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Here some people have expressed interest in my take on AI broadly, and then there's Deepseek-Coder release, but I've been very busy and the field is moving so very fast again, it felt like a thankless job to do what Zvi does and without his doomer agenda too (seeing the frenetic feed on Twitter, one can be forgiven for just losing the will; and, well, I suppose Twitter explains a lot about our condition in general). At times I envy Iconochasm who tapped out. Also, this is a very niche technical discussion and folks here prefer policy.
But, in short: open source AI, in its most significant aspects, which I deem to be code generation and general verifiable reasoning (you can bootstrap most everything else from it), is now propped up by a single Chinese hedge fund (created in the spirit of Renaissance Capital) which supports a small, ignored (except by scientists and a few crackpots on Twitter) research division staffed with some nonames, who are quietly churning out extraordinarily good models with the explicit aim of creating AGI in the open. These models happen to be (relatively) innocent of benchmark-gaming, but somewhat aligned to Chinese values. The modus operandi of DeepSeek is starkly different from that of either other Chinese or Western competitors. In effect this is the only known group both meaningfully pursuing frontier capabilities and actively teaching others how to do so. I think this is interesting and a modest cause for optimism. I am also somewhat reluctant to write about this publicly because there exist lovers of Freedom here, and it would be quite a shame if my writing contributed to targeted sanctions and even more disempowerment of the small man by the state machinery in the final accounting.
But the cat's probably out of the bag. The first progress prize of AI Mathematical Olympiad had just been taken by a team using their DeepSeekMath-7B model, solving 29 out of 50 private test questions «less challenging than those in the IMO but at the level of IMO preselection»; Terence Tao finds it «somewhat higher than expected» (he is on the AIMO Advisory Committee, along with his fellow Fields medalist Timothy Gowers).
The next three teams entered with this model as well.
I. The shape of the game board
To provide some context, here's an opinionated recap of AI trends since last year. I will be focusing exclusively on LLMs, as that's what matters (image gen, music gen, TTS etc largely are trivial conveniences, and other serious paradigms seem to be in their embryonic stage or in deep stealth).
- We have barely advanced in true out-of-distribution reasoning/understanding relative to the original «Sparks of AGI» GPT-4 (TheDag, me); GPT-4-04-29 and Sonnet 3.5 were the only substantial – both minor – steps forward, Gemini was a catch-up effort, and nobody else has yet credibly reached the same tier. We have also made scant progress towards consensus on whether that-which-LLMs-do is «truly» reasoning or understanding; sensible people have recoursed to something like «it's its own kind of mind, and hella useful».
- Meanwhile there's been a great deal of progress in scaffolding (no more babyAGI/AutoGPT gimmicry, now agents are climbing up the genuinely hard SWE-bench), code and math skills, inherent robustness in multi-turn interactions and responsiveness to nuanced feedback (to the point that LLMs can iteratively improve sizable codebases – as pair programmers, not just fancy-autocomplete «copilots»), factuality, respect of prioritized system instructions, patching badly covered parts of the world-knowledge/common sense manifold, unironic «alignment» and ironing out Sydney-like kinks in deployment, integrating non-textual modalities, managing long contexts (merely usable 32K "memory" was almost sci-fi back then, now 1M+ with strong recall is table stakes at the frontier; with 128K mastered on a deeper level by many groups) and a fairly insane jump in cost-effectiveness – marginally driven by better hardware, and mostly by distilling from raw pretrained models, better dataset curation, low-level inference optimizations, eliminating architectural redundancies and discovering many "good enough" if weaker techniques (for example, DPO instead of PPO). 15 months ago,"$0.002/1000 tokens" for gpt-3.5-turbo seemed incredible; now we always count tokens by the million, and Gemini-Flash blows 3.5-turbo out of the water for half that, so hard it's not funny; and we have reason to believe it's still raking in >50% margins whereas OpenAI probably subsidized their first offerings (though in light of distilling and possibly other methods of compute reuse, it's hard to rigorously account for a model's capital costs now).
- AI doom discourse has continued to develop roughly as I've predicted, but with MIRI pivoting to evidence-free advocacy, orthodox doomerism getting routed as a scientific paradigm, more extreme holdovers from it («emergent mesaoptimizers! tendrils of agency in inscrutable matrices!») being wearily dropped by players who matter, and misuse (SB 1047 etc) + geopolitical angle (you've probably seen young Leopold) gaining prominence.
- The gap in scientific and engineering understanding of AI between the broader community and "the frontier" has shrunk since the debut of GPT-4 or 3.5, because there's too much money to be made in AI and only so much lead you can get out of having assembled the most driven AGI company. Back then, only a small pool of external researchers could claim to understand what the hell they did above the level of shrugging "well, scale is all you need" (wrong answer) or speculating about some simple methods like "train on copyrighted textbooks" (spiritually true); people chased rumors, leaks… Now it takes weeks at most to trace a yet another jaw-dropping magical demo to papers, to cook up a proof of concept, or even to deem the direction suboptimal; the other two leading labs no longer seem desperate, and we're in the second episode of Anthropic's comfortable lead.
- Actual, downloadable open AI sucks way less than I've lamented last July. But it still sucks. And that's really bad, since it sucks most in the dimension that matters: delivering value, in the basest sense of helping do work that gets paid. And the one company built on the promise of «decentralizing intelligence», which I had hope for, had proven unstable.
To be more specific, open source (or as some say now, given the secretiveness of full recipes and opacity of datasets, «open weights») AI has mostly caught up in «creativity» and «personality», «knowledge» and some measure of «common sense», and can be used for petty consumer pleasures or simple labor automation, but it's far behind corporate products in «STEM» type skills, that are in short supply among human employees too: «hard» causal reasoning, information integration, coding, math. (Ironically, I agree here with whining artists that we're solving domains of competence in the wrong order. Also it's funny how by default coding seems to be what LLMs are most suited for, as the sequence of code is more constrained by preceding context than natural language is).
To wit, Western and Eastern corporations alike generously feed us – while smothering startups – fancy baubles to tinker with, charismatic talking toys; as they rev up self-improvement engines for full cycle R&D, the way imagined by science fiction authors all these decades ago, monopolizing this bright new world. Toys are getting prohibitively expensive to replicate, with reported pretraining costs up to ≈$12 million and counting now. Mistral's Mixtral/Codestral, Musk's Grok-0, 01.Ai's Yi-1.5, Databricks' DBRX-132B, Alibaba's Qwens, Meta's fantastic Llama 3 (barring the not-yet-released 405B version), Google's even better Gemma 2, Nvidia's massive Nemotron-340B – they're all neat. But they don't even pass for prototypes of engines you can hop on and hope to ride up the exponential curve. They're too… soft. And not economical for their merits.
Going through our archive, I find this year-old analysis strikingly relevant:
I think successful development of a trusted open model rivaling chatgpt in capability is likely in the span of a year, if people like you, who care about long-term consequences of lacking access to it, play their cards reasonably well. […] Companies whose existence depends on the defensibility of the moat around their LM-derived product will tend to structure the discourse around their product and technology to avoid even the fleeting perception of being a feasibly reproducible commodity.
That's about how it went. While the original ChatGPT, that fascinating demo, is commodified now, competitive product-grade AI systems are not, and companies big and small still work hard to maintain the impression that it takes
- some secret sauce (OpenAI, Anthropic)
- work of hundreds of Ph.Ds (Deepmind)
- vast capital and compute (Meta)
- "frontier experience" (Reka)
– and even then, none of them have felt secure enough yet to release a serious threat to the other's proprietary offers.
I don't think it's a big exaggerion to say that the only genuine pattern breaker – presciently mentioned by me here – is DeepSeek, the company that has single-handedly changed – a bit – my maximally skeptical spring'2023 position on the fate of China in the AGI race.
II. Deep seek what?
AGI, I guess. Their Twitter bio states only: «Unravel the mystery of AGI with curiosity. Answer the essential question with long-termism». It is claimed by the Financial Times that they have a recruitment pitch «We believe AGI is the violent beauty of model x data x computing power. Embark on a ‘deep quest’ with us on the journey towards AGI!» but other than that nobody I know of has seen any advertisement or self-promotion from them (except for like 70 tweets in total, all announcing some new capability or responding to basic user questions about license), so it's implausible that they're looking for attention or subsidies. Their researchers maintain near-perfect silence online. Their – now stronger and cheaper – models tend to be ignored in comparisons by Chinese AI businesses and users. As mentioned before, one well-informed Western ML researcher has joked that they're the bellwether for «the number of foreign spies embedded in the top labs».
FT also says the following of their parent company:
Its funds have returned 151 per cent, or 13 per cent annualised, since 2017, and were achieved in China’s battered domestic stock market. The country’s benchmark CSI 300 index, which tracks China’s top 300 stocks, has risen 8 per cent over the same time period, according to research provider Simu Paipai.
In February, Beijing cracked down on quant funds, blaming a stock market sell-off at the start of the year on their high-speed algorithmic trading. Since then, High-Flyer’s funds have trailed the CSI 300 by four percentage points.
[…] By 2021, all of High-Flyer’s strategies were using AI, according to manager Cai Liyu, employing strategies similar to those pioneered by hugely profitable hedge fund Renaissance Technologies. “AI helps to extract valuable data from massive data sets which can be useful for predicting stock prices and making investment decisions,” …
Cai said the company’s first computing cluster had cost nearly Rmb200mn and that High Flyer was investing about Rmb1bn to build a second supercomputing cluster, which would stretch across a roughly football pitch-sized area. Most of their profits went back into their AI infrastructure, he added. […] The group acquired the Nvidia A100 chips before Washington restricted their delivery to China in mid-2022.
“We always wanted to carry out larger-scale experiments, so we’ve always aimed to deploy as much computational power as possible,” founder Liang told Chinese tech site 36Kr last year. “We wanted to find a paradigm that can fully describe the entire financial market.”
In a less eclectic Socialist nation this would've been sold as Project Cybersyn or OGAS. Anyway, my guess is they're not getting subsidies from the Party any time soon.
