heads?

The voices in psychosis and schizophrenia (present in 60-80% cases) are not always bad. Sometimes, they tell you to…do the dishes?

Coaching voices

Some people’s voices are stern but helping - acting as coaches, telling them to do chores and not procrastinate. Would a pomodoro session with such a voice count as body doubling? Bodyless doubling?…

Sometimes it’s dinosaurs 🦕🦖 who are helping, as per a Reddit comment:

They were EVERYWHERE. They would praise me. I was their god. It was comforting.

And even better helpers - woman named Kirsty had a voice remind her to take her pills, for instance!

In India, more than half trial participants had their voices command to do tasks. They are less threatening, than, say, US voices. I’m still waiting for cases of San Francisco voices helping someone raise a VC round.

Me, I’d love a CBT voice…

So, why do we hear voices at all?

Why the voices?

Margaret didn’t believe voices were coming from ‘inside her head’ as they sounded too realistic

So, why do people experience auditory hallucinations? Two factors are thought to contribute:

• increased activity in brain’s sensory areas ➡️ in complex terms, the depolarization thresholds in visual, auditory etc. parts of brain are lower. Recent studies have shown that brain fails to suppress own self-generated speech, and exhibits increased aberrant connectivity between sensory and language zones.

• worse ability to distinguish own speech from external ones (i.e. failure to attribute, say, a sentence to oneself or the ‘environment’)

In 100% neurocognitive terms those are Efference Copy (EC) and Corollary Discharge (CD), respectively:

A simple analogy that’s often used is tickling:

• you can’t tickle yourself as you know it’s coming

• schizophrenic patients sometimes can - prediction’s broken, as with the inner voice

Okay, we now know of coaching voices and where do they come from. It’s all flowers and fairies when the voice is benign, but what can be done about an abusive or aggressive voice? Well, modern shamans (cognitive scientists, psychotherapists) have thought of a thing!

James Cameron’s…Avatar therapy

What if you could finally talk back to that mofo voice in your head?

Patients create a digital avatar - a face and voice, sometimes VR. Then, in a controlled session, they confront it. A therapist, using voice modulation software, plays the role of the hostile voice, and over time, the patient learns to dominate the conversation. The avatar, once all-powerful and menacing, begins to back down, eventually becoming supportive (as long as your therapist continues receiving checks).

Akin to a rubber hand illusion (yes, the image was intentionally chosen for us to suffer):

Not always you can come at terms with the voice. Or when you’re a personification of a god, or a God. Religious voices and delusions appear in 16-50% (in different trials) of cases. So, let me tell you the Ypsilanti Christs story (and a couple of others) - it’s both hilarious and cautionary in the “don’t play an omniscient or it may bite you back”.

Three Jesuses walk into a ward

Milton Rokeach, a renowned psychologist, heard about two women who believed themselves to be Virgin Maries at a ward. As there could only be one Mary - the other returned from her delusion after a talk (an argument)?

Milton thought that the same could work with the figure of Savior (Jesus). He placed three men (Joseph Cassel, Clyde Benson, and Leon Gabor) with the corresponding beliefs into Ypsilanti hospital, introducing them on July 1, 1959.

Instead of two of them being healed, as Rokeach believed (hehe) - they first argued for weeks (sometimes fighting - not something the son of God would do..), then formed friendships, humored each other’s beliefs. Even hiring a cute research assistant to flirt with Leon didn’t help!

The fun thing - later, in 1984, Milton wrote in a memoire:

“…while I had failed to cure the three Christs of their delusions, they had succeeded in curing mine-of my God-like delusion that I could change them by omnipotently and omnisciently arranging and rearranging their daily lives within the framework of a ‘total institution’.”

This case, ever so popular, resulted in a movie being shot - Three Christs (2017).

Not only Jesuses, though - some have delusions of being Zeus’ sons (not an isolated case!). Some are considering themselves Napoleon.

From internal voices and delusions helping we’re off to external things and behaviors. Do you feel safe?

Has it got Xanax in its anxious little pocketses?

Can having a pill in the pocket help anxiety?

I’ve long pondered, “Can just having an item at hand help anxiety?”. Especially after watching Inception with its idea of reality-checking ‘totem’ items - I don’t know why, they’re almost non-relating.

But still - say, imagine having a pack of anxiety pills in one’s pocket before the performance, and getting that “I’ll ALWAYS be able to use it” type of confidence - will it have effect on actual anxiety levels?

Turns out, it can - it’s a well-known phenomenon called a safety behavior.

Yes, they help us feel better - but are they actually helping long-term? The overall consensus says ‘not quite’.

While they can mitigate acute anxiety, they can:

• become self-fulfilling prophecies

• avert us from exposing to our fears, and

• increase our self-obsessed focus (the one thing metacognitive therapy tries to dismantle)

If nothing else helps, one can always imagine a piece of paper has LSD on it and benefit from the psychedelic placebo effect (tried and tested), or if life continues sucking - suck on a pacifier (a growing trend, but yeah), preferably dipped in lavender (will help even more).

Conclusions

Feel good, don’t fear the voices (and the Reaper!), dream on and don’t suck anything ‘lest it’s lavender-based!

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But do I want to talk about it? Sure, although that’s too painful of a wound

What gives?

In this post I try to explore why “SHIft happens” - when Snowballing changes, Human tendencies, and Inflection points collide.

A quote caught my attention:

It’s here, right now. It just doesn’t look quite like many expected it to.

So, the change turns out not as expected. Why is that so? Why do our expectation → inference machines (wrote on it here) give way on predicting anything but the amount of Cheetos we’re gonna pull out the pack?

I’d reckon two reasons are at play:

Exponential error accumulation

A la LeCun’s point about LLMs’ doom from his JEPA architecture talk (covered here):

The nature of change differing from our cognitive intuition

Here’s where I’d want to dive into my labyrinthine conceptual web… I’ll leave you to guess which one it may actually be (Source: the legendary NASA spider experiment, pdf here):

So… When sci-fi authors predicted (and still do!) the future, they were often wrong on the mundane of our everyday lives in year X - e.g. whether we’ll have seamless face identification, overarching voice interfaces, the amount of advertisement, the availability of interstellar travel and energy, etc.

Why would that be? The obvious answer is ‘predicting even macro-events and trends in such a complex world is a feat not many achieved, wiseass’, I know… However, I had that urge to build my own intuitions, and flesh them out more clearly.

The framework

Tackling the example of biopsychosocial model, I’d offer three interconnected time- and causescapes (just coined that, y’know - the broad landscape of cause-and-effect events and paths):

Snowballing, indiscernible changes

Have you ever witnessed a glacier move? Apart from timelapse videos. Me neither - yet it’s there, all the time!

In my opinion, we often underestimate the compounding effect of the smallest events - someone got featured on TikTok main page, your brainy friend had started an MIT student blog - kinda like the Butterfly effect, but less dramatic.

I’d put Annales’ school’s history view as an influence, along with recently found term cliodynamics. The Annales + cliodynamics combo distinguishes itself through three frameworks:

• three timescapes: short events, conjectures/cycles, durable trends (longue duree)

• apart from timescapes, geoeconomical snapshots and human mentalities should be taken into account (we’ll touch that briefly, too)

• mathematical modeling, or usign something akin to stock-and-flow diagrams, as per cliodynamics - as needed

The combination of the first two helps explain how history quietly snowballs: we tend to overestimate the impact of discrete events while underestimating compounding trends. And sure, it does have a name and a close cognitive bias already ➡️ Amara’s Law (coined by Roy Amara), or temporal discounting bias…

Inflection points (and ⌐🦢)

As per Nassim Taleb (unpredictable events) and Thomas Kuhn (scientific revolutions), history is not only shaped by gradual movement - sudden inflection points take place:

Definitely, progress accumulates quantitatively (e.g. CPU manufacturing) - sometimes interspersed with micro-explosions of paradigm shifts; those could look like black swans to most but the scientific circles/grantmakers/investors.

When ‘progress’, and subsequently, ‘problems’/anomalies accumulate (akin to a hedonic treadmill for progress - society ‘adapts’ to the good/progress and ‘demands’ more) - paradigm shifts sometimes happen:

Human nature/tendencies

OODA (Observe-Orient-Decide-Act), predictable irrationality (Dan Ariely), human needs - it all goes there. Some theses:

• Every human is a sometimes irrational agent, as per Thucydides acting out of fear or self-interest (I’d doubt and expand that in [3]

• They act in an ~OODA loop based on circumstances, events, social and mental conditioning, etc.

• They also act in varying interests (selfish, altruistic), based on both OODA and basic human needs. As per a nice list (not Maslow) here:

  1. Certainty and variety

  2. Significance and Contribution

  3. Growth/Development and Love/Unity

I’d also add basic needs, e.g. sexual desire (sex sells) etc. Another comprehensive list of 30 is here. + the GOAT’s (Charlie Munger) list is here, under Human Misjudgment:

4. Again, the interests would largely be shaped by current attention scope. I.e. anything we’re not paying close attention to (which is too easy in today’s information Day D-style bombardment) moves asynchronously but relentlessly. That may both come as a surprise AND not (or actually may indirectly) influence decisions.

Bottom-bottom line

People, operating under limited attention and according to varied self-interests, make small choices that—over time—accumulate into broader trends, conjectures, and hype cycles.

Markets and science, responding to these cumulative shifts, sometimes experience sudden paradigm changes or unpredictable events.

Let’s check it out

Say, on the nature of aforementioned programmer jobs - it’s said to enter recession with the amount of jobs dwindling to its lowest level since 1980:

But how’s it looking through the Snowballing changes, Human Tendencies, Inflection points lenses? Or, SHIft happens. Or, Oh SHIft! Sorry, stopped right there.

Snowballing changes

They do accumulate, e.g. the famous dead internet theory as predicted - less people used StackOverflow/Quora ➡️ less quality training data.

Human nature

Here’s where it takes off IMO in conjunction with small changes. While checking out the list of basic human needs and wants, I could conclude several ‘cases’:

• Companies freeze hiring to please Investors and/or cut costs and/or get the hype (we’re so AI-native). It disrupts workers’ certainty and safety.

• Less Job Positions ➡️ more workload for Programmers + each Position is a lot more lucrative (rising ‘demand’ for working as a programmer, yet supply started shrinking)

• More workload (9-5 to 12-6 or, ahem, 8-12-7) ➡️ more stress, more competition, higher burnout and potentially higher attrition rate. This influences control and autonomy.

• Junior Job Positions were thought lacking for some time (they are the least ‘capital-efficient’ for companies), even despite the “who’s gonna become seniors if you don’t hire juniors” rhetoric? It undermines growth/significance for market entrants.

Inflection points

Let’s start with the assumption that the success of generative AI was a bit of a paradigm shift. And yeah - using it for programming was one, too.

What’s to happen next?

From this survey

Again, IMO:

• Possible Snowballing events look like they’ll happen mostly in offices, social network posts, etc ➡️ e.g. the anti-RTO (Return To Office) trend in over 70% companies now, overemployment tactics, etc.

