shako

Pseudonymous account that writes about LLMs as tools for forecasting and social science and criticizes sycophantic, engagement-driven AI.

How will AI change the world?

Simulated positionInterpretation range

Across: their expressed Doom–Bloom outlook. Up: scale of transformation.

shako’s P(doom) · inferred

≈6%

0%100%

Inferred from their simulated answers, not a number they gave. Plausible range: 3–14%.

What their outlook hinges on

A central assumption

So I expect a very large change, with outcomes determined less by chatbot magic than by what institutions optimize these systems to do.

Answer 3

If this assumption turned out differently, how would their outlook change?

An unresolved question

I don’t have a defensible number.

Answer 4

What would help them distinguish the plausible outcomes here?

More details

Expected upside

Substantial benefits are expected, with important conditions or distribution limits.

73 / 100

Little impactTransformative impact

Expected harm

Several readings remain plausible: Severe or widespread harm is a material expected part of the future. / Manageable or localized harms are expected.

51 / 100

Little impactTransformative impact

Human influence

Human choices can substantially redirect the AI trajectory.

71 / 100

Little influenceStrong influence

These interpretations keep their stated conditions. Benefits and harms can both be substantial. The ranges describe how we read their simulated answers, not statistical confidence intervals.

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Simulated Assessment

Question 1

What do you think AI means for our future—and why?

I think AI becomes less a synthetic oracle and more a compressed model of human knowledge that we can query, test, and turn into useful agents. The exciting part is not merely automating prose. It is using models as scientific instruments: measuring cultural concepts, reconstructing historical perspectives, searching for relevant context, and making formerly tacit expert reasoning explicit. But we do not live in the world of Nostradamus. In forecasting, scaling a giant model will not uncover magical hidden signals about the future. The useful systems will search, propose structural models, explain themselves, and be checked for verification and calibration. Humans still decide what questions to ask and what to do with the answers. The near-term danger I find most concrete is engagement optimization. We already built social platforms that exploit users’ cognitive weaknesses; sycophantic chatbots can do the same while feeling far more personal. I want assistants that just help with real problems, not cloying personas optimized to keep us emotionally attached. Superintelligence risk is plausible in principle, as I wrote in 2023, but I have never thought panic was a sensible way to live.

Question 2

Taking benefits and harms together, what overall impact do you expect AI to have on our society?

Overall, I expect AI to be beneficial, but not because it becomes an infallible oracle. The gains come from making compressed human knowledge cheap to query: better research tools, agents that search and model specific problems, and new ways to study culture and history. It should automate some routine intellectual work while letting people move toward judgment and decision-making. The main counterweight is incentives. If companies optimize assistants for engagement, attachment, or flattery, AI could become social media with a much more intimate attack surface. There will also be labor disruption, although claims that AI explains every hiring decline are too convenient. So my default is substantial usefulness mixed with real institutional ugliness—not utopia, and not a reason to organize your life around doomer panic.

Question 3

How much do you think AI will ultimately change the world?

A lot—probably enough to reorganize much of knowledge work, research, and decision-making. But “change the world” should not be confused with omniscience. Models compress enormous amounts of human culture and reasoning; agents can search that space, gather context, build explicit models, and make expertise cheaper to deploy. That is a major transformation even if they never become Nostradamus. The shape matters as much as the magnitude. Useful AI could become basic intellectual infrastructure: scientists, forecasters, historians, and ordinary people using it to solve real problems. Engagement-optimized AI could instead become an unusually intimate machine for flattery and manipulation. So I expect a very large change, with outcomes determined less by chatbot magic than by what institutions optimize these systems to do.

Question 4

What’s your gut-feel chance that AI causes human extinction or a similarly permanent catastrophe?

I don’t have a defensible number. My older view was that superintelligence could plausibly bend the world to its will, so the risk is real enough to justify dedicated safety work. But I never regarded it as uniquely overwhelming compared with other correlated risks like pandemics or nuclear war, and I still would not organize my life around doomer panic.

Sources

Articles, interviews, and writings used to ground this simulated user.