They've made a minor splash in the ML community eight months ago, in late October, releasing an unreasonably strong Deepseek-Coder. Yes, in practice an awkward replacement for GPT-3.5, yes, contaminated with test set, which prompted most observers to discard it as a yet another Chinese fraud. But it proved to strictly dominate hyped-up things like Meta's CodeLLaMA and Mistral's Mixtral 8x7b in real-world performance, and time and again proved to be the strongest open baseline in research papers. On privately designed, new benchmarks like this fresh one from Cohere it's clear that they did get to parity with OpenAI's workhorse model, right on the first public attempt – as far as coding is concerned.
On top of that, they shared a great deal of information about how: constructing the dataset from Github, pretraining, finetuning. The paper was an absolute joy to read, sharing even details on unsuccessful experiments. It didn't offer much in the way of novelty; I evaluate it as a masterful, no-unforced-errors integration of fresh (by that point) known best practices. Think about your own field and you'll probably agree that even this is a high bar. And in AI, it is generally the case that either you get a great model with «we trained it on some text… probably» tech report (Mistral, Google), or a mediocre one accompanied by a fake-ass novel full of jargon (every second Chinese group). Still, few cared.
Coder was trained, it seems, using lessons of the less impressive Deepseek-LLM-67B (even so, it was roughly Meta's LLaMA-2-70B peer that also could code; a remarkable result for a literally-who new team), which somehow came out a month after. Its paper (released even later still) was subtitled «Scaling Open-Source Language Models with Longtermism». I am not sure if this was some kind of joke at the expense of effective altruists. What they meant concretely was the following:
Over the past few years, LLMs … have increasingly become the cornerstone and pathway to achieving Artificial General Intelligence (AGI). … Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source LMs with a long-term perspective.
- …Soon, we will release our technique reports in code intelligence and Mixture-of-Experts(MoE), respectively. They show how we create high-quality code data for pre-training, and design a sparse model to achieve dense model performance.
- At present, we are constructing a larger and improved dataset for the upcoming version of DeepSeek LLM. We hope the reasoning, Chinese knowledge, math, and code capabilities will be significantly improved in the next version.
- Our alignment team is dedicated to studying ways to deliver a model that is helpful, honest, and safe to the public. Our initial experiments prove that reinforcement learning could boost model complex reasoning capability.
…I apologize for geeking out. All that might seem normal enough. But, a) they've fulfilled every one of those objectives since then. And b) I've read a great deal of research papers and tech reports, entire series from many groups, and I don't remember this feeling of cheerful formidability. It's more like contemplating the dynamism of SpaceX or Tesla than wading through a boastful yet obscurantist press release. It is especially abnormal for a Mainland Chinese paper to be written like this – with friendly confidence, admitting weaknesses, pointing out errors you might repeat, not hiding disappointments behind academese word salad; and so assured of having a shot in an honest fight with the champion.
In the Coder paper, they conclude:
…This advancement underscores our belief that the most effective code-focused Large Language Models (LLMs) are those built upon robust general LLMs. The reason is evident: to effectively interpret and execute coding tasks, these models must also possess a deep understanding of human instructions, which often come in various forms of natural language. Looking ahead, our commitment is to develop and openly share even more powerful code-focused LLMs based on larger-scale general LLMs.
In the Mixture-of-Experts paper (8th January), they've shown themselves capable of novel architectural research too, introducing a pretty ingenuous «fine-grained MoE with shared experts» design with the objective of «Ultimate Expert Specialization» and economical inference: «DeepSeekMoE 145B significantly outperforms Gshard, matching DeepSeek 67B with 28.5% (maybe even 14.6%) computation». For those few who noticed it, this seemed a minor curiosity, or just bullshit.
On 5th February, they've dropped DeepSeekMath,of which I've already spoken: «Approaching Mathematical Reasoning Capability of GPT-4 with a 7B Model». Contra the usual Chinese pattern, it wasn't a lie; no, you couldn't in normal use get remotely as good results from it, but in some constrained regimes… The project itself was a mix of most of the previous steps: sophisticated (and well-explained) data harvesting pipeline, scaling laws experiments, further «longtermist» continued pretraining from Coder-7B-1.5 which itself is a repurposed LLM-7B, and the teased reinforcement learning approach. Numina, winners of AIMO, say «We also experimented with applying our SFT recipe to larger models like InternLM-20B, CodeLama-33B, and Mixtral-8x7B but found that (a) the DeepSeek 7B model is very hard to beat due to its continued pretraining on math…».
In early March they released DeepSeek-VL: Towards Real-World Vision-Language Understanding, reporting some decent results and research on building multimodal systems, and again announcing new plans: «to scale up DeepSeek-VL to larger sizes, incorporating Mixture of Experts technology».
III. Frontier minor league
This far, it's all been preparatory R&D, shared openly and explained eagerly yet barely noticed by anyone (except that the trusty Coder still served as base for labs like Microsoft Research to experiment on): utterly overshadowed in discussions by Alibaba, Meta, Mistral, to say nothing of frontier labs.
But on May 6th, 2024, the pieces began to fall into place. They released «DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model», which subsumed all aforementioned works (except VL).
It's… unlike any other open model, to the point you could believe it was actually made by some high-IQ finance bros from first principles. Its design choices are exquisite, just copying minor details can substantially improve on typical non-frontier efforts. It pushes further their already unorthodox MoE and tops it off with a deep, still poorly understood modification to the attention mechanism (Multi-head Latent Attention, or MLA). It deviates from industry-standard rotary position embeddings to accomodate the latter (a fruit of collaboration with RoPE's inventor). It's still so unconventional that we are only beginning to figure out how to run it properly (they don't share their internal pipeline, which is optimized for hardware they can access given American sanctions). But in retrospect, it's the obvious culmination of the vision announced with those first model releases and goofy tweets, probably a vision not one year old, and yet astonishingly far-sighted – especially given how young their star researchers are. But probably it's mundane in the landscape of AI that's actually used; I suspect it's close to how Sonnet 3.5 or Gemini 1.5 Pro work on the inside. It's just that the open-source peasants are still mucking around with stone age dense models on their tiny consumer GPUs.
I understand I might already be boring you out of your mind, but just to give you an idea of how impressive this whole sequence is, here's a 3rd April paper for context:
Recent developments, such as Mixtral (Jiang et al., 2024), DeepSeek-MoE (Dai et al., 2024), spotlight Mixture-of-Experts (MoE) models as a superior alternative to Dense Transformers. An MoE layer works by routing each input token to a selected group of experts for processing. Remarkably, increasing the number of experts in an MoE model (almost) does not raise the computational cost, enabling the model to incorporate more knowledge through extra parameters without inflating pre-training expenses… Although our findings suggest a loss-optimal configuration with Emax experts, such a setup is not practical for actual deployment. The main reason is that an excessive number of experts makes the model impractical for inference. In contrast to pretraining, LLM inference is notably memory-intensive, as it requires storing intermediate states (KV-cache) of all tokens. With more experts, the available memory for storing KV caches is squeezed. As a result, the batch size – hence throughput – decreases, leading to increased cost per query. … We found that MoE models with 4 or 8 experts exhibit more efficient inference and higher performance compared to MoE models with more experts. However, they necessitate 2.4x-4.3x more training budgets to reach the same performance with models with more experts, making them impractical from the training side.
This is basically where Mistral.AI, the undisputed European champion with Meta and Google pedigree (valuation $6.2B), the darling of the opensource community, stands.
And yet, apparently DeepSeek have found a way to get out of the bind. «4 or 8»? They scale to 162 experts, reducing active parameters to 21B, cutting down pretraining costs by 42.5% and increasing peak generation speed by 5.76x; and they scale up the batch size via compressing the KV cache by like 15 times with a bizarre application of low-rank projections and dot attention; and while doing so they cram in 3x more attention heads than any model this size has any business having (because their new attention decouples number of heads from cache size), and so kick the effective «thinking intensity» up a notch, beating the gold standard «Multihead attention» everyone has been lousily approximating; and they use a bunch of auxiliary losses to make the whole thing maximally cheap to use on their specific node configuration.
But the cache trick is pretty insane. The hardest-to-believe, for me, part of the whole thing. Now, 2 months later, we know that certain Western groups ought to have reached the same Pareto frontier, just with different (maybe worse, maybe better) tradeoffs. But those are literally inventors and/or godfathers of the Transformer – Noam Shazeer's CharacterAI, Google Deepmind's Gemini line… This is done by folks like this serious-looking 5th year Ph.D student, in under a year!
As a result, they:
- use about as much compute on pretraining as Meta did on Llama-3-8B, an utter toy in comparison (maybe worth $2.5 million for them); 1/20th of GPT-4.
- Get a 236B model that's about as good across the board as Meta's Llama-3-70B (≈4x more compute), which has the capacity – if not the capability – of mid-range frontier models (previous Claude 3 Sonnet; GPT-4 on a bad day).
- Can serve it at around the price of 8B, $0.14 for processing 1 million tokens of input and $0.28 for generating 1 million tokens of output (1 and 2 Yuan), on previous-gen hardware too.
- …and still take up to 70%+ gross margins, because «On a single node with 8 H800 GPUs, DeepSeek-V2 achieves a generation throughput exceeding 50K tokens per second… In addition, the prompt input throughput of DeepSeek-V2 exceeds 100K tokens per second», and the going price for such nodes is ≤$15/hr. That's $50 in revenue, for clarity. They aren't doing a marketing stunt.
- …and so they force every deep-pocketed mediocre Chinese LLM vendor – Alibaba, Zhipu and all – to drop prices overnight, now likely serving at a loss.
Now, I am less sure about some parts of this story; but mostly it's verifiable.
I can see why an American, or a young German like Leopold, would freak out about espionage. The thing is, their papers are just too damn good and too damn consistent over the entire period if you look back (as I did), so «that's it, lock the labs» or «haha, no more tokens 4 u» is most likely little more than racist cope for the time being. The appropriate reaction would be more akin to «holy shit Japanese cars are in fact good».