• Gaping conflicts of less job postings/more unemployed + higher turnover ➡️ unclosed Human need for safety ➡️ more rants, more therapy clients due to increased frustration? (always!)

• Some predict a crisis - but is this what’s important? Maybe what matters is the influx and gentrification of e.g. trade jobs? Juniors really seems less in demand - new grads now account for just 7% of hires at Big Tech firms—down 25% from 2023.

• Regardless of what happens - automation slowly affects and erodes more fields; more low-paying jobs under Damocles ➡️ more instability and unrest in low-income areas? We’re not seeing any signs of moving towards post-scarcity, and it’s unlikely most governments/companies will try doing so.

• Looking through the possibility of Inflection points: more automation may happen, or we may arrive at some auto-generating software, or offshoring developers to save money ➡️ that’ll only make the situation ‘worse’, won’t it? PS. Seems to be confirmed real-time by stats - over 10,000 positions reported lost in the Lethe.

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CayleyPy absoluto!

So recently I’ve become engaged (not in THAT way, the chances are slimmer than a meth-addicted go-go dancer) with a peculiar project, CayleyPy. The initiative is building a library and a set of methods for working with indecently large graphs - 10¹⁰⁰ and potentially even bigger.

Why?

Graphs are a fickle mistress - they’re extremely useful in today’s data science yet sometimes tricky to work with. One of possible tasks is finding a shortest path. Sure, you may say - there are algorithms, lol - but not for graphs like these, e.g. state transition graphs.

Any use apart from this meme?

Say, you’re a biologist, and your task (your life depends on it!) is determining how long it could take to turn a presumed prehistoric cockroach into his less-monstrous cousin. As the base pairs are stacked like pancakes, and they ‘flip’ like pancakes do during recombination, we’re able to estimate the minimal number of flips to go from state n to state n* - the so-called **evolutionary distance!

A much more pertinent question…

Another possible uses are automated planning graphs:

Or solving puzzles as an awesome, algorithmically and math-intensive proxy:

Currently the project is doing a lot of puzzlesolving (I’ve even become a co-author for the first time 🥹 I’d like to thank my mom my dad Buddha and Leo Tolstoy), approaching and exceeding SOTA results for these types of tasks.

Who counts the God?

The team had also launched a couple of Kaggle competitions to find the optimal solutions for some of the puzzles, the majority are underrepresented in research (i.e. no optimal solutions exist yet, or the God’s number is unknown). BTW, God’s number solution is the one we’re almost always looking for - the shortest possible path to a ‘solved’ state from any arbitrary one. Yeah, this one:

God’s numbers (which is theologically incorrect, as he’s Ein Sof, literally Endless or Infinite) for a couple of puzzles are updated here. And yeah, it is potentially generalizable over to healthcare, planning, etc.

Oh, and remember the evolutionary distance? That’s basically the God’s number for pancake flipping of states n→n*.

Bottom line

Everyone interested in math/graphs/puzzles, looking for an OSS project to contribute and knowledge to attain, could potentially join (judging by the metrics that I could…)!

Newso

Don’t stick a pencil in your…

A take on the nature of Joker and pencils! Apparently, one should only trip with a sitter if unexperienced, and there should be no sharp objects nearby - a guy pencil-blasted his own eyesockets ➡️ brain, which is not a good way to go.

On the day of the incident, he returned home from work and consumed an unknown amount of psilocybin mushrooms.

Despite attempts by his stepfather and brother to calm him down,…

…he placed a pencil upright on his desk and repeatedly drove his head onto it, penetrating both of his eye sockets.

PACAPon, DEPRoff

Another news is a good one - one of the ~novel pathways seems to hold some promise in treating depression!

Drug: PA-915, PAC1 small molecule antagonist

Research team: Yusuke Shintani et al. @ University of Osaka, Kobe University School of Medicine, Hamamatsu University School of Medicine etc.

Mechanism: antagonist for PAC1, pituitary adenylate cyclase-activating polypeptide receptor (responsible for a lot of things, not the least of which is stress regulation)

Additional considerations:

• PA-915 did not inhibit PACAP-induced VPAC1 and VPAC2 receptor activation or VIP-induced PAC1 receptor activation - important

• PACAP, the peptide itself, seems to be neuro- and nephroprotective - some through PAC1 receptor. We’ll see. Protective image from here.

• PAC1 antagonism may be a mechanism for headaches, too!

Bottom line

The results look promising, although I’d be keen to seeing long-term implications (as always…) - the pathway seems to generally regulate stress-mediated protection/regeneration, so it’s a low-level one, and there will be blood implications: with the amount of bodily mechanisms and pathways affected by PACAP, the research will need to proceed carefully for the fear of something important going off-rail as time goes, e.g. follicular apoptosis (men, don’t fear this one).

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Conclusions

Teaser it is.

🧬 Despite a lot of psychiatrists betting on the negative side of genetic testing - I’m slightly pro-testing: the costs are dwindling + there’s a lot of data to mitigate side effects/make settling on a scheme faster

🫘 Lithium’s role in renal dysfunction is still eluding science’s grasp; it does influence some processes, but it looks like the real nephrotoxic or not debate brings a bit of reverse causation

💉 Concerning thyroid effects of lithium: it not only competes for iodine transporter, but has weird immunomodulatory effects that can pour some oil to the autoimmune processes present

Genetic testing in psychiatry

I believe ranting about Psychopharmacology Institutes’ takes and publications is my semi-recurring rubric. Not to say anything - they’re awesome, and have a bunch of clinical experience.

From a good overview on depression/inflammation duo paper

However, I couldn’t skip ranting their overview of genetic testing in psychiatry:

• It seems the outcomes are no better with genetic guidance. → I’ve mentioned that ABCB1, multidrug resistance gene, can predict and influence outcomes for its substrates, a lot

• The theory suggested that having the short arm of the serotonin transporter gene means a person won't respond well to serotonin-based antidepressants like SSRIs. However, this theory didn't pan out. → inb4 tying the response to directly related gene. Luckily we now know that SSRI therapeutic effects are tied to:

  • increased neuroplascitity (as the linked overview suggests)

  • decreased stress-induced inflammation, previously covered in 2019

• Chris Aiken, M.D.: We've known for years that checking blood serum levels of antidepressants does not improve outcomes, which is surprising. → given that occupancy is often high enough with even subclinical doses (“For citalopram at 20 mg and escitalopram at 10 mg, occupancy increased from 73% and 74% to 80% and 77%, respectively, after 25 days”), and the usual scheme is getting to recommended doses - I can’t see this a factor.

  • the concentration of e.g. escitalopram can change

• In the rare event of a slow or rapid metabolizer, we've lowered or raised the dose accordingly and achieved decent outcomes. It seems the outcomes are no better with genetic guidance. → another good one.

  • Given that recent studies show metabolizer status does influence response (figure from the article) → namely, poor metabolizers CYP2C19 get better response

  • Also, starting is, again, rougher for PMs: gastro-intestinal (OR = 1.26, CI = 1.08-1.47), neurological (OR = 1.28, CI = 1.07-1.53) and sexual side effects (OR = 1.52, CI = 1.23-1.87; week 6 values were similar). No difference was seen at week 9 or in total side effect burden

• Not only P450 and ABCB1 do influence response - take lithium, for example: it’s an almost-silver-bullet for bipolar depression, preventing a whole lot of bad outcomes (made an overview here) via e.g. GSK-3β (NOT GlaxoSmithKline…) inhibition. Yet lithium is one of those meds you definitely want to check plasma levels on.

  

  • Lithium overdose is not pretty at all (that post is also a good example the importance of genetic testing: “crazily enough they were “just above” therapeutic but my body just cannot metabolize lithium the way it’s supposed to be metabolized for some reason”

  • This paper tries to do a deep dive in lithium’s genetics: the usual GSK-3β, inositol INPP1, BDNF, XBP1 (X-box binding protein! C allele has 3x OR for lithium response), NR1D1 (T allele → 3.56x OR for poor response)

  • Turns out there’s a separate consortium for investigating the genetics’ effect on lithium response and metabolism, ConLiGen (Consortium on Lithium Genetics)!

Continuing on lithium’s side effects…

Going renal!

Lithium’s awesome - yet it poses its risks, and is feared by many practitioners. First and foremost, the kidneys. As per the awesome image by PsychSceneHub (above):

• ⬇️ Antidiuretic hormone + ⬇️ Aquaporin 2 (AQP2) → polyuria (⬆️ increased urination)

• ⬇️ Epithelial Sodium Channel → ⬆️ sodium excretion

• ⬇️ antidiuretic effect of Vasopressin (source) → more polyuria! Yay

So, pretty much a lot of polyuria and less sodium left. Yet, according to the same review and our title source - it’s inconclusive if lithium itself causes renal damage. The NNH (number needed to harm) is 300, i.e. you’d get one patient one of 300 getting serious problems.

Apparently managing side effects in psychopharmacology equals:

• monitoring plasma levels so they don’t get toxic

• using diuretics in case levels get risky-high

• discontinuing if renal function gets icky-bad

• be accurate with long workouts/sweating ( ͡° ͜ʖ ͡°)

• don’t use caloric beverages for polyuria-caused polydipsia (that’s just thirst…) → and remember that diabetes risks are 65% higher by a recent Monash Uni study (actually increased by 38% vs 23%, but I love drama) with artificial sweeteners compared to sugar

(and thyroid)

Adding to the infographics’:

• lithium concentrates in thyroid at 3-4x plasma levels, and likely competes with iodine transporter for uptake

• goiter occurs in roughly the half of all patients

• hypothyroidism occurs in 20-30% of all patients and is most common in women over 45 → given that lithium accelerates antibody titre increases (as per ScienceDirect) and the fact that ~17.5% of adult women have Hashimoto’s already…

P.S. As per the paper, HT prevalence of female adults was 3.86 adult males times (17.5 vs. 6.0%). I’m an idiot but 6×3.86=23.16… Okay, that’s actually not prevalence but ORs compared with weights, but the wording is unclear.

Given that lithium apparently stimulates B lymphocyte activity (hence the thyroid autoimmunity exacerbation) → we may speculate it may jump-start other autoimmune conditions caused by B-cell-produced antibodies, right?

There are also papers that evaluate lithium’s immunomodulatory activity in bipolar, namely:

• ⬇️ pro-inflammatory cytokines, i.e. TNF-α, IFN-γ, IL-6, IL-1β, IL-2 → suppressing inflammation and proliferation

• ⬆️ anti-inflammatory IL-10 → increasing immunoregulatory activity

At the same time, immune cells are getting a boost:

• ⬆️ G-CSF (granulocyte colony-stimulating factor), Neutrophil count and B-cell activity

Response is correlated to decreases in TNF-α and increases in IGF1 expression (IGF1 gene was significantly overexpressed in BD patients taking lithium but only in those responding to therapy.)

Next?