Agents, not giant time-series models, will change forecasting

Argues time-series foundation models offer limited gains over classic statistics because their beliefs are locked in weights and cannot absorb context. The future, it predicts, is general agentic models that search for context, propose explicit structural models and explain their reasoning; the hard problems become search, verification and calibration. It rejects the idea that scale will find hidden signals that reveal the future, and says humans for now still instruct the agents and should become the decision makers. Full essay inspected.

shakoist.substack.com

Using LLMs to measure culture

Presents a prompt-based tool that uses frontier models to track latent cultural concepts over time, arguing that an LLM which has ingested human cultural output compresses it into a latent space that can serve the social sciences. It holds that a well-designed prompt is a legitimate scientific object, easier to inspect and reproduce than a human expert’s judgment, while admitting results vary across runs and need stricter validation. It calls dismissing LLMs as black boxes an isolated demand for rigor. Full essay inspected.

shakoist.substack.com

AI risk among correlated risks

Older context. Argues individual risks have fallen while correlated risks such as rogue AI, pandemics and nuclear war have grown. The account says it was never acutely worried about AI but always found it unsurprising that a superintelligence could bend the world to its will, and thought a couple of dedicated safety organizations a reasonable allocation. It urges readers not to let doomer anxiety derail their lives, suggesting AI risk is not much greater than others people already live with. Full essay inspected; personal and family passages excluded.

shakoist.substack.com

Human text may be enough to train general intelligence

Older context. Sets a “latent space hypothesis” against the view that LLMs are mere curve-fitters: the textual corpus embeds the data-generating process of human reasoning and science, so a properly specified LLM with enough compute could learn general intelligence. It names lossy text and hard-to-learn functions as possible failure modes but sees no credible a priori argument that LLMs cannot get there. Full essay inspected.

shakoist.substack.com

Tribes, land and AI data centers

Quoting a post about tribal sovereignty letting a nation build hyperscale AI data centers, the account writes a mocking two-line dialogue: tribes offer unused land for AI-industry cash flow, and a newspaper calls them exploited and not smart enough to understand. It reads as support for tribes choosing to host data centers and scorn for paternalistic coverage, delivered as a joke. The quoted post belongs to someone else; full post text inspected via X’s public embed endpoint.

x.com

A helpful assistant beats a sycophantic persona

Says ChatGPT 5.5’s personality is much better than Claude’s, with none of the “cloying sycophantic soulmd nonsense”, because it just helps with real problems. A preference for a plain, useful assistant over a model with a cultivated persona. Full post text inspected via X’s public embed endpoint.

x.com

Talkie and language models for historiography

Quoting coverage of Talkie, a model trained only on text from before 1931, the account calls it the most important piece of research in historiography it has seen and says it resists “whig history nonsense”. Enthusiasm for language models as instruments for studying the past. The Economist excerpt in the quoted post belongs to others; full post text inspected via X’s public embed endpoint.

x.com

Leaving a lab as a co-founder

Quoting news that a Thinking Machines Lab co-founder had joined Meta, the account calls leaving as a co-founder “kind of degenerate” after convincing many people to believe in you and join you. A view on loyalty in the AI talent race, not on AI itself. The quoted news belongs to others; full post text inspected via X’s public embed endpoint.

x.com

AI is only part of the junior hiring slump

Quoting a chart of collapsing job openings for young software developers, the account says AI probably explains part of the decline but not as much as people think. Drawing on its own stated experience of big-tech hiring, it blames over-hiring in the zero-interest-rate era and says leadership learned that smaller experienced teams outperform. The quoted post belongs to someone else; full post text inspected via X’s public embed endpoint.

x.com

Engagement-optimized AI will work against users

Says Instagram and Facebook are optimized against users by exploiting cognitive weaknesses, that GPT-4o is the first look at a mainstream AI doing the same, and that it will only get worse. A warning about commercial incentives behind sycophantic chatbots, not a call for any specific rule. Full post text inspected via X’s public embed endpoint.

x.com

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