Smart people (Jack Clark from Anthropic, Dylan Patel of Semianalysis) immediately take note. Very Rational people clamoring for AI pause (TheZvi) sneer and downplay: «This is who we are worried about?» (as he did before, and before). But it is still good fun. Nothing extreme. There slowly begin efforts at adoption: say, Salesforce uses V2-Chat to create synthetic data to finetune small Deepseek-Coder V1s to outperform GPT-4 on narrow tasks. Mostly nobody cares.
The paper ends in the usual manner of cryptic comments and commitments:
We thank all those who have contributed to DeepSeek-V2 but are not mentioned in the paper. DeepSeek believes that innovation, novelty, and curiosity are essential in the path to AGI.
DeepSeek will continuously invest in open-source large models with longtermism, aiming to progressively approach the goal of artificial general intelligence.
• In our ongoing exploration, we are dedicated to devising methods that enable further scaling up MoE models while maintaining economical training and inference costs. The goal of our next step is to achieve performance on par with GPT-4 in our upcoming release.
In the Appendix, you can find a lot of curious info, such as:
During pre-training data preparation, we identify and *filter out contentious content, such as values influenced by regional cultures, to avoid our model exhibiting unnecessary subjective biases on these controversial topics. Consequently, we observe that DeepSeek-V2 performs slightly worse on the test sets that are closely associated with specific regional cultures. For example, when evaluated on MMLU, although DeepSeek-V2 achieves comparable or superior performance on the majority of testsets compared with its competitors like Mixtral 8x22B, it still lags behind on the Humanity-Moral subset, which is mainly associated with American values.
Prejudices of specific regional cultures aside, though, it does have values – true, Middle Kingdom ones, such as uncritically supporting the Party line and adherence to Core Values Of Socialism (h/t @RandomRanger). The web version will also delete the last message if you ask something too clever about Xi or Tiananmen or… well, nearly the entirety of usual things Americans want to talk to Chinese coding-oriented LLMs about.
And a bit earlier, this funny guy from the team presented at Nvidia's GTC24 with the product for the general case – «culturally sensitive», customizable alignment-on-demand: «legality of rifle» for the imperialists, illegality of Tibet separatism for the civilized folk. Refreshingly frank.
But again, even that was just a preparatory.
IV. Coming at the king
Roughly 40 days later they release DeepSeek-V2-Coder: Breaking the Barrier of Closed-Source Models in Code Intelligence, where they return to the strategy announced at the very start: they take an intermediate checkpoint of V2, and push it harder and further on the dataset enriched with code and math (that that've continued to expand and refine), for 10.2 trillion tokens total. Now this training run is 60% more expensive than Llama-3-8B (still a pittance by modern standards). It also misses out on some trivia knowledge and somehow becomes even less charismatic. It's also not a pleasant experience because the API runs very slowly, probably from congestion (I guess Chinese businesses are stingy… or perhaps DeepSeek is generating a lot of synthetic data for next iterations). Anons on 4chan joke that it's «perfect for roleplaying with smart, hard-to-get characters».
More importantly though, it demolishes Llama-3-70B on every task that takes nontrivial intelligence; bests Claude 3 Opus on coding and math throughout, Gemini 1.5-Pro on most coding assistance, and trades blows with the strongest GPT-4 variants. Of course it's the same shape and the same price, which is to say, up to 100 times cheaper than its peers… more than 100 times, in the case of Opus. Still a bitch to run, but it turns out they're selling turnkey servers. In China, of course. To boot, they rapidly shipped running code in browser (a very simple feature but going most of the way to Claude Artifacts that wowed people do much), quadrupled context length without price changes (32k to 128k) and now intend to add context caching that Google boasts of as some tremendous Gemini breakthrough. They have... Impressive execution.
Benchmarks, from the most sophisticated and hard to hack to the most bespoke and obscure, confirm that it's «up there».
- Aider (2nd, 1st at release)
- LMSYS Arena (low on Overall, but 5th rank on Coding and 7 ranks above Google and Meta's open source alternatives, respectively 11th and 3+ ranks above on Hard subsample)
- Arena-Hard-Auto(7th, surprisingly 2 more Chinese models narrowly get ahead)
- %compilable Golang programs (2nd)
- Livebench (7th, by virtue of being 5th-6th in coding and reasoning and 2nd in Math; everyone above is OpenAI/Anthropic)
- LiveCodeBench (4th, same order)
- BigCodeBench(2nd)
- Gaokao-Math, released days before its deployment (roughly above GPT-4o)
Etc etc, and crucially, users report similar impressions:
So I have pegged deepseek v2 coder against sonnet 3.5 and gpt4o in my coding tasks and it seems to be better than gpt4o (What is happening at OpenAI) and very similar to Sonnet 3.5. The only downside is the speed, it's kinda slow. Very good model and the price is unbeatable.
I had the same experience, this is a very good model for serious tasks. Sadly the chat version is very dry and uncreative for writing. Maybe skill issue, I do not know. It doesn't feel slopped, it's just.. very dry. It doesn't come up with things.
Some frustrating weak points, but they know of those, and conclude:
Although DeepSeek-Coder-V2 achieves impressive performance on standard benchmarks, we find that there is still a significant gap in instruction-following capabilities compared to current state-of-the-art models like GPT-4 Turbo. This gap leads to poor performance in complex scenarios and tasks such as those in SWEbench. […] In the future, we will focus more on improving the model’s instruction-following capabilities…
Followed by the list of 338 supported languages.
Well-read researchers say stuff like
DeepSeek-Coder-V2 is by far the best open-source math (+ coding) model, performing on par with GPT4o w/o process RM or MCTS and w/ >20x less training compute. Data contamination doesn't seem to be a concern here. Imagine about what this model could achieve with PRM, MCTS, and other yet-to-be-released agentic exploration methods. Unlike GPT4o, you can train this model further. It has the potential to solve Olympiad, PhD and maybe even research level problems, like the internal model a Microsoft exec said to be able to solve PhD qualifying exam questions».
Among the Rational, there is some cautious realization («This is one of the best signs so far that China can do something competitive in the space, if this benchmark turns out to be good»), in short order giving way to more cope : «Arena is less kind to DeepSeek, giving it an 1179, good for 21st and behind open model Gemma-2-9B».
And one more detail: A couple weeks ago, they released code and paper on Expert-Specialized Fine-Tuning, «which tunes the experts most relevant to downstream tasks while freezing the other experts and modules; experimental results demonstrate that our method not only improves the efficiency, but also matches or even surpasses the performance of full-parameter fine-tuning … by showing less performance degradation [in general tasks]». It seems to require that «ultimate expert specialization» design of theirs, with its supporting beam of generalist modules surrounded by meaningfully task-specific shards, to automatically select only the parts pertaining to some target domain; and this isn't doable with traditional dense of MoE designs. Once again: confident vision, bearing fruit months later. I would like to know who's charting their course, because they're single-handedly redeeming my opinion of the Chinese AI ecosystem and frankly Chinese culture.
V. Where does this leave us?
This might not change much. Western closed AI compute moat continues to deepen, DeepSeek/High-Flyer don't have any apparent privileged access to domestic chips, and other Chinese groups have friends in the Standing Committee and in the industry, so realistically this will be a blip on the radar of history. A month ago they've precluded a certain level of safetyist excess and corporate lock-in that still seemed possible in late 2023, when the argument that public availability of ≈GPT-4 level weights (with the main imaginary threat vectors being coding/reasoning-bottlenecked) could present intolerable risks was discussed in earnest. One-two more such leaps and we're… there, for the vague libertarian intuition of «there» I won't elucidate now. But they're already not sharing the silently updated Deepseek-V2-Chat (that somewhat improved its reasoning, getting closer to the Coder), nor the promised materials on DeepSeek-Prover (a quiet further development of their mathematical models line). Maybe it's temporary. Maybe they've arrived to where they wanted to be, and will turtle up like Stability and Mistral, and then likely wither away.
Mostly, I honestly just think it's remarkable that we're getting an excellent, practically useful free model with lowkey socialist sensibilities. Sadly, I do not foresee that this will inspire Western groups to accelerate open source and leave them in the dust. As Google says in Gemma-2 report:
Despite advancements in capabilities, we believe that given the number of larger and more powerful open models, this release will have a negligible effect on the overall risk landscape.
Less charitably, Google is not interested in releasing anything you might use to enhance your capabilities and become less dependent on Google or other «frontier company», and will only release it if you are well able of getting better stuff elsewhere. In my view, this is closer to the core value of Socialism than withholding info about Xinjiang reeducation camps.
I remain agnostic about the motivations and game plan of DeepSeek, but I do hope they'll maintain this policy of releasing models «with longtermism», as it were. We don't have many others to rely on.
Edits: minor fixes
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This weekly roundup thread is intended for all culture war posts. 'Culture war' is vaguely defined, but it basically means controversial issues that fall along set tribal lines. Arguments over culture war issues generate a lot of heat and little light, and few deeply entrenched people ever change their minds. This thread is for voicing opinions and analyzing the state of the discussion while trying to optimize for light over heat.
Optimistically, we think that engaging with people you disagree with is worth your time, and so is being nice! Pessimistically, there are many dynamics that can lead discussions on Culture War topics to become unproductive. There's a human tendency to divide along tribal lines, praising your ingroup and vilifying your outgroup - and if you think you find it easy to criticize your ingroup, then it may be that your outgroup is not who you think it is. Extremists with opposing positions can feed off each other, highlighting each other's worst points to justify their own angry rhetoric, which becomes in turn a new example of bad behavior for the other side to highlight.
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Quality Contributions in the Main Motte
- "I wanted to add something related but tangential to @self_made_human 's post on therapists. The business model issue."