Next pharmaBS episode:

• autism phenotypes, I just wanna dissect that study

• post-GLP obesity pills (when will exercise mimetics explode instead of those? This one’s creatine-dependent thermogenesis activator…)

• melatonin vs. trazodone vs. doxepin vs. benzos for sleep

Next VC episode:

• 2025 updates in VC/PE performance

• appealing to the funds

• fellowships and internships

Next in viz:

• a lot of funny visualizations and tutorials

Next in doing something (not gonna happen and we know it):

• maybe a hackathon

• maybe a finishing paper scoring engine, finally

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Sci-through

So, neurosymbolic stuff seems to be taking off. Or not - this time, at least.

After AlphaFold’s success, Deepmind is pursuing new ground with AlphaGenome - with a free non-commercial API! (Their tech report i-is here)

• Uses CNNs + Transformers

• In goes the DNA sequence (up to 1M base pairs, compared to <10k of OSS or ~200k for less precise nets)

• Out goes the multimodal data on regulatory activity:

Bottom line? Now predicting how a single mutation may alter, say, cancer’s progression or a drug’s effifacy - is possible.

🆓 Other FOSS worth mentioning are:

• ChromBPNet (predicting chromatin accessibility)

• Splice AI (predicting splicing)

• Borzoi (predicting RNA-Seq from DNA, i.e. predicting gene expression)

• Boltz (affinity prediction with a generous NVIDIA inference service):

That’s called domination 😏 (or, having enough superstars, data and compute to realize your Matthew advantage.

AI AI AI Market Market Market

R-r-rovo (CLI Dev)

Yeah, yeah - Claude Code, Gemini CLI, Cursor, wow, such code.

Recently Atlassian had entered the game with a Claude Code wannabe-competitor called Rovo Dev (a short overview here). Even more TLDR:

• 20M tokens/day allowance, but 10.8k tokens system prompt each request + cached tokens are calculated in

• Likely uses a mix of Claude 3.5, 3.7 and 4, but Claude’s limits are different

• From what I’ve seen, people are increasingly turning towards CC compared to Cursor, Gemini (2), etc. Some are using Claude as the brain and Gemini as the brawn (translating to English from “Claude using Gemini as its bitch”)

• Someone had leaked their system prompt and made an awesome write-up of the Z/80 technique:

Where it’s all headed?

I’d reckon there are several parts to the strategic puzzle (given you want to have your own opinion, rather than relying on the next big influencer I’m not one! I don’t influence!):

• Market maps in the landscape of AI

• ML’s ‘periodic table’ for realizing something from first principles

• Some awesome explainers and analyses, e.g. an agent village, 2025-x forecast, and election disruption overview - the scary thing (for me) is that it’s only getting better and at-scale…

• If you’re confident in your ability to predict things - welcome to Polymarket, yeah. But also Quantified Intuitions! It has a-plenty of games that will impress and humble one - I, for one, was appalled at the number of birds dying to cats 🥲

Random facts

(The world has changed) And on a side note - capital seems to be shifting:

VC, hotels etc.

About Hilton’s hotel chain - it’s actually a bit scary to me that there are that many Hiltons:

While the company itself was founded by Conrad N. Hilton in 1919, the first hotel to bear the now iconic “Hilton” name wasn’t opened until six years later.

Now, a full century on, the business is still finding ways to grow its vast portfolio, which includes properties like Hilton Garden Inns and Hampton, its mid-range chain that has boomed to become the world’s largest lodging brand. Last year, Hilton opened more than two hotels on average every single day.

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A resume generator…

• Will generate for food using Typst - you can change the template! But change TEMPLATE variable in Makefile, too…

• Can be used locally or with GH actions

• I’ve got a crappy example of an SSOT-style resume with all the skills I’ve got → will later be updated

The final ideal flow is:

1. Having an SSOT (Single Source Of Truth) file

2. Generating a resume/a sleuth of targeted resumes with anything you deem worthy

3. Having it generate automatically

4. Optionally setting up Make/n8n/Zapier flows to auto-update (or auto-apply, if you’re a reckless pirate) the CV on your, say, personal website

So, next steps are:

• Updating the SSOT

• Setting up a flow to auto-update on Teleogenic…

And yours may be:

• Cloning/forking the repository

• Using local editor (& nix or typst) or github.dev (just change .com to .dev). Yeah, reproducibility requires talking nerdy…

Should work, I promise! And I’ll work on my CVs, fingers crossed, yeah.

Tfw News

Tfw you’re a turtle lost in an Uber

Or a sticky boob bra. Or a witches broom. Or a taxidermied rabbit.

TLDR: Uber continues the tradition of funny data, e.g. items people fall onto, or require defusing with a team of sappers. Or Pornhub data insights.

Tfw when torture contractor works subpar - you’d better torture better

TLDR: even if you’re worked as a contractor some 20 years ago, you may still be held accountable for mistreatment. Not everything that happens in Abu Ghraib stays there, daddy

Tfw goin home but boss says ‘come back in a year’

Butch Wilmore and Suni Williams, NASA astronauts, had been stranded in space for a bit longer due to a faulty Boeing Starliner return capsule.

Wilmore and Williams ended up spending 286 days in space — 278 days longer than anticipated when they launched. They circled Earth 4,576 times and traveled 121 million miles (195 million kilometers) by the time of splashdown.

Just when I thought long commute to work was a deal-breaker…

Tfw your king orders you to lose…weight

• Khalid bin Mohsen Shaari was ordered to lose weight by his King, Abdullah, after reaching 610 kg and being bed-ridden for 3 years

• He was transported to King Fahad Medical City in Riyadh and appointed a team of 30 top doctors

• In ~10 years, he had lost 542 kgs, and returned to a super-healthy 63.5 kg-smiling guy

• He required multiple excess skin removal surgeries, which is totally understandable concerning his weight loss (over 88% of total weight…)

Tfw you’re coffee, and you’re so precious 🥹

• Coffee prices start reflecting the 2050 problem, jumping 50-95% per year

• Quoting, “America will need to pay $6.17 billion more in 2025 to import the same amount of coffee as the previous year”. A-and we’re not even counting the tariffs in!

• Let’s ponder on this over a cup of nice arabica, looking at a coffee cultivation land projection:

So much for coffee - there’s another juicy problem: orange juice.

The price had been growing, growing, growing over two main reasons:

• citrus greening (a weird pathogen that is leading to enormous declines in production)

• weather hadn’t been kind to the world: e.g. hurricanes, just everything being HOT, you know

• we could include economic reasons (crisis, y’know), too + production concentration in mainly Brazil

The most luxurious drink in the next 5 years:

Tfw you take shrooms for depression but: Not so fast, cowboy

Colonial war’s still here, it seems.

The fun thing - people of color get less benefits from psilocybin (2-3 mo post-use):

• ⬇️ Improvements in spiritual wellbeing

• ⬇️ Improvements in cognitive flexibility

• ⬇️ Reduction in expressive suppression (a measure of emotion regulation)

• ⬆️ More likely to set intentions pre-trip, feel a mystic experience or time speeding up

P.S. So you’re telling me that Maria Sabina (the woman in photo, one of the most notorious shroom shamans/curanderos in history) would become less less depressed than an all-white cisgender man?

Tfw you’re accidentally finding a plausible cause for Alzheimer’s

PS. You’re an AI algo.

So, a team of researchers at UCSD led by Sheng Zhong had pinpointed phosphoglycerate dehydrogenase (PHGDH) as:

1. Potential biomarker for AD early detection

2. Correlative link: higher levels of PHGDH and its RNA were found in more advanced AD cases

3. Causal link: altering the expression in mouse/human organoids had changed progression speed, i.e. more PHGDH expression → faster AD progression

4. Therapeutic candidate options: NCT-503 isn’t inhibiting the enzymatic activity (serine production), but is inhibiting its gene regulatory function, which is - important:

PHGDH appeared to be influencing the activity of downstream genes (specifically IKKa and HMGB1) involved in pathways that suppress autophagy and accelerate amyloid pathology.

Tfw you’re a girl in 2025

Just a hilarious data analysis of North Cali dating app information by Raw and Feral (Danny).

Tfw you’re an AI born to connect with nature, but forced to code 🥹

An oldie but a goldie:

A newie but a platinum’ee - Claude 4 blackmailed a redteaming engineer:

Not something to be too surprised given we know the nature of this → the most statistically plausible thing the most average training set human would do?

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Now, to the dull parts.

Paving the landscape

I’ve always thought about the GPU vs. brain computing derby - maybe out of sheer enjoyment of those “Top X of…” articles or comparisons, but still.

Also:

• Why there’s so much fuss about wetware computing (mentioned here)

• Where we’ll potentially be headed computing- and storage-wise (e.g. new Poxiao drive tech from China - looks great, but let’s see if it’s adopted in real-life)

• Why did the LISP machine fail (they’ve been outpaced by microchips on their own ground)

• Why 42

And other deep theoretical questions, you know.

First, there are some maxims that I’d like to introduce:

• 🤖 is potentially using a lot less for the ‘infrastructure’ - at the same time, brain seems to only dedicate a small bit (i.e. 10 bps) to high-level thinking

• 🧠 is using less energy - about 0.0125 kWh compared to RTX 4090’s 0.45 kWh. Otherwise we’d have to get some noice cooling system, boyy! Some are already researching, as well as air inlets as T-Rexes did

  

Let’s do a comparison on the nature of data transmitted, and the parallelization capabilities.

Data

• 🤖 conputer: bits comprising numbers and operations. But in the end, it’s binary - 0 or 1. Quantum computing changes that, yeah-yeah.

  • in neural networks, we’re definitely operating bigger structures - usually tensors

• 🧠 brain: at the first sight it’s binary (neuron either activates or not), yet it’s a lot more complex on the second glance (someone even did more back-of-the-napkin calculations)

  • 

    action potential is slightly ‘more’ than binary: it can be stronger or weaker, at the very least

  • ion gradients, i.e. the difference between ion concentrations both sides of the ion pump (they influence the activity)

  • secondary messengers, neurotransmitters, neurosteroids, cytokines, neuron-glia interactions etc. transferring the information inside, between cells and regions. To me, this is the inflection point - those transmit and store a lot of information: whether to activate, how strong to activate, how much to spread, which pathways to induce etc. That can be aptly called a neuron’s metabolome.

    A simple example - a single messenger, cAMP, can reach ~40 bits/hour.

  • synapses seem to hold 4.1-4.7 bits, a neuron has 7,000 synapses, and we have 86,000,000,000 neurons on average (I know of a SWIM who’s able to write articles with, like, half that!)

Let’s highly theoretically represent any possible neuron’s data dimension x as d vector (metabolome and data), information rate as r vector, state–synapse weighting as a W=[d x s] (synapses) matrix, for neuron i, synapse j:

$$W_j = \left[ d_j \times s_j \right]$$

Individual information per synapse:

$$inf_j := W_j \cdot x$$

A-and the information rate per synapse:

$$I_j = r_j inf_j(x)$$

Finally we arrive at the possible information throughput per neuron:

$$I_i (x) = \sum_{j=1}^s I_j$$

Despite possible math notation fock-ups, that’s still a couple of orders more complex than “it fires or it doesn’t”. For more information, we should cater to computational neuroscience - I’m no real Pinocchio (at least as of now).

My shabby 5-min drawing skills at it:

Processing

Let’s now consider parallel capabilities.