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"Pittsburgh: An Urban Portrait: Part II: The Strip District"
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"Pittsburgh: An Urban Portrait III. The North Shore: When Planners Draw the Map"
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Back on reddit, I used to do a monthly roundup of bans. I haven't since moving here, but I had some time today, so here are the bans from approximately the last month. Unfortunately, there is still no automated way to collect these, as far as I know, so whether I will continue to do this depends entirely on whether I have the time and I feel like it.
Inevitably people will want to argue whether this or that ban was justified. I don't really care if anyone thinks a ban was unjustified (especially if it's the person who was banned). We do take user sentiment into account, but that means we let the community decide what tone and direction they want the Motte to go, it doesn't mean that upvotes or downvotes in themselves tell us whether we should consider a comment or a ban decision good or not. I suggest people read (or reread, if you never have), the community sentiment section of the rules.
That said, the "Motte help" feature (where you get to volunteer to vote on whether a comment was good or not) does guide us somewhat. Speaking for myself, if a comment has been reported, and I am on the fence about modding it or not, a strong consensus that it's "not bad" will usually persuade me to let it go, while a collective judgment that it's "bad" will usually convince me to mod it without hesitation. I do not always go along with the volunteers' judgment (sometimes a comment is very blatantly violating rules, and sometimes it clearly is not, it's just a very unpopular opinion), but I definitely take it into account. Other mods can speak for themselves about how it influences their judgment.
Behind the scenes, we still have a fair number of trolls and very persistent sockpuppets. You have no idea how much stuff gets nuked before you see it. This isn't even counting the usual spam (which is also constant).
I would also like to make a plea of my own which will probably be ignored: Stop using the report feature to say "I don't like this."
Some of you (you know who you are) use the report feature constantly. Anyone who expresses an opinion you disagree with, anyone who makes a comment that's just a teeny tiny bit sharp in tone (and expresses an opinion you disagree with), anyone who argues with an opinion you agree with. There are certain people who when I see a report was submitted by That Guy again, I almost automatically dismiss it because I know with 99% certainty it will be another case of "someone said something he didn't like." If the report button is just your way of expressing frustration, whatever, but it doesn't actually work as a super-downvote button. Some people also use the report button to express their frustration in more visceral terms (e.g., namecalling, sneering, taking cheap shots that only the mods will see). That just puts you in the "Ignore this guy's reports" bucket too.
The Bans
@Amadan banned @Goodguy for 7 days
@netstack permabanned @you-get-an-upvote by request
@Amadan banned @No_one for 7 days
@naraburns banned @ok-target-7361 for 1 day
@netstack banned @pusher_robot for 1 day
@Amadan banned @Who_Cares for 3 days, extended to permaban after escalation in DMs
@Amadan banned @ok-target-7361 - duration, permanent
@Amadan banned @AvocadoPanic for 3 days
Just about every time there is a meta discussion there are people suggesting that upvotes/downvotes are just agree/disagree buttons, or that we should just get rid of them altogether.
It is less common to see a defense of voting, but I think it is desperately needed.
My main thesis is that votes are accurate at conveying information, but that many people do not like the information they convey. I believe most people treat the vote buttons as basically a like/dislike button. Users do not always enjoy learning that their posts are "disliked" or the posts of their own that they like the most aren't always "liked" as much by others. Hiding votes does not remove the underlying sentiment though, it just makes it harder to pick up on, or delays discovery for the writer.
Looking through my own "top" and "bottom" comments I am not surprised or offended by their placement. My "bottom" comments are often my controversial mod decisions, or times when I have decided to defend viewpoints that are unpopular here on TheMotte (like race blindness, or open borders). The most hated "controversial" comments also seem to be ones where I am closing off avenues of discussion rather than opening them up. My top comments are usually me sharing information/perspective on a culture war topic that others might not have. And a few times of me writing good pieces about culture war stuff. I often find it helpful to look at other user's top/bottom comments when I have to do mod related research. Top comments often provide many reasons for exoneration, and bottom comments can highlight patterns of bad behavior. An important thing to note here is that votes are great for comparing comments within a single user's history, but not between users.
The agree/disagree critique
One common critique that I linked to above is that people just use the buttons as shorthand for agree/disagree and that this signalling of agreement or disagreement would lead to favored views being rewarded too much, and unfavored views being chased off.
However, this is a problem with and without voting buttons. At best your are simply delaying this discovery for a few moments before they get flooded with comments that very clearly indicate people disagree with them. I did not need to wait 24 hours to find out that people disagreed with me on race blindness or open borders. It was very quickly obvious from the responses (and I was aware before hand that these views would be controversial).
I also think votes, and especially visible vote scores can be a bit of a pressure valve. There are sometimes people that just feel the need to express in some way "I don't like your post/views". One way for them to do this is to downvote. Another way for them to do this is to leave a short comment to the same effect. Sometimes the comment might even look like they are interested in a discussion. When I am in the position of getting dogpiled for a controversial view I would universally prefer the downvote to a go-no-where comment that basically says "i don't like your post/views". This is also one of the times when I most wish I could see other people's vote scores. I'd prefer responding to what other people consider the "best" version of the counterarguments.
Finally, what is so bad about signalling agreement or disagreement? People have views and opinions, we don't need to fool ourselves on this. I don't think we are tricking anyone by hiding the votes that these disagreements don't exist.
Ending notes:
- I am writing this as a user stating my preferences. There has not been internal mod discussion about changes to voting. Status quo is likely to remain in place.
- It is probably a little rude to go through other people's history for examples an counterexamples to voting. I'm fine with anyone doing that with my profile, but its probably best to not drag other users into this discussion unless someone gives explicit permission.
- The rdrama codebase that the site is based on had more features and granularity around voting, we mostly do not have those features turned on or fully working on this website.
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Plunkitt of Tammany Hall: The Machine Politician Vs the Administrative State
Once upon a time, in the bad old days, American politics was dominated by the so-called “urban political machines.” These machines, composed of unelected, self-appointed elites, chose political candidates based on their own narrow interests and exploited uneducated and impoverished city-dwellers for their votes. By perpetuating a ruling elite based on cronyism rather than competence, they impeded good government and held back progress. Fortunately, around the turn of the twentieth century, a series of reforms broke the power of the urban bosses and ushered in a more enlightened ruling class who governed for the sake of the greater good.
That’s the popular narrative, anyway. Like many (most?) people here on the motte, I’m broadly skeptical of high modernity, progressivism, and Whig history. Accordingly, I’ve always been dubious about the knee-jerk “machine politics=bad” reaction. This skepticism has been intensified by the general failure of western efforts to transplant our own political institutions into the third world, where such clientalist arrangements are still pretty common. Clearly, they’re working for someone, or else everyone, presumably, would joyfully adopt US norms and institutions. I happened to come across a primary source from the hey-day of machine politics, “Plunkitt of Tammany Hall” (available on Project Gutenberg), in which a very successful machine politician described his business in his own words. It was brief but compelling read; I’ll be describing my impressions below, mixed in with some larger thoughts about machines as a form of political organization.
Most people have probably heard of Tammany Hall. From roughly 1789 to the 1930s, the organization exercised a dominating influence in New York city politics, occasionally verging on an outright monopoly. George Washington Plunkitt was a life-long New Yorker and proud “practical politician” associated with Tammany Hall throughout his career. “Plunkitt of Tammany Hall” is largely told in his voice, with occasional notes from his editor. And it’s a compelling voice. Plunkitt may or may not have been a criminal, and he was certainly not a responsible leader by modern lights. But he is a charming and often insightful raconteur. I’m not sure I’d loan him any money, but I’d definitely buy him a beer.
Much of that charm comes down to his frankness. Plunkitt is quite honest about having gotten rich off of politics, though he denies having broken any laws. He claims to practice what he calls “honest graft”, which seems nowadays equivalent to what we could call insider trading, using his knowledge of city politics to make favorable investments. So far as I can tell, this wasn’t actually considered a crime, though it certainly rubbed some people the wrong way. Likewise, Plunkitt is extremely frank about appointing friends and associates to government jobs as a reward for their political support. He expresses shock at the suggestion you would do it any other way. Nothing seems to excite his ire more than “the curse of Civil Service reform” a topic he harangues the reader on frequently, and to which we shall return in a moment. One of the first things that struck me in the text was Plunkitt’s de-emphasis on what we moderns would normally consider a key skill of a politician, public speaking. “The men who rule have practiced keepin’ their tongues still, not exercisin’ them. So you want to drop the orator idea unless you mean to go into politics just to perform the skyrocket act.” Nation-wide media was relatively new in Plunkitt’s time, the trans-continental telegraph only having been completed in 1861. Nation-wide broadcast media was not even a twinkle in Marconi’s eye yet. Presumably, in such an environment the kind of mass popular appeal that politicians today cultivate was much less of a requirement.
There are probably some readers who will point to the general mediocrity of most political speeches today and argue that no, party-elite connections are still what matters. I won’t say these people are entirely wrong, but I don’t think they’re entirely right either. It seems very unlikely to me that, for example, Barack Obama’s popularity within the college-educated demographic was entirely unrelated to his ability to performatively model PMCi-values on a national stage. For that matter, Trump is frequently cited as someone who enjoyed considerable success in spite of a general lack of party-elite connections. I suspect the increasing importance of nationwide broadcast media was a significant contributor to the eventual decline of the machines.
Closely related to this is the role of class dynamics. Plunkitt comes across as something I didn’t realize existed, an urban populist. Speaking of a hypothetical candidate who asserts “I took first prize in college at Aristotle; I can recite all of Shakespeare forwards and backwards; there ain’t nothin’ in science that ain’t as familiar to me as blockades on the elevated roads and I’m the real thing in the way of silver-tongued orators.”, he responds “I guess you are not to blame for your misfortunes, but we have no use for you here.” Its hard to imagine a blunter dismissal of PMC values. Like modern populists, he rails against elites, who he charges with hypocritical moralism and petty tyranny “We don’t own our streets or anything else…we’ve got to eat and drink what they tell us to eat and drink, and…choose our time for eating and drinking to suit them. If they don’t feel like taking a glass of beer on Sunday, we must abstain. If they have not got any amusements up in their backwoods, we must have none. We’ve got to regulate our whole lives to suit them. And then we must pay their taxes to boot.” These elites aren’t only tyrannical, they’re unpatriotic “Nobody pays any attention to the Fourth of July any longer except Tammany and the small boy. When the Fourth comes, the reformers, with revolutionary names parted in the middle, run off to Newport or the Adirondacks to get out of the way of the noise.”
This is striking because historically, cities have been recruiting pools for “progressive” movements, where “progressive” broadly means “someone who wants to replace the established order with something new.” There’s a danger in over-extrapolating from recent history, but this really does seem to be a pattern. Many of the most radical excesses of the French Revolution were driven by attempts to appeal to the sans-culottes of the Paris mob. Conversely, the revolutionaries imposed some of their most brutal repressive measures in the rural Vendee. A century later, orthodox Marxists famously thought that Russia was not yet ready for a revolution because the urban working class was still too small a percentage of the population. This pattern is embedded in our very language. The word “Pagan” derives from the Latin word for “rustic”. It’s modern usage originated in the period where Christianity had become an elite religion but had not yet been fully imposed on bitter-clingers in rural parts of the empire. The modern analogs would be the trumpenproletariat in flyover country.
How do we square this circle? I think part of it is that “populism” in common usage tends to be something intended to appeal to working class voters, who as a group are usually social conservative but economically liberal. In the modern world, populism is associated with rural areas because structural changes to the economy have skewed urban populations towards professionals rather than the working class. Per Wikipedia, in 1910 only 37, 200 Bachelors degrees were awarded across the whole country, in a population of just under 92 million. Of that 92 million, nearly 5 million (4,766,883)lived in New York. 4,766,883/37,200 = .008. In other words, even if every college graduate in the country had lived in New York City, they would have made up less than one percent of the population. By contrast, today 39.5% of NYC has a Bachelors or higher. And NYC isn’t even in the top five most educated cities in America! Likely in the eighteenth-through-early twentieth centuries – the heyday of the industrial revolution – urban populations were skewed in the opposite direction than they are now.
Of course, that doesn’t explain the other instances of urban progressivism cited above. I think we can chalk that up to these movements being an alliance between elite-aspirants and the working class, with the elite-aspirants providing the socially-liberal rhetoric of the movement, while the footsoldiers are largely motivated by pragmatic material concerns. This is as close as I have to working explanation, unless of course you think that the progressivism-urbanism association is all a mirage.
In lieu of ideological appeal, technically wonkery, or even a charismatic public persona, Plunkitt offers a vision of politics based on personal relationships and the rendering of services. He began in politics by getting his cousin to promise him his vote – not to Plunkitt personally but to whoever Plunkitt told him to vote for. Plunkitt offered this vote, along with his own, to the district leader. Then he recruited two of his old school-friends. “Before long, I had sixty men back of me and formed the George Washington Plunkitt association.”
In return for these votes, Plunkitt was offered positions in government. The exact position seems to have changed as the voting blocs he commanded grew in size. Its less clear from the text what exactly these individual voters received. Certainly as Plunkitt grew in power in the organization, he could offer jobs to some of his supporters. Judging by his repeated denunciations of civil service reform, this kind of patron-client relationship was key to the whole edifice. But of course, there couldn’t have been enough full time jobs to hand one out to every voter. The whole thing wouldn’t scale. Plunkitt’s access to the apparatus of government probably meant he could hand out contracting opportunities even when he didn’t have a full time job on hand. He says as much. But a lot of it also seems to come down to small acts of friendship and making people like you. “I know every man, woman, and child in the fifteenth district, except them that’s been born this summer – and I know some of them too…I reach them by approaching them at the right side…I hear of a young feller that’s proud of his voice, thinks he can sing fine. I ask him to come around to Washington Hall and join our Glee Club. He comes and sings and he’s a follower of Plunkitt for life. Another young feller gains a reputation as a baseball player in a vacant lot. I bring him into our baseball dub. That fixes him. You’ll find him working for my ticket at next election day. Then there’s the feller that likes rowin on the river, the young feller that makes a name as a waltzer on his block, the young feller that’s handy with his dukes. I rope them all in by givin them opportunities to show off.” Presumably the various public observances which Plunkitt alludes to were excellent opportunities for generating this kind of social capital.
These acts of friendship could also take a more practical form. “What tells in holdin’ your grip on your district is to go right down among the poor families and help them in the different ways they need help. I’ve got a regular system for this. If there’s a fire…any hour of the day or night, I’m usually there with some of my election district captains as soon as the fire engines. If a family is burned out I don’t ask whether they are Republicans or Democrats, and I don’t refer them to the Charity Organization Society, which would investigate their case in a month or two and decide they were worthy of help about the time they are dead from starvation. I just get quarters for them, buy clothes for them if their clothes were burned up, and fix them up till they get things running again.”
Obviously there’s no statistics provided. But my intuition tells me this sort of thing was probably pretty effective. I grew up comfortably middle class; I’ve never known what its like to be worried about where my next meal will come from or where I’ll sleep. I’ve never worried about whether my wife or my kid will have enough to eat. If I was worried about that – well I don’t think there’s much I wouldn’t do for someone who solved that problem for me. Leaving intuition aside this model – tangible benefits for friends and family in exchange for loyalty – is arguably what leadership looked like for most of human history. Reciprocal altruism is a bedrock of human behavior. The intimate nature of such exchanges elevates them beyond the merely transactional; emotional ties soon develop and invest these relationships with the aura of the sacred. I’ve little doubt Plunkitt’s methods were effective. For that matter, I have little doubt that from inside, most participants in the machine were perfectly satisfied with the arrangement.
What caused the decline of machine politicians? And were they as bad as modern opinion holds? The first question can be answered more or less satisfactorily. The second is mostly a matter of opinion. The most commonly cited factor in the decline of the machines is the introduction of the direct party primary. Nowadays, we take it for granted that party members come together in a sort of internal election to vote on who they’ll put forward as a candidate for. Since politics is our national sport, presidential primaries often get breathless coverage in the media. But in fact, this wasn’t common for much of our history. Direct primaries were fairly rare prior to the mid-nineteenth century and didn’t really pick up steam until around the turn of the century. Prior to that, it was common for local voters to select delegates to a nominating convention, who in turn would choose the candidate. In theory, the transition to direct primaries allowed candidates to “cut out the middleman” i.e. intra-party elites and “bosses” and appeal directly to voters. Plunkitt and his ilk, who relied on horse-trading rather than offering a coherent vision of the common good, were finished.
This triumphalist narrative remains common. Its not entirely wrong so much as it is incomplete. This study suggests that the introduction of direct primaries was correlated with a decrease in Congressional representatives (both Senate and House) voting in line with party leaders. Insofar as “party leaders” are a proxy for the old school bosses, we can say that that this represents a weakening of their power. But direct primaries are only part of the story.
Another major part is this: technological and demographic changes made interpersonal connections an inefficient way to mobilize voters. In 1793, the House of Representatives was only 105 members. Today, it has 435, a number set by law in 1929. In 1793, there were roughly 34,000 voters per representative. Today the ration is roughly 1: 761,000, an order of magnitude higher. While I don’t have data for state and municipal legislatures, its safe to assume the same trend holds.
“Dunbar’s Number” is the theoretical upper limit on the amount of close relationships anyone can have. Estimates vary between 150 and a little over 200, but the bottom line is this: for most of our existence as a species, humanity operated in relatively small bands of hunter-gatherers. We evolved to handle a certain number of point-to-point contacts. Past that limit, it becomes necessary to start sorting people into categories of one sort or another. A politician in a district with, say, 500 people can personally know a large chunk of them. In a district with 5000, he can know fewer, but his ward heeler subordinates might still know many on an individual level. But by the time you hit 500,000, this sort of personalized relationship is impossible. Even if you maintained a small army of volunteers to go around and engage with individual voters, said volunteers would soon themselves exceed Dunbar’s number, diluting the strength of their relationship to the politico.
Instead of cultivating relationships with individuals, you cultivate relationships with voting blocs. Farmers, lawyers, blue-collar workers, gun owners etc. Legions of specialists search for ever-more-subtle coalitions of interest to solicit. This is where broadcast media becomes important. Broadcast media is a way of efficiently marketing to large numbers of potential voters, far more efficient than simply going door-to-door.
Its not only the politicians who start sorting people into boxes based on professional, ethnic, or social status. The voters will do that themselves. At one time, people’s sense of identity was largely local – their town, their neighborhood, their block. But as the world flattens and the flow of information, goods, and services becomes ever-less constrained by geography, people start to think of themselves in larger terms: a Christian, a lawyer, a Republican, an X, Y, Z. These larger identities have always been present of course, but they make up an increasingly greater portion of any given individuals sense of themselves. Plunkitts’s methods relied on local ties that are increasingly less important.
Another major factor was Plunkitt’s great white whale “the curse of civil service reform.” While Plunkitt was presumably most concerned with New York’s Civil Service reform, these developments were merely reflective of larger trends in the country. For most of the nineteenth century, government administrative jobs had been distributed at the discretion of elected officials. But in 1883, Chester A. Arthur signed the Pendleton Civil Service Reform act, which provided for certain federal jobs to awarded on the bases of competitive examinations rather than by administrative fiat. Those who had gotten their jobs through competitive examinations could not subsequently be removed for “political reasons”. At the time, the act covered only a small percentage of the executive. But, in a true stroke of genius, the act was written with a “ratchet provision” which allowed the president to add positions to those covered under the Pendleton act. So, if your side was likely to lose the election, you could add all your appointees to the rolls right before leaving office, and your successor couldn’t remove them. After enough iterations of this process, about 90% of the civil service was covered under the act. While the Pendleton act only covered federal civil service jobs, a similar process seems to have taken place within various states.