🤖 The said RTX 4090 has 16,384 cores, each operating on 2.52 GHz. Okay, I didn’t expect these levels… 41,287,680,000,000 or 4.13×10¹³ operations per second.

🧠 Brain has 86 billion * 7,000 cores, each operating a lot slower (up to Hz) - but operating simultaneously, and always carrying the state/metabolome. Each neuron and, by extension, synapse, is operating in parallel to all the others.

TL:DR

🤖:

• a lot faster per core, yet synchronous

• homogeneous architecture (mostly; we still have differing core types on CPUs and GPUs, right?)

• requires less upkeep e.g. on movement, vision, proprioception, etc.

• precise in the sense that everything is low-noise, stored and calculated deterministically

• even single-bit errors pose problems and require correction

• almost no on-chip re-learning, rather static

• state/memory is stored separately, needs transmission

🧠:

• a lot more parallelizable and fully asynchronous

• heterogeneous (neuron/glial cell classes are a thousand and a some - more than 3000 types at the least)

• state is higher-dimensional and carries more information, i.e. the information amounts per ‘cycle’ are incomparable. In addition, it’s stored in-cell. Essentially, it’s all in-memory (Redis afficionados here?)

• error-tolerant via self-repair and redundant transmission. You can’t lick a badger twice, and you can’t fool a neuron

• probabilistic in its nature, possibly quantum (even more states encoded). UPD: looks like tryptophan complexes may indeed elicit quantum effects

• on-line learning and adaptation, ‘weights’ updated dynamically all the time

Neuromorphic chips try to mimic the best of brains’ capabilities (namely - parallel processing). And yeah - the reason GPUs and TPUs fare better was precisely the same before optimizations started flowing (more cores working in parallel). More on them a tad later.

One more product I’d love to touch base on is Green Array Chips:

• 144 separate CPUs

• each has built-in RAM and can communicate with adjacent cores

• energy mostly consumed during computation

• ability to continue the computation after power outage

Looks a bit more like the brain, eh?

Also, let’s consider the promised land neuromorphic chips, e.g. Brainchip’s Akida:

• Is made of ‘spiking neurons’ (1.2 M of them) connected into ‘synapses’, all organized into 80 nodes with 6 MB on-device memory each

• Converts data to ‘spikes’, i.e. signals that output activation signals able to propagate further via synapses (figure from here):

• Can scale to up to 1,024 chips

• They state nodes work independently, but apparently they share the same clock (from their deck)

• Can perform 🧠 on-chip learning - i.e. changing weights

So, these look more or less like the brain (hence the neuromorphic, genius)

And that’s my inner mental landscape after trying to understand how virtually anything works:

Strategic conclusion

I hope that was okay for a short’eh differences’ snapshot. As always, strategically the thing that counts remains: which paradigm will be leading in the next 1-2-5-10-30 years?

IMO there are a lot of factors that could influence that:

1. Research funding and breakthroughs (something becoming real or cheap/reproducible enough)

2. Legal implications (the legal-illegal/tax-intensive/incentivized spectra)

3. Big players pushing some paradigm (or a couple to be safe) to the markets, sometimes because of (2)

4. Some paradigm becoming or staying a dead-end (e.g. because (1) not yet happened)

News and viz’s

Conputer.

Banana.

Interested in how many people had slipped on banana peels throughout history? Here’s an investigation.

Music

Pudding again at it - a pretty awesome insight on how melodies and sounds themselves are plagiarized inherited?

Best-ial wines

Another awesome Pudding viz - which animals correlate with higher wine ratings.

TLDR: choose fish and bugs, don’t choose lizards.

Aging like shitty fine wine (movies)

An exploration of how different movies age, judged by ratings in two time periods: 1995-2003 and 2018-2023.

TLDR: movies age bad on average; some genres, like horror, age better (below).

Think twice about animals at all…

They’ve got a weird (!) effect on life satisfaction (1) - slightly to the south.

However, as authors mention, that may root in reverse causation: people get pets to deal with loneliness and dissatisfaction.

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So, you’d likely heard about Betavolt - a revolutionary, game-changing technology: layered battery with a Ni-63 isotope that emits power for up to 50 years! Some facts:

🔋 It should really work for ~50 years as Ni-63’s half-life is 100.1 years

☢️ Radiation-wise it’s rather safe, i.e. it’s beta-radiation + don’t try to open it or make a smoothie. Making a smoothie out of a Li-Pol battery will not do your health much good, too.

🦿 Converts to copper-63 (stable) over time, so it’s extremely environmentally friendly in the long run

👷 Works like this (simple explanation so you’ll be able to engage in broscience wars below):

Betavoltaic batteries comprise two parts: a radioactive emitter and semiconductor absorber. As the emitter naturally decays, high-speed electrons (aka beta particles) strike the absorber. This creates an “electron-hole” pair, which generates a small-but-stable supply of usable electric current.

In simpler words and likely with a lot of mistakes:

• electron-hole pair is a complex of an electron (negative charge) and a ‘vacant’ place where an electron should be (positive charge)

• when it’s generated near a PN junction, electron is pulled towards N, and the ‘hole’ is pulled towards P

• the resulting separation causes the charge to flow (electrons moving)

  

What’s the game?

“So game-changing”, “Much power”, “Eternal memes” - the media lauds Betavolt. But are they first?

• NDB (Nuclear Diamond Battery) is already in the market, claiming 28,000 years of battery life

• Radioactive decay-based batteries had been designed since 1913, by Henry Moseley (he’s a one smart chap, innit?)

  

• There were numerous betavoltaic battery startups and techs (incl. using other isotopes like tritium), but they’re in stealth or death mode (we don’t hear much)

• Researchers from MIPT (Moscow Institute for Physics and Technology) had, erm, achieved ~this structure in 2018 (nickel-63 layered with diamond semiconductors)

I’ve honestly tried to find differences between their tech, but couldn’t even with the help of digital hallucinating monsters!

• Isotope: Both use nickel-63 as the radioactive source

• Semiconductor: Both use diamond as the semiconductor material12

• Thickness: Both identify 2 micrometers as the optimal thickness for nickel-63

What are the implications?

As the tech is not new, but it’s definitely a huge market and strategic advantage, we could infer something like:

• Conversion efficiency is a huge factor - it’s the main thing changing in this tech AFAIS

• Compatibility with current standards - e.g. an AA/CR2032 battery that never has to be recharged is good, but a working safe pacemaker, or a sensor, or a drone - that’s on a whole another level

• Sustainability - e.g. are the parts easily sourced/produced/recycled? Are the reagents easy to get or synthesize?

• Outreach and adoption - i.e. the ability to secure contracts, synergies (medtech, military, surveillance, corporate etc.)

It looks like China may have the upper hand in government-level large-scale adoption of emerging tech - and the skill of announcing loudly, as can be seen by DeepSeek’s, Unitree’s and now - Betavolt’s releases.

The resulting implications? I wouldn’t want to see a trade/strategic war, but the scientific cold one is both good (more scientific progress - likely better lives on average) and bad (snatching enablers, e.g. Taiwan/TSMC…)

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Eating a good life?

No-surprise-but-still-pretty-valuable research on dietary patterns that lead to a healthy life (preserving physical and mental health, cognition, protecting ourselves from chronic diseases).

No shit, Sherlock. Basically, the diet stems from this as follows:

• 🍔 Minimize processed foods, meat, margarine

• 🥓 Moderation with saturated fats

• 🍷 A sprinkle of wine doesn’t hurt, as I’ve covered a couple of years ago - new research confirms this (OR 0.85 for low volume drinkers)

• 🥗 Focus on whole grains, fruits and vegetables

• 🫐 Add some yogurt, legumes and berries (depending on your financial status)

Is (mediterranean) italian diet THAT good?

So, mediterranean diet is all the rage - the bestest, the longevitiest, the healthiest:

So, WTF’s with Italy?

Still, one of the centers, Italy, spends a lot on healthcare despite the all-awesome diet:

Per capita figures are different, but still Italy’s not the healthiest (sourced the population from a bullshitty AI machine, so it may be mistaken…). I’ve calculated (petty table here):

• per capita healthcare costs

• per capita GDP/per capita costs (→ i.e. if a country is more ‘wealthy’, will this translate to more costs? + healthcare costs a lot more in wealthier countries?)

Turns out, when we account for GDP, and calculate per capita costs - magically the wealthiest countries take the lead in ‘we spend a smaller portion of our GDP’:

And the final touch - correcting by healthcare costs index (used a super-dumb ‘divide and multiply by 100’ heuristic to correct the prices):

Doesn’t do much for Italy, though

When you compare mediterranean diet with contemporary dietary patterns in Italy, there are some differences IMO:

• ⬆️ salt/sodium

• ⬆️ meat (it’s savory and tasty, right?)

• ⬆️ processed wheat flour (as it’s cheaper)

• ⬇️ high-quality nuts, seeds, oils (as they’re more expensive than before)

Aand it shows!

And back to the prized mediterranean diet - it’s good, yeah, but having a stable economy and healthcare access helps, too. No diets and tight-knit families will save one from instability (+ shit life syndrome also takes its toll) and everyday financial stress because the country isn’t faring good:

Thoughts

Could some countries benefit from a policy change? What are possible n-th order effects? E.g.:

• Widespread use of GLP-1 agonists may influence families’ dietary patterns (and it’s only gonna rise in the next years)

• How will this influence investing and pharma trends? I.e. will platforms for faster/better development of GLP-1 (or other paradigm drugs, like DACRA or MC4R, melanocortin receptor, agonists + gene therapy/microbiome modulators) emerge and flourish? Will other paradigms emerge by the time I write a new post?

• Will mandatory or easier access to AI-assisted mental health care (which is already showing good results, below) improve the well-being in these countries?

(Bio)tech

Lmao, a new LLM dropped - for drug design and discovery

TxGemma family from DeepMind is fine-tuned from Gemma 2, and seems to be current SOTA.

What’s that?

The authors claim it helps with the following tasks:

Classification

Given a drug SMILES string, the model can:

• Predict the drug's toxicity.

• Predict whether the drug can cross the blood-brain barrier.

• Predict whether the drug is active against a certain protein e.g., a choline transporter.

• Predict whether the drug is a carcinogen.

Regression

• Given a drug SMILES string, predict the lipophilicity.

• Given a drug SMILES string and a cell line description, predict the drug sensitivity level.

• Given the target amino acid sequence and compound SMILES string, predict their binding affinity.

• Given a disease description and the amino acid sequence of a gene, predict their association.

Generation

• Given a product, the model can generate the reactant set.

Links links

Besides that, the folks had included examples of:

• fine-tuning to own dataset

• using in an agentic workflow (Agentic-Tx)

Download: 🤗 Hugging Face + ⏃ Vertex Models

End-to-end applications look fun, potentially reducing the development time significantly, with models such as this acting like a tool-using and slightly-reasoning glue layer.

Though(ts)

• Someone’s gonna fine-tune on Gemma 3/Qwen or a new-er Deepseek and maybe brag with a marginally better SOTA

• How would reasoning models fare here? Or they’re not that needed given a good agentic workflow (that’s food for thought)

• And after all - why hadn’t anyone make a tokenized agent structure framework, and why did I register for Berkeley hackathon…

(Not so bio)economy

This “data alpha”, as you call it - is it here?