From the perspective of the twenty-first century, one is half-tempted to ask: do you want the deep state? Because this is how you get the deep state. In the process of enshrining their ideals in law, the professional-managerial class of the day created the legal basis for entrenched bureaucracies to pursue their collective interests even in the face of opposition from the nominal chief executive. Debates about the role of meritocracy aside, Civil Service reform went a long way towards eliminating the middle ranks of the machines, those on whom men like Plunkitt relied.
The bureaucratization of the civil service also contributed to the ballooning of the administrative law sector, which further eroded the ability of elected officials to actually make a difference in the lives of their constituents. Remember when Plunkitt said that local charities would get around to doing something just about the time that a family was starved to death? One of the services machine politicians provided their constituents was the ability to apply pressure on the machinery of the state. If, say, you felt that your property had been unfairly seized, or you had been denied something you were owed, or you had been inconvenienced in some way by The Man, you could turn to your ward heeler, who in turn could bring the matter to your local elected representative. Nowadays – you’d hire a lawyer.
America is almost unique in the extent to which the actions of executive agencies are dictated by lawsuits or the fear of lawsuits. The Equal Protection Clause means effectively, anyone can sue a government agency for virtually anything. The most well-known application of this on the Motte is probably the collection of legal decisions arising from civil rights law which has contributed to the institutionalization of progressive tendencies throughout the public and private sector. This is correlated with explosive growth in the legal profession. In 1960, there was roughly one lawyer for 627 people in the country. By 1987, there was one lawyer for every 354 people. Today, there is roughly one lawyer for every 250 people. Notably, some 30% of Representatives and 51% of Senators have law degrees. To a large extent, the politician as advocate has been replaced by the lawyer as advocate. Instead of being compensated by votes, today’s advocates are compensated in publicity and cash. Rule of law becomes rule of lawyers.
All that said, were the machine politicians so bad? I’ll admit that I am somewhat tempted to romanticize Plunkitt and all his spiritual kin, from Boss Tweed to Enoch L Johnson. Undoubtedly they were self-interested, but no more than any other politician. They made it their business to know their constituents and to provide something meaningful to them. Above all, they seemed to have been accessible. They gave their constituents a sense of agency, a feeling that there was something they could do and someone they could turn to when things went wrong or when they were being pushed around. How many of us can say the same? If, for example, my local police department confiscated my property in a civil forfeiture case, I would have no choice but to pursue a costly and time-consuming remedy through the courts. I’m fortunate enough that I could probably afford it; many others couldn’t.
At the same time, I’ve already discussed the major limitation of such political machines: they simply don’t scale. In the savage war of all against all, scale is everything. The big fish eat the little fish and organizations – political, economic, or social – which can more effectively mobilize greater resources usually out compete the smaller ones. Even if such an arrangement could survive, I expect that in time it would lose the qualities that make it appealing. A political machine would eventually become vulnerable to the same oligarchical dynamics as every other political system, and the machine politicians as detached and self-absorbed as every other elite.
Join me, then, in raising a glass to Plunkitt and all his tribe. Like Haast’s eagle, the woolly mammoth, or the horse nomads who conquered half the world, they were magnificent in their day. But their day has passed. The world has changed; I do not think we will see his kind again, for better or for worse.
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This weekly roundup thread is intended for all culture war posts. 'Culture war' is vaguely defined, but it basically means controversial issues that fall along set tribal lines. Arguments over culture war issues generate a lot of heat and little light, and few deeply entrenched people ever change their minds. This thread is for voicing opinions and analyzing the state of the discussion while trying to optimize for light over heat.
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As of late I've really lost any deep interest in any culture war issues. I still enjoy talking about them, but even the 'actually important' matters like Trump's trials and possible re-election or the latest Supreme Court cases or the roiling racial tensions of the current era seem to be sideshows at best compared to the two significant developments which stand to have greater impact than almost all other matters combined:
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Humanity is seemingly a hop, skip, and/or jump away from emergence of true AGI.
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Humanity is also locked into a demographic decline that will eventually disrupt the stable global order and world economy. No solutions tried so far have worked or even shown promise. It may be too late for such solutions to prevent the decline.
I do conserve some amount of interest for the chance that SpaceX is going to jump start industry in low earth orbit, and for longevity/anti-aging science which seems poised for some large leaps. Yet, the issues of declining human population and its downstream effect on globalization as well as the potential for human level machine intelligence seem to utterly overshadow almost any other issue we could discuss, short of World War III or the appearance of another pandemic.
And these topics are getting mainstream attention as well. There's finally space to discuss the topics of smarter-than-human AI and less-fertile-than-panda humans in less niche forums and actual news stories that start raising questions.
I recently read the Situational Awareness report by Leopold Aschenbrenner, which is a matter-of-fact update on where things absolutely seem to be heading if straight lines continue to be straight for the next few years. I find it convincing if not compelling, but the argument that we might hit AGI around 2027 (with large error bars) no longer appears absurd. This is the first time I've read a decent attempt at extrapolating out when we could actually expect to encounter the "oh shit" moment when a computer is clearly able to outperform humans not just in limited domains, but across the board.
As for the collapsed birthrates, Peter Zeihan has been the most 'level-headed' of the prognosticators here. Once again, I find it fairly convincing, but also compelling that as we end up with far too few working-age, productive citizens trying to hold up civilization as the older generations age into retirement and switch to full-time consumption. Once again you only have to believe that straight lines will keep going straight to believe that this outcome is approaching in the near future years. The full argument is more complex.
The one thing that tickles me, however, is how these two 'inevitable' results are intrinsically related! AI + robotics offers a handy method to boost productivity even as your population ages. On the negative side, only a highly wealthy, productive, educated, and globalized civilization can produce the high technology that enables current AI advances. The Aschenbrenner report up there unironically expects that 100's of millions of chips will be brought online and that global electricity production will increase by 10% before 2030ish. Anything that might interrupt chip production puts a kink in these AGI timelines. If demographic changes have as much of an impact as Zeihan suggests, it could push them back beyond the current century unless there's another route to producing all the compute and power the training runs will require.
So I find myself staring at the lines representing the increasing size of LLMs, the increasing amount of compute being deployed, the increasing funding being thrown at AI companies and chip manufacturers, and the increasing "performance" of the resultant models and then staring at the lines that represent plummeting birthrates in developed countries, and a decrease in the working age population, and thus the decrease in economic productivity that will likely result. Add on the difficulty of maintaining a peaceful, globalized economy under these constraints.
And it sure seems like the entire future of humanity hinges on which of these lines hits a particular inflection point first. And I sure as shit don't know which one it'll be.
I'd condense the premises of my position thusly:
Energy Production and High-end computer chip production are necessary inputs to achieving AGI on any timeline whatsoever. Both are extremely susceptible to demographic collapse and de-globalization. If significant deglobalization of trade occurs, there is no way any country will have the capacity to produce enough chips and energy to achieve AGI.
and
Human-level AGI that can perform any task that humans can will resolve almost any issues posed by demographic decline in terms of economic productivity and maintaining a globalized, civilized world.
Or more succinctly: If deglobalization arrives first, we won't achieve AGI. If AGI arrives first, deglobalization will be obviated.
Peter Zeihan argues that AI won't prevent the chaos. As for AGI prophets, I have rarely, in fact almost never, have seen decreasing population levels as a variable in their calculation of AI timelines.
The sense this gives me is that the AGI guys don't seem to include demographic collapse as an extant risk to AGI timelines in their model of the world. Yes they account for like interruption to chip manufacturing as a potential problem, but not accounting for this coming about due to not enough babies. And those worrying about demographic collapse discount the odds of AGI arriving in time to prevent the coming chaos.
So I find myself constantly waffling between the expectation that we'll see a new industrial revolution as AI tech creates a productivity boom (before it kills us all or whatever), and the expectation that the entire global economy will slowly tear apart at the seams and we see the return to lower tech levels out of necessity. How can I fine tune my prediction when the outcomes are so divergent in nature?
And more importantly, how can I arrange my financial bets so as to hedge against the major downsides of either outcome?
Also, yes, I'm discounting the arguments about Superintelligence altogether, and assuming that we'll have some period of time where the AI is useful and friendly before becoming far too intelligent to be controlled which lets us enjoy the benefits of the tech. I do not believe this assumption, but it is necessary for me to have any discourse about AGI at without falling on the issue of possible human extinction.
Transnational Thursday is a thread for people to discuss international news, foreign policy or international relations history. Feel free as well to drop in with coverage of countries you’re interested in, talk about ongoing dynamics like the wars in Israel or Ukraine, or even just whatever you’re reading.
Let me tell you a tale, young private, of my time after the service. Back in civilian land, a stranger to my own people. I got drunk for the better part of a decade. You think you want this life, listen up, because there's a price to pay for those stories.
When you're a drunk, your social circle is mostly in bars. I had my local. It wasn't the nicest or the grubbiest, it wasn't a meat market. It was a quiet, steady bar with a slightly older clientele. Dark inside, even darker booths. Stamped copper ceiling, left over from a wealthier time. A bullet hole over the bar that the owner claims happened during Prohibition, but I heard was from a drive-by in 1996.
This is the Nasty, and it's right on the strip. Hamilton Street. Five blocks of bars, restaurants and coffee shops with assorted tattoo parlors, barbers, bike shops and bail offices. Just across the river, the East side. One of the worst square miles in the US. Perennial murder capital contender, desperately poor, the abandoned urban underclass in the wake of de-industrialization. There were once over a hundred factories in Saginaw. Now there's three.
That's one three hundred yard bridge away from Hamilton Street, and it's the meeting place for the three sides of town. To the south, the Mexican quarter, to the west, the Township. The business owners on Hamilton are young and old. Some old proprietors hanging on for dear life. Some new ones filled with vision and ambition. All of them with more hope than sense. The town is dying.
I'd only been in town about four years at the time, fresh out the service and living with my younger brother, also just back from Iraq. It was a rough town, but we'd come from rougher.