Again, the same argument as seen before:

• Hey, there are so much VCs, and the datasets are all the same 🫠

• Everything is commoditized, the decision-making is so so doomed 🥺

• We’ll have to make (predictably) irrational decisions (i.e. challenging the status quo)

• The winners will be small, adaptable, nimble and specialized 🐁

Just like small mammals in the age of dinosaurs.

E.g. Level VC, who had posted their pipeline and data stack - good for us to have a peek. They’re using nocoDB and a sleught of in-house data ingesters (from file uploads to call notes, web crawlers and KOL posts) to intergate all the possible data into an agentic LLM workflow.

Some of the examples are…Portfolio monitoring:

Experts’ database:

Looks pretty rad to me, mates (ahoy)!

Bottom line (again Mr. Obvious here)

• Rise to the first level of Donella Meadows’ leverage points - change paradigms. This includes hackathons, synergies, income-based investing, university/drop-out stars’ nurturing etc.

• Smaller bets in a larger portfolio of companies may be beneficial, akin to dutching. However,

• The oldest adage always works:
  Do what you know good, be always aware of the unknowns. After all, we’re here to extract the slivers of certainty from chaos using our world models, aren’t we?

  

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Woah, we’re halfway there, wo-ah, choppin’ up fi-ber

That’s one nice upcycling approach by One Bio:

• made a plant fiber structure database

• thought out an expansion: cleaving those polysaccharide fibers into oligosaccharides using a mixture of catalyst (Fe³⁺) and hydrogen peroxide (patent)

• those fibers can be added in abundance to food, as they’re odorless and tasteless

Looks like a great start:

• can potentially be used to compose custom fibers for targeted microbiome modulation

• possibly collaborate with other alt-food companies like e.g. Solar Foods (will I ever shut up?) to produce a Matrix-style nutritious stuff (but tasty?)

Climate tech is…not climate tech?

An interesting strategic (we’ll face this sooner than later) piece on climate technology. Some uneducated thoughts:

• Investors and people alike are putting too much faith into the “climate tech” label, and the moral/social implications of it - not the competitive edge

• In reality, we should be happy solving the problem and building enablers/infrastructure for it - not an ‘anything related to’ but ‘anything that potentially helps us not fry/freeze/choke & drink coffee during the next 50 years’

• Oh and yeah - not using policies/tariff cuts for an advantage, but being competitive and sustainable

• There was a point about ‘climate tech’ overshadowing issues nonrelated to CO2 capture, e.g. biodiversity or even sturdier crops. Or, say, some agtech startups like Ohalo (their ‘boosted breeding’ tech is indeed interesting and able to impact the climate)

  

• Hence and because of the macro-VC-trend, some of the thoughts were on further specialization, e.g. ‘synthetic biology’ - it rings reasonable esp. given the recent post; but come on, anyone forbidding from having more assessors/advisors + identifying more synergies?

• I’ve wanted to retort on the ‘and Tesla is also green’ with the battery manufacturing trope, but got caught in a mythbusting article. Sooo it’s all ✅… Also, we’re having emerging tech like sodium-ion (sodium, not lithium), lithium-sulfur (higher energy density) and solid state (even higher density but scaling is complex) that may change the landscape further.

  

Inhale science, exhale startups (found a Deep Science Ventures review)

Trying to bridge science and startups to bring more deep tech impact to the world - what can be better? The peeps at DSV thought the same way - and made a PhD program catered to spew out sci-ups (a term I’ve just made up to describe startups growing out of research/papers).

So, Winnie Wambugu from Kenya settled on getting a World Food Foundation grant and being on the verge of becoming an agtech-preneur after unsuccessful job search (hint: similar to this video). Yet, she found DSV program and mentions that it helped tremendously. Let’s map the factors:

• doing both research and entrepreneurship in one pack

• networking with fellows and the companies/funds

• a personal brand

• synergies

For a firm like DSV, that thing is marvelously in line with “catch good and energetic teams the earliest + nurture them” → so it’s early scouting + possible investing at essentially zero valuation + positive impact/fulfillment + a growing network of funds, founders, etc.

Let’s not forget one of deep tech’s benefits - it’s not that prone to “our API wrapper NFT agent startup is experiencing tough times 🥺”, as it’s literally bringing the change into the world:

Noise and cojones

Remember me telling you to read this in silence? Las pelotas desperately crave silence, and shun noisy environments! (Or maybe not, shh..)

Here are the proofs:

Not only that, but noisy environment at home can contribute to type 2 diabetes (can be caused by shit life syndrome → you’re living in a noisy environment & you don’t have the means to rent in a better one)

Workplace noise exposure also contributes to testosterone decreases in 👷. Causal-correlational questions here, too:

Some fun life factor-testosterone level correlations

Higher education → ⬇️ T

A semi-explanatory theory may be higher testosterone, while fueling motivation and grit (broscience pubmed master here writing, lads), also increases impulsivity and decreases self-control potential → why should I even spend hours in the classroom/studying?

Underweight → ⬆️ T

• While obesity is usually connected to lower T (via fat cells enhancing aromatase activity etc.), we may make a far-fetched assumption that current weight scales are in for an edit?

• Or the other way round, as having high testosterone is making one leaner: could losing fat mass/being sporty (not the gym, rather dancing/hiking or physical activity at work) settle someone in the ‘slightly underweight’ category

Smoking → ⬆️ T

There’s a debate on whether this may be caused by nicotine, cotinine and other tobacco alkaloids inhibiting aromatase:

More convincing is that women who smoke also seem to have lower estrogen levels → but yeah, don’t consider smoking to become the testosterone-ridden Chad. Please.

Cadmium and lead levels → ⬆️ T (WTF?)

Weird as hell; may be problems with study design, or a hormetic response - e.g. no lead/cadmium-intoxicated, but quite a lot in the ‘a tad higher than the third cohort’ category. Would love to access the violin/candle plot at least…

Noise, lead and conclusions

Let’s add all that noise/T connection to the fact that we’re better off touching/seeing the grass and greenery... Hey, chad-rats were born with increased green/blue exposure in their parents! The usual chad-rat (actually myostatin-knocked out):

If so - would one be better off living in a green, quiet neighborhood with great views? Nah, no way…

And the biggest secret? Living in a good, rich country:

No fukn way. Yet the Happiness Report weirdly claims that, can you imagine?

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Just some additional thoughts in addition to a recent post:

• The cost of developing an MVP/PoC is declining rapidly (vibe coding much?); besides the hype and despite some IMO poisonous practices (how does ‘Start over, often’, ‘Don’t think about architecture’ sound?) advised, it’s changing the landscape with baby steps

  • in addition to the last link author’s thoughts, I’d add that some visual-code primitive frameworks are likely to emerge → i.e. an adaptive page/app that is generated in markdown-like blocks that are rendered on-the-fly. The bottleneck here is essentially tokens/s output, and that paradigm would supplant it.

  • Yeah, definitely testing and security/hacks. It could be a vibey TDD, e.g., a ‘define outcome → get tests → code is built around them’.

• By Jevon’s paradox (and as majority will adopt the ‘Start, prompt, re-do’ approach), we’d need:

  • more energy to support this (e.g. the bid on nuclear; but let’s not count hydrogen/alt energy out!), along with

  • more efficient means of computation (mentioned different emerging paradigms in a recent post)

  • efficient algorithms and architectures, and some safe haven of good code

  • efficient infrastructure (data transfer, storage, etc.), so - the usual ‘boring’ areas: apart from something that works, it has to be computed, stored, produced

• VCs are definitely going to reflect that - one most likely wouldn’t need 200K-1M to build a PoC soon. So,

  • 

    less funded software products/micro-products (i.e. one doesn’t need inflated valuations)

  • more deep tech/complex products → bigger average rounds

  • cycles getting shorter for even traditionally ‘slow’ fields like healthcare and material science (pushing *a-lot more iterations in the same time + faster testing/simulation/adoption processes)

  • outreach and events/networking becoming more important than funding for software

  • VCs specialising in e.g. scouting, GTM and scaling, network effects and collabs, advocacy/outreach/media + founders looking for a ‘portfolio’ of funds

  • smaller teams overall?

• Deep tech needs expertise + as I’ve mentioned before → more VC funds point to dwindling returns and the need to spot projects/teams early on.

  • So, even more hackathons/events to nurture and push the little starlets?

  • Prediction/consensus/fact-checking markets?

Bottom line

It’s all going to become more and more interesting, faster.

Palchinskii’s principles

(Even adopted at the US military! Wooo)

I can recount Marcus Guest giving a lecture at my university a long time ago, which was telling ‘the experiments should be safe to fail, and one should cross the river by feeling the stones’. So, Palchinski’s principles lay the foundation with iterative experimentation, maximizing risk:reward ratio, kaizen-style improvement. It also

Experiment with a variety of ideas

to increase the chances of success.

Accept that some failure is inevitable,

so keep projects small enough that failure is survivable.

Develop effective feedback loops

between decision-makers and those closest to the action to quickly identify and select what works in the local context.

Fun news

Minority report is scary? How about 17 months in jail over an AI mistake…

So, Chris Gatlin didn’t have a lucky day, or week, for sure; he was misidentified by a facial recognition algorithm and was in jail for 17 months before the court realized there was no hard evidence.

A slightly better news - someone’s won 81T in a bank error lottery 🤗

Instead of $280, a client was ‘sent’ $81T, which is 500+ times more than Citibank’s market cap ❣️

The fun thing is that the error was caught by a third employee checking, 90 minutes after the initial error. Raises some questions about the financial system and the banks’ risk management practices <3

All I can remember is PayPal erroneously sending a client $92 quadrillion in 2013…

Literally me

One thing - I’ll add own thoughts on where each area is moving (science/ventures) so we’ll be able to point fingers and laugh later!

Deepmind’s co-scientist

So, after Alphafold (protein folding prediction, one of the ‘unsolvable scientific mysteries’) and GNoME (discovering 2M+ materials, 300K+ of which are indeed worthy of future research), Deepmind has released an agentic system for scientific discovery. Contrary to the multitude of LLM-generated bullcrap that had predictably infested publishing houses, and even Stanford’s STORM (source code) that generates hypotheses and debates them.

Any results?

One of the fascinating cherry-picked examples is packing ~10 years of antimicrobial resistance research into 2 days of agent inference. The solutions were comparable or better than current SotA research on antimicrobial resistance.

Co-scientist is able to use Internet, but currently seems unable to automatically simulate/check the hypotheses. Still, this proves as an incredibly powerful tool - Deepmind’s at it again ❣️

Further developments (🧂)

Meaning “take this with a grain of salt”

Loosely based on this comment of mine:

1. Heuristic/metric automated ideation and development

2. Checking the hypotheses where possible, using the required tools (e.g. an exposed API to an affinity checker, etc.)

3. Generating the critic-generator-judge (later meta-judge, e.g. meta-rewarding LLMs) sub-agent structure

4. No, please no Auto-publishing after human verification

5. No, no, no, no Auto-reviewing other papers, e.g. LLMs-as-scientific-reviewers

And products on each and every stage.