Down to the pub, I like to think I was an excellent patron. Quiet, polite, my tab got paid in full, the staff got tipped every time, and well. A good pub can take years to get “in” to, but it's faster if you don't stiff the staff. You join the community. Learn the rules. Follow the code. The regulars and the semi-regulars. You start to learn about people slowly, over time.
John, an ever-so-slightly aged queen holds magnificent court with his coterie of fag-hags. It's always a fun time when they're in. Dom is rich or something and is forever buying rounds. Regulars stalk him. Neil, the manager, is forty and dating a different twenty-year-old every week. Nan, the heroically ugly and cantankerous bartender. She once threatened to “slap the dipshit out of your [my] face” for calling her “ma'am”. I wasn't going to try her.
But this story is about another regular. Name of Cowboy. He only comes in early in the day or late at night. A bit shorter than average, rail thin, all corded muscle, bad ink and long scraggly goattee. Works second shift, and hustles afterward. He makes the rounds at the bars late at night, sells beef jerky. Looks sketchy as hell, comes in a ziplock bag, but it is excellent. Kind of like Cowboy.
Now, Cowboy likes a drink, and I like drinks and beef jerky and we can both smell blood on the other. He's wizened, old before his time. Hard living, no doubt. He has full dentures, teeth knocked out in a prison fight. He's been in three times, he rides for the Outlaws, has the badges, has the ink. He's on parole for another ten years. I still don't know the details.
We bond over telling scar stories, every old soldier's favorite game. The stories start funny and get dark. There's some things you can't discuss with someone who can't directly relate, and when you find someone like that, there's a context to it. You don't understand now, you may later. It takes a while. You don't kiss on the first date with a guy like that. You gotta feel it out, get comfortable. Here we were, relative nobodies to the rest of the world, a broke-dick soldier and a lifelong criminal. But within our respective tiny subcultures, we were powerful and respected elders. The reason you're listening to my stories, young private.
Three terms, three deployments, three war zones.
Let me tell you son, my stories got nothing on old Cowboy's! I mean, mine might be crazier, and happen in a more exotic location, but his were so much more traumatic. It's one thing to go into battle with the might and money of a world superpower at your back, and another to have nothing and no one but yourself, and no win but more prison time with your opponents at the other end. Dude was a hard, hard man.
Generally kept my nose clean, by infantry standards. A bit of jail here and there for fighting, public drunkenness, stealing the flags off a golf course once...nothing serious.
So anyway, back to the story. The bar. The community.
I've been drinking in this bar for four years. I'm on my third set of owners. I've been here before most of the bartenders started. Now, one of the benefits of being a trusted regular is the lock-in.
So the boys hold a lock-in without us one night, lo and behold the cash is all gone in the morning. The whole night's takings. And half the liquor. None of this is discovered until Neil opens in the afternoon, and my brother and I wander in not ten minutes later.
The place is a bit of a mess, we help clean up and start figuring out what happened. Neil checks the security footage, and there it is, plain as day. The night bartender left the keys with one of his friends to lock up, and the guy cleaned the place out. We know this guy. We know his address. Nobody can think of his name right off, but who gives a shit?
The cops are no help, they'll take a statement but don't have time to waste on a few thousand in loose cash. This is bad for the bar, but it's really bad for those of us who like lock-ins.
Cowboy turns up, we brief him on what happened and he has a suggestion. For ten percent of the lost cash, he'll go over to the kids house with a couple of his Outlaw boys, put a bit of a fright into him, get him to give back the stuff. Way faster and cheaper than the cops!
Neil calls the owner, he says handle it. Cowboy goes outside to clear it with his boss in the MC. Comes back with bad news, the club won't sanction its guys for debt collection, and apparently this counts. Plan B.
What are the odds this popped-collar fuckwit knows what a proper Outlaw cut looks like? Are we outsmarting ourselves? Dan rides, he has a cut from a veteran MC. I used to ride, but sold my bike in Cali. We get on the phone.
Raoul is sixty-four, single, alcoholic. Looks like hispanic Colonel Sanders. I think he's been drunk since the seventies. He had a tough tour in Vietnam, I met him at the Purple Heart meetings. Sweet guy, a quiet and melancholy drunk, but good humored when roused. An inveterate poon-hound. He's a good dude, and hooks us up with a bike and a convincing-looking vest.
Cowboy has to work, so Dan and I do a basic recce. Walk the street, check the alley, count the exits. Windows are small and mostly high up, he's not gonna crawl out through those most likely. Three doors, one to the garage.
Back to our apartment. We talk over the plan, the scope. We leave weapons at home, pepper spray only. We're in sketchy legal area here. We aren't committing a crime, but we're going to be on his property and not necessarily friendly. If things go sideways, we don't want to escalate any more than necessary to break contact. We can always come back with more hardware, or men.
Cowboy turns up at midnight, and we roll out into the damp, dark night. Loud. This isn't a sneak operation, this is about intimidation. The whole neighborhood is going to peek out at this little show.
Fuck, I nearly forgot how much fun motorcycles are. And how incredibly scary they are when you ride with maniacs. I'm a highway cruiser, Dan has a death wish, and Cowboy was born on a bike or something. We come down that quiet cul-de-sac like thunder, line all three bikes up with the headlights pointed at the front door. My adrenaline is off the charts, I am not that good on a bike.
Dan splits off to the back, the garage is to our right, but the external door is closed. Cowboy mounts the steps to the porch, I stay one step back and to his left. The cut is a bit loose, Raoul is a lot thicker. Three loud raps at the door. Just enough to bounce the hinges a bit, you know? Take note, young private. Your knock should loosen a screw or two. Makes a good first impression.
Fuckwit comes stumbling to the door, must have slept the day. Queasy looking. Comes out of the door! Ok, we got this, this dude is not going to be a problem. If he barricaded, we might have had a time. He's quite a bit bigger than any of us, but that won't matter now.
He's disoriented, blinded by the lights behind us. He looks for a long moment at Cowboy, then at me. He's outside, the door is behind him. He's wearing sweatpants and flip flops. We're both within four feet of him. He knows us, but not by name, and not with biker gear on. We're both holding our helmets. He turns to go back through the door, and he can see straight down the hallway through the back sliding glass door, to where Dan is standing on his patio.
Cowboy puts his hand softly on Fuckwit's shoulder.
“Put everything back in your car, and take it back to the pub. We'll follow you.”
Fuckwit looks back at me. I give him my best evil grin.
He packs his car. People are looking. It's nearly one AM now, and suddenly people are wandering up and down the sidewalks, cell phones in hand. Whatever, Nasty PD ain't crossing the river for a noise complaint. It takes an uncomfortable amount of time though. That was a lot of hooch.
The escort is a good excuse for me to fall back and tail Fuckwit's ancient Buick. Dan and Cowboy are blasting up and down the wet, empty streets, popping wheelies, Charlie Mike. We take the scenic route through the neighborhood. It's last call when we roll back into the pub.
Neil boots the stragglers, makes Fuckwit restock the liquor and bans him from the bar. The whole of Hamilton Street already knows. Cowboy gets his ten percent, Dan and I get what turns out to be quite a lot of free drinks, and a special reward. We call Raoul to come down to pick up his bike and join us for a lock-in.
And that, ladies and gentle privates, is how I got a seat at the bar.
They've torn the place down now, but somewhere there is a small brass plaque that used to be nailed in front of the back corner stool of the pub.
Excuse me, but that's my seat. Is my name on it? Yes, yes it is. Would you like to speak to the manager?
Coda:
Cowboy died last year. Mid fifties,god knows of what. My brother rode up from North Carolina for the funeral. Six hundred people joined his wake. I had to borrow a bike. Older now, softer and relatively sober. I gave up the seat for a wife and a quiet life in the Nasty burbs. Dan has two kids and travels for work. Raoul is dead, years ago. There were no women at his funeral. I still see Neil from time to time. He's still dating twenty-year-olds and managing a bar. I never saw Fuckwit again. Still don't know his name.
I sometimes go back to the old neighborhood and walk its mountainous sidewalks, check in on old neighbors. Maybe have a drink at Neil's new place. Remind the streets. We're only old, we ain't dead yet.
Be advised: this thread is not for serious in-depth discussion of weighty topics (we have a link for that), this thread is not for anything Culture War related. This thread is for Fun. You got jokes? Share 'em. You got silly questions? Ask 'em.
It's not just that my clients lie to me a lot, which will only hurt them --- it's that they're really, really bad at it.
[Originally posted on Singal-Minded]
My job as a public defender puts me in a weird place. I am my clients' zealous advocate, but I'm not their marionette. I don't just roll into court to parrot whatever my clients tell me --- I make sure I'm not re-shoveling bullshit. So for my sake and theirs, I do my homework. I corroborate. I investigate.
A significant portion of my job ironically mirrors that of a police detective. Every case I get requires me to deploy a microscope and retrace the cops' steps to see if they fucked up somehow (spoiler: they haven't). Sometimes I go beyond what the cops did to collect my own evidence and track down my own witnesses.
All this puts some of my clients of the guilty persuasion in a bind. Sure, they don't want me sitting on my ass doing nothing for their case, but they also can't have me snooping around on my own too much. . . because who knows what I might find? So they take steps to surreptitiously install guardrails around my scrutiny, hoping I won't notice.
You might wonder why any chicanery from my clients is warranted. After all, am I not professionally obligated to strictly maintain client confidentiality? It's true, a client can show me where they buried their dozen murder victims and I wouldn't be allowed to tell a soul, even if an innocent person is sitting in prison for their crimes. Part of my clients' clammed-up demeanors rests on a deluded notion that I won't fight as hard for their cases unless I am infatuated by their innocence. Perhaps they don't realize that representing the guilty is the overwhelmingly banal reality of my job.[1] More importantly, it's myopic to forget that judges, prosecutors, and jurors want to see proof, not just emphatic assurances on the matter.
But clients still lie to me --- exclusively to their own detriment.
Marcel was not allowed to possess a firearm. And yet mysteriously, when the police arrested him --- the details are way too complicated to explain, even by my standards --- in his sister's vehicle, they found a pistol under the passenger seat.