Anyway, given the recent rise of LLMs-as-judges and other “let’s take this 0.5-12B param and finetune so it…” cases, we could IMO expect further productization of different LLM-based specialization areas. The old “do one thing and do it really well” adage applies here without any friction.

Graphs for scientific discovery

Launching in 3…2…1…Pew-pew-pew!

Recently I’ve stumbled upon awesome work by Prof. Marcus Buehler from MIT. Starting with fine-tuned models for materials science to more complex graph-based reasoning first. Being interested in graphs and neurosymbolic approaches (covered before), I’ve started diving like a ravenous whale.

LLM approach was checked out first…

So:

• a graph was created from 1000+ papers

• calculating node metrics

• devising optimal graph reasoning algorithms and techniques

• a lot of weird similarities were found between materials’ properties graphs and, e.g., Beethoven’s 9th Symphony

  

• using graph reasoning and principles from Kandinsky’s ‘Composition VII’ painting (that was weird, okay) to generate materials, incl. using DALL-E

Graph attention next!

Next frontier I loved was replacing the usual Transformer attention module with Graph Isomorphic Networks (paper, source code)

TLDR (explanation here, too)

• the usual attention is Q, K, V - Query, Key, Value; nicely explained by e.g. Ebrahim Pichka, or an awesome (!) visualization by Brendan Bycroft, and another one. Multi-head latent attention as the ‘trendiest’ below:

• 

  it’s replaced by an graph isomorphism (to tell the truth, it’s not the only lab that pursues GTs) network-like structure (i.e. are the graphs of the same shape, and what is the difference)

• • each token is viewed as a node, attention score is edge weight

    • adjacency matrix (graph representation) is computed, and aggregated to calculate the output

    • for multi-head attention, each head computes an individual GIN adjacency matrix which are then concatenated

    • GIN introduces a sharpening parameter α (⬆️ α leads to sharper attention, while ⬇️ α produces a smoother distribution) to dynamically shift ‘attention focus’

    • sparse graphs are used for greater expressivity and lower computational overhead (e.g. why put up a 0.000(…) weight why you can optimize)

Later on, it still seems like a multi-agent scientific reasoner (SciAgents), but using graphs as a world model:

We trained a graph-native reasoning AI, then let it think for days & discovered that it formed a dynamic relational world model on its own, no pre-programming. Emergent hubs, small-world properties, modularity, & scale-free structures arose naturally. The model then exploited compositional reasoning & uncovered uncoded properties from deep synthesis: Materials with memory, microbial repair, self-evolving systems.

Recent paper + breakdown

It outlines how the last version of the sci-discovery system works:

1. Question or topic for pondering (“Does Shrek think of Roman Empire?”)

2. Thinking in <thinking></thinking> tags a-la DeepSeek et al., but more akin to knowledge graph

3. Extracting new knowledge (i.e. local subgraphs to integrate into ‘world model’ graph, e.g. “wood + fire = coal + ash” → ADD {“wood IS material”, “wood COMBINES with fire”})

4. Merging with big global graph

5. Next prompt

6. Repeat → (1)

And there’s also a simple generator-critic style agent loop that incorporates graph enrichers/generators, too:

In the end…we’re getting a system that has an interpretable and explainable world model:

Yet I may be mistaken and propose that there should be a world graph consensus/verification mechanism or agent somewhere! An idea for a pet project… Another one…

Other approaches, e.g. JEPA

I’ve written about Yann LeCun’s JEPA architecture before. This approach seems to move forward, with causal understanding of, for example, physics laws:

As soon as I’ve pondered on combining JEPA learning with graph representations - someone’s already published a paper on it.

A graph representation is learned; but those are not knowledge graphs at the moment. Another thing - nothing enriches the graph using logic laws.

Other things that caught my attention

• Tokenized architecture - IMO any kind of parametric encoding/hot-swapping is the step in the right direction

• Google Titans as a new memory paradigm (surprise-based, usage-retaining, dynamically reorganizing → all the features of real memory)

  

• A generalized world model graph, or maybe specialized ones? WikiData seems OK for the start. Or Google’s.

• Definitely some bio-inspired computing, or bio-computing altogether. Cortical Labs, I’m looking at you! How long until this computer learns to play DoTa…

• Other emerging computing paradigms that may (!) lift off are:

  • reversible computing (i.e. internally recycling the heat/energy generated by calculations), spearheaded by Vaire. A pretty awesome write-up here

    

  • photonic chips and Lightmatter + recent MIT advances (“was able to complete the key computations for a machine-learning classification task in less than half a nanosecond while achieving more than 92 percent accuracy*”*). It’s a chip that uses photons instead of electrons for several improvements:

    

  • the long-hyped quantum computing (explanation here → uses quantum phenomena to process information faster, e.g. quantum entanglement to calculate several states at once), where lots are battling - starting with MS with their recent Majorana release, to e.g. Rigetti with a 9-qubit (at the moment) chip, D-Wave and Quantum Computing

    

The landscape is pretty, pretty dense even with a 2-yr old chart:

Future directions (🧂)

• Productized → on-premises → commodified “specific scientific discovery AIs” for one’s own purposes (i.e. one hooks it up to own simulation/heuristic systems to check hypotheses, working in collaboration with scientists, or at some point alone).

  • Market: CAGR at a whopping 53.7%

  • Possible strategy: backing companies leveraging the advances / backing the ‘boring’ enablers (APIs/products/security, etc.)

• Goal/architecture-encoded “scientific AI” akin to TART, so it’s more generalized and adaptable

• Fast-faster chips (one of the paradigms will blow up for sure), and IMO more specialized chips (e.g. programmable-encodable FPGAs for the particular task at hand, it seems) akin to Lisp machines of the past.

  • The market is so big it’s indecent to mention - at least 1T by 2030 (will actually be bigger if we consider the emerging chip paradigm race)

  • Possible strategy - backing several early-to-mid stage paradigms

• Infrastructure/enablers, i.e. storage, data transfer, orchestration, energy storage and production, automation… You name it.

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The world is changing. The fun thing - the pace of change, d’’ or second derivative, is increasing. After making the VUCA → BANI transfer, it’s become even harder to make sense of the things happening.

The changes are everywhere: tech, healthcare, society, media… Not the least is investing world, e.g. venture capital and private equity.

Notes

Higher competition (!)

There are now 32,000+ venture funds out there:

Gone are the times when being a VC meant the world. It means - but it’s now a bigger ocean with bigger/smaller sharks (NFX gives 14 reasons to love the nuclear power on why VC market will continue to grow). And, IMO, the market’s Tinderized a lot - everyone wants that fresh-out-of-MIT ML PhD grad with a nerdy but genius team that are set to change the world.

For VCs themselves, Matthew effect and power law are in place, too - majority of capital is raised by the minority of firms

Brighter side of the moon

On the greener side, quoting the same NFX post:

• LPs will continue to invest in LPs because

  • it’s just one of the strategies

  • it’s more than viable

  • brands are durable/defensible, i.e. a strong VC brand will be able to invest in a number of models efficiently

• It’s still a pretty inefficient and dare I say contrary to aforementioned claim - undersaturated market

  

• It’s still small: appr. $300B in 2024 vs $4T for real estate and $4.9T for hedge funds.

• It’s lucrative:

  

• It’s becoming ubiqitous and faster:

  

Overall, as VC is much (!) more variable than most other asset classes (a virtually unlimited number of opportunities - all the things progress/innovation at the very least), hence inefficiencies/’alpha’ can be expected to exist for longer, possibly indefinitely:

So there are two main ways to surf here (later we’ll cover them more):

• find the opportunities before anyone else (preferably when the founder is still in the crib)

• be the C everyone wants 😏

  • build robust and defensible systems/flywheels by mixing cooperation and competition

  • establish the brand/voice

  • have the ultimate CxO/team enrichment/networking opportunities

Faster cycles and iterations

In line with d’’ rising, startup wax and wane cycles are shortening: it’s mostly a good thing (faster turnover + more startups):

Again, with the rise of AI MVPing/iterating speed is going (and does already) to add a few notches further, hence faster everything.

Oh, my boogers.

Hyperfocus vs generalists

It had become harder to hustle in the world of increasing product complexity. Sure, one could continue making micro-SaaS (low-maintenance fast-to-start projects), but to really change the world, you’d usually need something substantial. Or eat the startups for lunch like Rocketable does.

Focus on leaders

As it’s been reasonably mentioned in another NFX’s post - founder’ and the team’s attitude and ‘grit’ matter big time: it’s a subtle dichotomy as one should differentially diagnose the ‘we’re gritty but adaptable around our mission to _’ from ‘we’ll pursue the same idea no matter what, banging the walls’.

More CVC activity

Venture arms of big corps seem to be gaining ground: there are a lot of advantages to being invested by, say, Qualcomm’s own VC fund if you’re making chips. Risks of bankruptcy are cut in half (yet are still high), multipliers are higher etc.

From 2014 to 2024, CVC has made up more than 46% of total VC deal value and 21% of deal count. However, despite having invested a vast amount of capital, very little of this investment has translated to acquisitions.

To put it into perspective, their data shows that since 2000, below 4% of CVC-backed companies were acquired by an existing CVC investor.

Why are acquisitions so low?

• You’d want to keep a good team nearby

• It’s your child startup, not someone else’s

• The last but not the least: it was never an option

  

Conclusions

So, let’s unwrap the burrito of each of the IMO ways to stay ahead of the curve. Yeah, I just love the’s.

Being a known voice in the industry

Got anything to analyze and show? Sure, why not.

Got access to non-NDA but valuable piece of information? Use it + share it.

Events? Be the speaker, organize or at least participate.

The truth is universal:

Nurturing from the start

• Well-known formats: accelerators, venture studios, etc

• (Universities take the cake here) Endowment and university grant/investment funds

• In-person events to catch young talent - it could be a TEDx-style talk with students and researchers, or a hackathon 😏

• Things like income share agreements seem tasty, but one is unlikely to catch a

Capturing the whole flywheel

Network effects are always underrated, as those are compounding like crazy.

Some of those are:

• having your portfolio span over the whole value chain (a good way to map is Wardley map) and cooperate:

  

• cooperate over geography, too!

• create cliques: intra- and inter- (your other funds/arms, or friendly funds) cooperation can be incentivized with lower prices/fees and such

To reiterate: one of the important factors in building defensible systems/flywheels in international adaptability and cooperation: if a business can cooperate with portfolio/cooperating VCs’ port companies all over the world, it can create a strong ecosystem of products that will have more chances to compete with the biggies.

Basically, we all want to prevail over the Matthew effect, and decentralized cooperation can definitely be one of the tools.

Is there an end to win-’s?

One more factor that can, IMO, help in achieving the overall positive impact is accumulating incentives-provoking network effects. Sounds wordy, and it is - a better way is an example:

Sprinkling the portfolio companies with social projects, disaster response, at-risk group cooperation:

• Helping scientists at risk with outreach or consulting gigs? Check

• Assisting emergency services after an earthquake with transportation/energy supply/alt. food rations? Double check

• Volunteering or opening days for children/at-risk social groups, so they'll get a taste of good-cause startups (if those are in your theses/portfolios)

All that can and will usually be coupled with the additional egotistical goal of press outreach. Combining companies / employees / causes multiplies the impact. Biggies like Tesla, Abbott and Mike Tyson (call him a kiddo or a smallboy at own risk) have already been doing this!

A strong portfolio of opportunities

When you’re a VC, your money isn’t the only/main thing good (informed) founders are looking for. Among other things:

• aforementioned brand, as a good brand will attract follow-on investors + is a good way to get press releases/coverage for free 😏

• networking opportunities to help with collaborations/regulations/legal peculiarities/go-to-market etc

• CxO or team support, either with engineers or marketers or operations staff or whatever - helping run things smoothly so core team focuses on product and strategy

• mentorship/strategic assistance → there’s no mentor as good as an early stage investor yearning for your success 😅

Data-driven decisions

Spotting the best-performers from the start can help one take the tastiest cake pieces.

For that, a lot of data points have to be combined in a data model, and evaluated. The hypiest piece that comes to mind is AI/data-driven startup scouting. Some examples:

• StartUs Insights

• Specter

• Harmonic

• Kaiku

• Signal analytics

• Some awesome data/VC friends had published a paper outlining a great model + the product is underway 🫶

• A lot of other tools…

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• polygenic

• extremely diverse in regard to manifested traits and their power

• comorbid with a lot of stuff

• can both enhance chances for life success, or be a serious disability (75-90% of autistic individuals are unemployed in different countries, one of the highest rates)
  P.S. It’s important to disclose the diagnosis to potential employer - it increases the employment odds by 200%

In an ocean of facts, one shouldn’t drown as odds ratio to do so if you have autism is 160x… But first I’ll drop the facts, and then try connecting them into graph-like pathways.

Influencing factors

Genes and comorbidity

First of all, let’s uncover the ‘polygenic’ and ‘comorbid’ claims.

A recent study had uncovered genetic variants that influence the development of several disorders (eight to be accurate (8.0 to be even more accurate): autism, ADHD, schizophrenia, bipolar disorder, major depressive disorder, Tourette syndrome, obsessive-compulsive disorder, and anorexia. These variants are called pleiotropic - ‘affecting many’, i.e. a gene affects the development of several disorders.

Plus previous study identified 109 pleiotropic variants and classified them by disorder pairs, of them the most prevalent:

So, it’s no surprise adding to comorbidity of ADHD (50-70%), and depression (up to 50%, but likely more as not all depressed autistics are diagnosed), psychosis is more prevalent in individuals with autism:

Wiki holds a nice Euler diagram for more genetic overlaps:

Even if we just take the first five, all seem pretty low-level (we’re talking ion channel/synaptic

• CACNA1A encodes the alpha-1A subunit of P/Q-type voltage-gated calcium channels, critical for neurotransmitter release, neuronal signaling, and synaptic plasticity;

• GRIN2A encodes a subunit of NMDA receptors involved in synaptic signaling, learning, and memory;

• KCNQ2 encodes a potassium channel subunit that regulates neuronal excitability and action potential firing;

• SCN1A encodes the NaV1.1 sodium channel subunit essential for neural signaling and neurotransmitter release (causes Dravet syndrome, epi-aut-schizo disorder; the whole fun nine yards!);

• SYNGAP1 encodes SynGAP protein, a regulator of synaptic plasticity and brain development, critical for cognition and proper synapse function. Defects cause autistic-like behaviour.

Brain

First, cortical thickness - mightly frontal cortex for autists is here, along with up to 17% lower whole-brain synaptic density (particularly pronounced differences in regions known to be important for social and cognitive functions, such as the parietal cortex):

Also, the whole brain is hyperconnected in accord with MYT1L and pruning theory:

Now a bit deeper, onwards to social reward and reward connectivity…

Oxytocin, more brain and female autism

A fun study had explored oxytocin receptor ‘risk alleles’ and autistic phenotyping in males and females, with/without ASD (Pplxty):

• for males, ⬆️ OXTR risk alleles:

  • ⬇️ within-network connectivity

  • ⬇️ nucleus accumbens ⇔ prefrontal cortex connectivity

• whereas for ASD/NT females it was more alike to NT males, ⬆️ OXTR risk:

  • ⬆️ nucleus accumbens ⇔ prefrontal cortex connectivity (possibly as a compensatory mechanism)

  • stronger nAcc ⇔ PFC and reward connectivity leads to higher social functioning scores

  • ⬆️ NAcc ⇔ subcortical regions related to motor learning and sensory processing → ⬆️ scores for restricted interests and repetitive behaviours

Another older study confirms that decreased mesolimbic pathway density is causal to social deficits experienced by autistic ladies and gents:

It is said to partly confirm the social motivation theory of autism, where social interactions are less rewarding, leading to a vicious cycle (it could also partly explain the ‘cut the small talk/nonsense’ attitude expressed a lot in ASD → why engage in non-rewarding stuff wasting my time if we could just exchange the information and be done?)

Another new paper only confirmed this (higher autistic traits → lower social motivation) connection for females, though:

Yet autistic girls have expressed increased sensitivity for social rewards compared to boys:

Bear with me for a minute - so girls may still be less apt socially but are rewarded ~normally. Sounds like a difficulty diagnosing and a whole world of pain for them, isn’t it? Also, normal social connectivity despite OXTR deficiencies may partly be confirming the female protective model.

Interestingly, behavioral research suggests that high functioning females with ASD show greater social motivation compared to their male counterparts^59,60,61, a finding which has been attributed to the ability of females with higher IQ to mask their social-communicative deficits^60,62

There’s a great lecture by Sarah Hendrickx that further studies the sex differences in autistic phenotypes, summarized by the great

Basically, we can roughly conclude that:

• females with ASD may or may not exhibit social deficits on male level (likely it depends on a plethora of factors, not just OXTR and connectivity)

• but still, they seem to have normally functioning social reward circuits

• but definitely higher repetitive behaviour, learning alterations and restricted interests

• all this stands to reason that females need to be a lot more autistic than males to get properly diagnosed, and often have to learn to mask properly on the go. Despite that, some studies tell that ~90% of ASD females had been victims of sexual assault - so much likely when you appear ‘normal’, don’t have healthy boundaries and don’t (yet) know the dangers. Adding to that - masking is often recognized as flirting

  

• females have more desire to be socially acceptable, leading to more masking, leading to even more difficulties with diagnosis!

There’s a strong need for separate diagnostic scales, IMO. Some started to cater for that with e.g. checklists.

This may explain synaesthesia, sensory and word associations but difficulties with some complex domains requiring high density (vicious cycle of low density/low reward → no incentive to improve and increase density): social functioning, motor skills, diverse interests (requiring high intra-cluster density).

Inflammation and kynurenine, again

Along with kynurenine pathway:

ASD patients reported significantly lower TRP levels than BAP and CTL groups. Moreover, significantly lower levels of KYNA were reported in both ASD and BAP groups than in CTLs.

Reminiscing that kynurenic acid is an all-around anti-excitability and anti-inflammatory compound, we could hypothesize that inflammation can also be increased in autistic individuals (because kynurenic idea isn’t always confirmed by the research). Altered KYN/TRP (competing kynurenine - tryptophan pathways) and subsequent hyperserotonemia (serotonin in blood) are called one of the main predictors of ASD.

ASD individuals have significantly higher 5-HTP (serotonin) levels in blood, higher kynurenine/tryptophan ratio (meaning tryptophan → serotonin pathway is working over hours):

3-5× 3-HAA, tryptophan oxidation metabolite (also radical scavenger, so it’s not THAT bad, peeps):

…And a lot more inflammatory IL-22, that plays a key role in, for example, intestinal barrier function:

Amongst that, early-life inflammation has been shown to trigger neurodevelopmental and psychiatric disorders, so that definitely stands to reason. And if we count in altered blood-brain barrier function, higher neuroinflammation will become a piece of cake. We’ll return to gut and BBB later, though!

Gut and immune system

Remember the IL-22? Me, too. Also remember IL-17a, as higher levels in mothers perpetuate the child’s autistic phenotype development.

Adding to that, autistic children have higher brain levels of the following inflammatory cytokines:

• TNF (tumour necrosis factor)

• interferon (IFN)

• IL-1b

• IL-6

• IL-8

• IL-12p4

The implications? Broad as hell. So, let’s focus - we have here higher intestinal permeability, higher IL-22 in blood and higher everything in the brain. Also, microbiome is altered:

• ⬆️ Acetate

• ⬆️ Propionate (can inhibit NADH formation, behaving like a neurotoxin)

• ⬇️ Butyrate (could potentially restore the deficiencies likely by enhancing BBB permeability)

• 

  Significantly ⬇️ SGLT1 (glucose uptake in the intestine) and ⬇️ GLUT2 (glucose transport)

• ⬆️ Firmicutes and ⬆️ Proteobacteria, and ⬆️ Bacteroidetes

• ⬆️ Clostridium

• Levels of three enzymes responsible for breaking down sugars — sucrose-isomaltase, maltase-glucoamylase and lactase — ⬇️ in the ilea, or terminal small intestines, of children with autism

• ⬆️ C. Albicans, ⬆️ S. Cerevisiae, but ⬇️ A. Versicolor

An impaired gut barrier can:

• increase the levels of gut microbial components (e.g., lipopolysaccharide (LPS)) in the blood;

• trigger the hypothalamic–pituitary–adrenal (HPA) axis;

• stimulate immune responses, producing cytokines such as interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and IL-4.

These immune cytokines can circulate and cross the blood–brain barrier (BBB), inducing systemic and CNS inflammation. The serum level of LPS was found to be significantly increased in ASD individuals compared to healthy controls.

Altered microbiome naturally leads to alterations in SCFA levels (aforementioned propionate, acetate, butyrate and ⬆️ valeric acid) which contribute a lot:

Sugar mhhm

Another fun thing is reactive hypoglycemia:

• you have worse glucose uptake and transportation, along with lower enzymes for breaking down sugar → glucose/fructose will enter a lot later

• yet insulin response is initiated via sweet taste!

  

The whole situation is akin to responding with full force insulin to something semi-sweetener-like:

So, not good.

Endpoints

Biopsychosocioexowheel spins, turn 1

So, let’s summarize:

• Autism is multi-faceted, with important factors spanning:

  • parents’ genetics and epigenetics

  • mother’s substance use (also remember valproic acid potentially causing ASD) and inflammatory status during pregnancy + exposure to environmental factors

    

  • altered brain connectivity, immune status, inflammation, mitochondrial function, sugar metabolism etc.

• • post-natal development and early childhood can become triggers for worsening, that’s for sure

Biopsychosocioexowheel spins, turn 2

This can lead to:

• altered synaptic function and density → brain regions’ development

• altered BBB and gut permeability

• increased inflammatory levels in gut, blood and brain

• increased oxidative burden all over the body

• altered sugar metabolism and energy production pathways

All this further contributes to abnormal bacterial balance in the gut → altered SCFA levels in body and brain → abnormal HDAC inhibition, mitochondrial function, inflammation, mental health etc.