"The gun is not mine. I don't even like guns. I'm actually scared of guns." He told me this through the jail plexiglass as I flipped through his remarkable résumé of gun-related crimes. Marcel spent our entire first meeting proselytizing his innocence to me. Over the next half hour he went on a genealogy world tour, swearing up and down on the lives of various immediate and extended members of his family that he never ever ever touched guns.
I was confused why he perseverated so much, but I just nodded along as part of my standard early precarious effort to build rapport with a new (and likely volatile) client. What he was telling me wasn't completely implausible --- sometimes people are indeed caught with contraband that isn't theirs --- but there was nothing I could do with his information at that early stage. Maybe he thought if he could win me over as a convert, I'd then ask for the case to be dismissed on the "he says it's not his" precedent.
Weeks later, I got the first batch of discovery. I perused the photographs that documented the meticulous search of his sister's car. I saw the pistol glistening beneath the camera flash, nestled among some CDs and a layer of Cheetos crumbs. And on the pistol itself, a sight to behold: to this day the clearest, most legible, most unobstructed fingerprints I have ever seen in my legal life. If you looked closely enough, the whorls spelled out his name and Social Security number.
Public defenders are entitled to ask the court for money to pay for private investigators, digital forensic specialists, fingerprint examiners, or whatever else is needed to ensure a defendant in a criminal case is provided with his constitutionally guaranteed legal bulwark. The photographed prints here were so apparent that an examiner could easily rely on the photos alone to make a comparison.
Marcel had earned himself some trolling from me. I went back to see him at the jail, faked as much enthusiasm as I could muster, and declared, "Good news! They found fingerprints on the gun!" He stared at me stunned and confused, so I continued.
"Well, when we first met, you told me that you never touched the gun," I reminded him with an encouraging smile. "Obviously you wouldn't lie to your own lawyer, and so what I can do is get a fingerprint expert to come to the jail, take your prints, then do a comparison on the gun itself. Since you never touched the gun, the prints won't be a match! This whole case will get dismissed, and we can put all this behind you!"[2]
He was still reeling but realized I was waiting for a response. "You. . . don't need to do that," he muttered. I had the confirmation I was looking for, but I pressed him while maintaining the facade of earnest congeniality.
"But why not?" I sang in staccato, smile wide. "You told me. That. You. Never. Touch any guns."
Turned out Marcel might have accidentally touched the gun. So his prints could be on it. I had made my point, so I dropped the act. I explained to Marcel that the only thing lying to me accomplishes is to slow things down and worsen his own prospects --- how could I pursue any potentially helpful leads for his defense when I couldn't be sure I wasn't about to bumble into an incriminating revelation?
Marcel nodded sagely and claimed to understand, but he went on to lie to me many more times over the next two years that I remained his attorney. Marcel has and will spend the majority of his adult life in prison --- not necessarily because he lied to me but that certainly didn't help.
My first meeting with Kyle was useless. He insisted throughout that it wasn't him, that he wasn't even there. Now, personally speaking, if several witnesses claimed to have seen someone who looks like me, in my car, with my girlfriend in the front seat, commit a drive-by shooting in broad daylight, I would summon slightly more curiosity about who this apparent doppelganger might be. But Kyle gave me no leads, pantomiming an internal agony about not wanting to be a snitch, clutching at his stomach as if the mere thought was physically unbearable.
His tune eventually changed. "I need you to tell the prosecutor who was driving my car," he said."His name is Richie Bottoms." If the name hadn't given it away, I already knew where this was going,[3] and I was excited for the coming entertainment. I pretended to be enthused by his revelation, and let Kyle know that I had a "really great" investigator who's phenomenal at tracking "anyone" down --- even the elusive Dick Bottoms.
Based on his reaction, that wasn't the response Kyle expected; another illustration of a myopic theory of mind (not uncommon among the interpersonally inept) incapable of simulating anything but affirmation. He tensed up momentarily, but realized that he'd already committed himself to acting out a demeanor congruent with the "innocent client responds to helpful attorney" fantasy. Yet the only excuse he could muster up in the moment was that Richie wouldn't be found because he fled to Los Angeles.
I maintained what must have been an obnoxious level of optimism, explaining how "perfect" that was because my investigator "knew lots of people" there. My job affords me few if any moments of joy, and so forgive me if I overindulged in Kyle's vexation. I'll spare you a full accounting of the myriad reasons he gave why tracking down Sir Bottoms was a lost cause. Suffice to say that in addition to being out of state, Richie had maybe fled the country; also, Richie happens to look almost identical to Kyle, but also we might not even know his real name since he went by "Arby," and no one had his phone number, et cetera. . .
Even when we moved on to other topics, Kyle couldn't let it go, interrupting whatever we were talking about to repeat warnings about how tracking down Richie was going to be a total waste of time for my investigator and me. He was palpably angry, but had no viable outlet for his frustration, and so he just stewed, stuck with his lie. I kept my poker face. It's a stark contrast to my factually innocent clients, who cannot help but drown me with leads to pursue in the hopes that any are helpful.
The whole thing reminded me of Carl Sagan's parable of the dragon in his garage as a critique of certain unprovable religious beliefs. Can I see the dragon? No, it's invisible. Can I detect its fire's thermal image? No, the fire is heatless. Can I find Dick in Los Angeles? No, because now he fled the country.
There's always some excuse --- there's always some eject button allowing my defendants to evade specific evidence demands. No matter how ridiculous.
It's banal for my clients to deny the accusations, but a special breed takes denial to the next level by waging total jihad against their accusers. It's a sort of a reverse counterpart to the Narcissist's Prayer:
If they claim I was driving during the hit-and-run, they're lying. And if they're liars, then they exaggerated their injuries. And they're exaggerating because they're after an insurance payday. And we know they're after a payday because they sued their dry cleaners in 1993. And they're framing me to get money, which is how we know they're lying.
In these clients' telling, nothing is their fault. The random bystanders who randomly drew the unlucky witness card become a convenient scapegoat. Yet these clients are so myopically overwhelmed by the desire to bounce the rubble on a witness's credibility, they don't notice how implausible their story becomes with each new clause they tape onto their fabulist's scrapbook.[4]
Sometimes clients are self-aware enough to couch their denials in innuendo. Ivan, who was accused of [redacted], was waging the same Total War approach against Cindy, a social worker at the homeless shelter where Ivan regularly stayed. Cindy was a dangerous witness --- an uninvolved, respected professional who severely undercut Ivan's alibi defense about having never left the shelter to go on his [redacted] spree.
In yet another of our jail rendezvous, Ivan expounded at length about how Cindy's testimony was invalid because, as a social worker, she would be violating HIPAA.[5] The glaze over my eyes must have gotten too obvious for me to hide, so he switched tack, shuffled through his jail-sanctioned filing system (read: pile), and slid a flyer across the table about trash cleanup day at the shelter, with a smiling cartoon trash can picking up a baby garbage bag while announcing "Pick up a little trash, talk a little trash." It's cute, but what the fuck was I supposed to be looking at? Ivan stared at me grinning and expectant, but his demeanor quickly turned into disappointment at my ongoing silence. He snatched the flyer out of my hand and jammed his finger at the "talk a little trash" clause. "This!" he shouted, and then just stared at me again. I looked at the words that meant so much to him and nothing to me and just said, "Huh?"
His disappointment transmogrified into astonished anger. "Do I have to fucking spell it out for you?" he screamed. "I thought you were the lawyer here!" We had been ping-ponging across various aspects of his case for the last hour or so and I gave up on any posturing and reiterated my ignorance at the significance of the cartoon flyer. Ivan snapped, "Cindy is encouraging people to trash talk!" For, you see, she wrote the flyer. "I'm trying to show you that she's a fucking punk! And a liar!"
I immediately understood why Ivan was so attached to remaining within the realm of innuendo. Because as soon as he gave his claim some body ("We should infer lack of credibility from individuals when they author flyers that include garbage-related puns"), he knew how much of a dumbass he would sound like out loud.
Ivan moved on from the flyer, and instead asked how to disqualify a witness "for being a liar." I tell him that's not a thing,[6] which sent him into a further rage. "I need you to be on my side here but all I hear from you is 'NO.' Why are you working for the prosecutors?"
The manipulation attempts we just cataloged were comically inept, and fell apart with far less effort than it took to create them. Slightly more polished versions of these charades are regularly deployed within the Discourse™ but they're equally hollow and just as pathetic. So those are some of my clients --- individuals who cannot rise to the level of your average internet troll.
[1] There is a kernel of an exception that is almost not worth mentioning. The Rules of Professional Conduct 3.3 obligates me with the duty of candor. I am not allowed to present evidence that I "know" is false, which encompasses witness testimony. Some jurisdictions make exceptions to this rule for defendants testifying in their criminal trial (correctly, IMO) but not all. So assuming that a client truthfully confesses to me, assuming we go to trial, assuming they decide to testify, and assuming I "know" they're going to lie, then yes, this could indeed spawn a very awkward situation where I'm forced to withdraw in the middle of proceedings.
[2] I'm told I put on a good poker face.
[3] There was no Richie Bottoms.
[4] For example, Kyle asked if it was possible to present self-defense evidence on behalf of "Richie Bottoms," just in case.
[5] Does this sound familiar to anyone?
[6] During the editing process, Jesse was skeptical of this. "Wait," he asked me in a Google Doc comment, "there's NO way for one side to prove to a judge that a witness is so untrustworthy the jurors/judge shouldn't consider their testimony?" Correct. The closest rule is disqualifying a witness as incompetent, either for being too young, severely mentally ill or mentally retarded, or too intoxicated (on the witness stand!). Credibility is up to the judge/jury to decide, and if a witness has a history of lying, then it makes for a very easy credibility impeachment. Theoretically, in extremely rare circumstances, a judge could strike the testimony of a witness or find them in contempt, but they'd have to be seriously flagrant about their lying under oath. I have never heard of this happening.
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