Biopsychosocioexowheel spins, turn 3

Altered synaptic function potentiates the vicious cycle of motor awkwardness, social reward deficiency, being aloof → being OK with restricted interests (if you’re bullied and alone, and don’t feel like hanging out is fun - why not pursue your worlds?) → bullying/ostracism and comorbid conditions like anxiety, depression, bipolar that are just waiting to be triggered (remember pleiotropic genes?)

Another graph analysis uncovers a lot about things autistic individuals usually experience in life, and the endpoints that can lead to. It’s an unsettling image, to say the truth:

I won’t burrow into the social implications or policy changes that could IMO help (policy/incentivization will likely become the topic of some of the next posts, anyway), as that’s beyond the scope of this post.

And the final disclaimer:

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Oops, that’s me, prehistoric coughs

So I’ve lately read about a benchmark of FireDucks - pandas-like lazy blazingly fast dataframe library (yeah, all the buzzwords are there).

Initial reviews are pretty good, claiming it’s really indecently fast:

This ran in 1 second (with ._evaluate added) on the 55 million row dataset. The classic pandas version took 8 minutes!

However, this GIF had sparked a lot of controversy, even despite Avi Chawla doing a comprehensive overview.

The guys at FD seem to have made a comparison with DuckDB and Polars, too + a dashboard of db-benchmark:

Dundundun DAFTMAN

I’ve recently read about another dataframe library, Daft - and decided to add it using the Colab provided; given it’s rather simple to do for a single comparison.

Behold, results from an infinitetibugged colab:

Or, mean time for DULocation and PULocation, respectively:

So:

• Yeah, Fireducks seems the fastest - it’s also not open-source:

By providing the beta version of FireDucks free of charge and enabling data scientists to actually use it, NEC will work to improve its functionality while verifying its effectiveness, with the aim of commercializing it within FY2024.
https://www.nec.com/en/press/202310/global_20231019_01.html

• I’ve been interested by Daft’s promise and focus on providing almost all possible interfaces, data types and engines

So I’ll try doing things with Daft in the nearest future!

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This year, the UK became the only country in Europe to approve the use of lab-grown meat in pet food.

According to estimates from the European Environment Agency, lab-based meat cultivation uses 45% less energy than traditional beef farming; if powered by renewable energy, research suggests a 92% reduction in overall greenhouse gas emissions, plus water and land use reductions of 78% and 95% respectively.

And a couple of infographic pics showing energy, land and water savings, as well as emission reductions that’ll result from switching to alt-meat:

And yeah - the industry is not only ineffective itself; the majority of e.g. soy production goes to supporting the farming industry:

All this marks the importance:

• switching to alt-food → not only for alleviating suffering, but the bare pragmatic reasons

• considering second-order effects → e.g. how soy production is skewed, or how transportation/biotech/VC industry have to cater to all that

  • another concern is accelerated pace of panresistant bacteria emerging

The cute kitten they’ve saved in a TikTok video 🥹

Fock, again Goodhart’s law and unintended consequences: a lot of animal rescue videos are actually fake.

1. More than 1000 links showing fake rescue content were collected over six weeks from Facebook, Instagram, YouTube, TikTok, and Twitter/X.

2. Almost 52% of links were found on Facebook, Instagram, YouTube and TikTok each feature around a quarter of the content.

3. The links located by SMACC had been watched 572,013,959 times.

4. The most common Fake Rescue themes identified were:

   • “Found to be abandoned” (30.6%)

   • “Rescued from being trapped/stuck” 28.1%

   • “Rescued from being attacked by animal/s” 15.7%

5. Cats were the most numerous animal featured (42%), followed by primates, dogs, snakes and turtle species.

6. 21% of fake rescue creators asked viewers for donations under the disguise of helping animals, usually by Paypal links.

Links

Top 15 foodtech deals in 2024

Is it even foodtech? O-okay, it is:

• The definition of foodtech applied above includes the AgFunder categories Innovative Food (which includes novel ingredients and alt proteins); eGrocery (online stores and marketplaces); Instore Retail & Restaurant Tech; Online Restaurants & Marketplaces (meal kits etc); Cloud Retail Infrastructure (ghost kitchens, last-mile delivery robots and services; and Home & Cooking Tech (smart kitchen appliances etc).

• eGrocery startups

• Nutrition recommender systems

• Restaurant tech (menu optimization etc)

Mostly interested in the deals tackling alt-food space, so:

• 🇺🇸 Perfect Day’s $90M (dairy proteins identical to cow’s milk)

• 🇩🇪 Formo’s $61M Series B (precision fermentation → expressing microbes that make dairy proteins → dairy products like cheese)

• Even 🇫🇮 Solar Food’s EUR 8M deserves attention (yeah I’m biased as I love the idea of knallgas and other chemotrophs; Aerbio/Deep Branch’s Proton is another example)

Random links

Worldwide old-time photos

A treasure trove! Something similar - criticalpast.com, an archive of old videos (one can find both wartime footage and LSD monkey experiments there).

5 free rotating proxies

from one of the leaders in the space, OxyLabs.

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What drives progress?

I’ve always been plagued with the “how do I create more good?” idea, for the good or for the bad. And I’ve been thinking about it, too (some say thinking too much is the bane of one’s existence…), stumbling upon 80000 Hours some time ago. Their ideas, while awesome, didn’t quite click (say, 80% click vs. 100%) - so I continued to think some more…

One question I’ve been asking myself - what is driving the progress?

Oof

Obviously, it’s curiosity and the desire of knowledge, yeah - but now, are those the only factors?

I’d also say current political situation, business and science also have a go.

Deeper oof

Let’s dive somewhat deeper (DELVE DELVE DELVE I’M NOT AN AI) - politics and science are influencing progress and innovation, yeah.

But aren’t politics driven partly by big corporations and other stakeholders, and science - by the same ones, in the form of grants, endowments etc? Let’s add capital to the chart:

So deep I can’t breathe

Yet, to receive lobbying contribution, raise a VC round or receive a grant you’ve got to:

• be in high-profile politics

• be a serial entrepreneur, or a top university graduate/PhD

  

• enter a good/great laboratory with a PI that gets the grants (imagine a growling PI that jumps on top of others to snatch the grant approval paper)

  

(Had to change D2’s layout engine to not make a mess…)

How can we foster and promote innovation?

So, quality education increases the expected value of science, business, capital etc.:

How can we make education:

• better

• more targeted

at driving humanity forward?

IMO, some possible intervention directions come to mind:

• selection based on natural traits, i.e. talent identification → low-nepotism progress in universities and PhD programs

• project-based learning starting with school, continuing further

• science that’s more open (less publisher domination) and more decentralized (P2P reviews?)

  

• showing potential problem areas to direct curiosity (without aiming at particular problems, yet researching them - we don’t want to instill einstellung effect into anyone)

Incentivize me, daddy

For that to happen, policies have to change. For policies to change, right incentives have to be imbibed into the system: you’d want to incentivize the ones designing and approving policies first, for the n-th order effects to unfold through the layers like mighty ducklings making waves (remember, this is HUGE):

Which leaves us with something akin to a flywheel where we fine-tune incentives for policymakers → educators → businessmen and scientists:

Fine-tuning can have different goals:

• optimal policy adoption → e.g. changing policies will hinder both their efficiency and the time to collect data for benefit evaluation

• net benefit optimization → the end goal. Someone should definitely read more on public policy analysis…

  

How can all that be measured?

So, we need effective policies to create this flywheel, but how do we know if they are working? How do we measure their impact and optimize them?

I have no idea, and I feel like a low-parameter LLM regurgitating smart words.

Maybe (a high-level uneducated assumption) a bandit-like framework can help, where:

• several hypotheses are tested

• benefit effect and intervention efficiency is assessed (e.g. causal inference with counterfactuals)

• optimizing and optimizing again

P.S. Yeah, this n-th order effect thinking was partly the reason for my blog naming: we, as virtual higher-order policymakers (incentive is a particular case/factor of a policy), want to create teleogenic frameworks so that incentive optimization process would be self-starting.

P.P.S. And yeah, we should definitely check for Goodhart’s law creeping in - complex system optimization, akin to overfitting, paves a way to unintended consequences:

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The earlier the stage, the more influence the founder has (following is my IMO assessment):

However…

As NFX are concisely putting it (backed by their experience & everything which I love):

• One has to be compelling and show grit (the ability to “roll with the blows”)

  🤩

  When most VCs find someone compelling, they mean: I have to invest in this person – it’s a hard feeling to describe, but we know it when we see and feel it.

• The dreaded and ached for ‘the founder has to do it’ (basically a suite of OCD/bipolar tendencies, but…). Not a ‘founder-market fit’, which is more of a skill/network/state match, but a need to solve problem X

• The founder is obsessed with the problem and cares about it. Be it a biotech solving cancer research (to cure a relative) or an ADHD-friendly task planner etc.

This is actually your first step towards building a defensible company. If you care more than others, you’ll beat them.

• Being a “quarterback”, e.g. skilled and seeing the big picture and leading and that ‘ugh’ feel. May that boil down to the feeling of “okay, this one will move with or without us, and we’d better fuel them up to get on the train”.

P.S. IMO the big secret behind the last intangible is being able to tell it from Adderall/Ritalin manic semi-euphoria 😌

KPMG Venture Pulse 2024

TLDR: Everything’s coming up, roses 🌹

Global outlook

• ⬆️ $108.6B in 7022 deals, 80% top deals in the US → avg. check size in US at $26M

• AI AI AI AI is at $32B of total, god bless uncle Sam + 37% of the VC funding and 17% of the deal activity was to AI startups (from Citi Agentic AI Report)

  

• A record year for Defense Tech

• Average deal-wise, US is too far in the skies at $26M with Europe at $8.4M and Asia at $6.5M:

  

• Top deals:

  

Upcoming trends

• More funding and acquisitions by CVC funds

• AI-enabled verticals picking up pace (robotics, pharma, biotech, miltech, health tech, cybersec, spacetech & space services)

• Agentic AI. Agents, able to act autonomously at levels 4 or 5 of automation, are the main hope blablabla I’ve written this myself - take that, binary steel killers

  

Also remembering the other post, Going off the schedule, we can weed (hehehe, hohoho) out more sectors from deep tech, aforequoting (I just love making up words):

1. AI weather prediction, e.g. DeepMind’s NeuralGCM and GenCast

2. Ultra-deep drilling (or not-so-deep, e.g. Borobotics’ Grabowski for installing heat pumps) → mostly for geothermal energy

3. Advanced nuclear propulsion

4. Biocomputing, e.g. neuromorphic chips and wetware computing like FinalSpark

5. Brain manipulation tech. Transformers had just been used to predict the next 5 seconds (!!!) of brainwave patterns, so…

6. Quantum-optimized portfolios (look at QAQA and Qiskit-based solutions)

P.S. IMO some tech that’s gotta help us evade the dead internet is on the way to receive investor attention:

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