Down goes the Dow

mark another indicator that stopped working in the post-pandemic economy. So many of these. It really is different this time.

Needless to say, there was no recession.

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Puru Saxena
@saxena_puru
Real GDI has never contracted YoY outside a recession
Divergences between Real GDI and Real GDP (which should be the same) have only been seen just before onset of prior recessions, with Real GDI turning out to be the correct reading.
Real GDI is now contracting YoY.

8:09 PM · Jan 6, 2024
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58.9K
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meeeeeeelllllllttttttttt uuuuuuuuuuuuupppppppppp

settle down

Same. I sold off about 10% of my margin about 6 weeks ago. So far, not the right move, but we'll see. Who do you use for margin?

Glad to see this up day, don't get me wrong, but it won't even make up for yesterday's downturn, at least in my case.

Longer tern, yeah, would tend to agree.

margin? good lord man. No. No margin. Big margin of error maybe.

yeah this rebound in small caps is great for me but it's come at the cost of some tech names. Still, tech up this week. maybe a percent.

But small caps up 5%ish.

Earnings Scorecard: For Q2 2024 (with 5% of S&P 500 companies reporting actual results), 81% of S&P 500 companies have reported a positive EPS surprise and 56% of S&P 500 companies have reported a positive revenue surprise.

ABSTRACT:

This paper evaluates claims about large macroeconomic implications of new advances in AI. It starts from a task-based model of AI’s effects, working through automation and task complementarities. So long as AI’s microeconomic effects are driven by cost savings/ productivity improvements at the task level, its macroeconomic consequences will be given by a version of Hulten’s theorem: GDP and aggregate productivity gains can be estimated by what fraction of tasks are impacted and average task-level cost savings. Using existing estimates on exposure to AI and productivity improvements at the task level, these macroeconomic effects appear nontrivial but modest—no more than a 0.66%increase in total factor productivity (TFP) over 10 years. The paper then argues that even these estimates could be exaggerated, because early evidence is from easy-to-learn tasks, whereas some of the future effects will come from hard-to-learn tasks, where there are many context-dependent factors affecting decision-making and no objective outcome measures from which to learn successful performance. Consequently, predicted TFP gains over the next 10 years are even more modest and are predicted to be less than 0.53%. I also explore AI’s wage and inequality effects. I show theoretically that even when AI improves the productivity of low-skill workers in certain tasks (without creating new tasks for them), this may increase rather than reduce inequality. Empirically, I find that AI advances are unlikely to increase inequality as much as previous automation technologies because their impact is more equally distributed across demographic groups, but there is also no evidence that AI will reduce labor income inequality. Instead, AI is predicted to widen the gap between capital and labor income. Finally, some of the new tasks created by AI may have negative social value (such as design of algorithms for online manipulation), and I discuss how to incorporate the macroeconomic effects of new tasks that may have negative social value.

https://www.nber.org/system/files/working_papers/w32487/w32487.pdf

“New tasks created with AI can more significantly boost productivity. However, some of the new AI-generated tasks are manipulative and may have negative social value, such as deepfakes, misleading digital advertisements, addictive social media or AI-powered malicious computer attacks. While it is difficult to put numbers on good and bad new tasks, based on recent research I suggest that the negative effects from new bad tasks could be sizable. I make a very speculative attempt using numbers on the negative welfare effects of social media from a recent paper by Bursztyn et al. (2023). These authors find that consumers have positive willingness to pay for using social media (in particular Instagram and TikTok) when others are using it, but they would prefer that neither themselves nor others use it. Roughly speaking, their estimates imply that revenue can increase by about $53 per user-month, but this has a negative impact on total GDP/welfare equivalent to $19 per user-month. Combining these numbers with an estimate of the fraction of activities that may generate negative social value (in practice, revenues from social media and spending on attack-defense arms races in IT security), I suggest that with more intensive use of AI, it is possible to have nontrivial increases in GDP. For example, AI may appear to increase GDP by 2%, while in reality reducing welfare by −0.72% (in consumption equivalent units).”

“‘Hard tasks’ typically do not have a simple mapping between action and desired outcome. In hard problems, what leads to the desired outcome in a given problem is typically not known and strongly depends on contextual factors, or the number of relevant contexts may be vast, or new problem-solving may be required. Additionally, there is typically not enough information for the AI system to learn or it is unclear exactly what needs to be learned. Diagnosing the cause of a persistent cough and proposing a course of treatment is a hard problem. There are many complex interactions between past events that may be the cause of the lingering cough and many rare conditions that should be considered. Moreover, there is no large, well-curated data set of successful diagnoses and cures. In hard tasks, AI models can still learn from human decision-makers, but because there is no clear metric of success, identifying and learning from workers with the highest level of expertise will not be straightforward either. As a result, there will be a tendency for the performance of AI models to be similar to the average performance of human decision-makers, limiting the potential for large productivity improvements and cost savings.”

Okay, so rather than AI as autonomous, I've heard it said that AI would be a very good supplement and tool for human decision makers, and so good in fact, that it would greatly expand their effectiveness and capabilities, and add great value therein. AI could be an indispensible mechanixm of data collection, rough formulations, rough draft formulation, and performer of limited tasks, all of which would be compiled, edited, and put into its final format by a production oriented human.

“My assessment is that there are indeed much bigger gains to be had from generative AI, which is a promising technology, but these gains will remain elusive unless there is a funda- mental reorientation of the industry, including perhaps a major change in the architecture of the most common generative AI models, such as the LLMs, in order to focus on reliable information that can increase the marginal productivity of different kinds of workers, rather than prioritizing the development of general human-like conversational tools. The general- purpose nature of the current approach to generative AI could be ill-suited for providing such reliable information. To put it simply, it remains an open question whether we need foundation models (or the current kind of LLMs) that can engage in human-like conver- sations and write Shakespearean sonnets if what we want is reliable information useful for educators, healthcare professionals, electricians, plumbers and other craft workers.”

* or as my man, Iggy Pop and The Stooges says, "Look out honey because I';m using technology. I ain't got to make no apology."

Love those guys.

Interesting. That seems to be along the lines of what I was saying earlier.

in their view, the stooges stand in direct opposition to the Big Bopper, who said in a famously cryptic aside, probably about generative AI:

What's that, honey?
Pick you up at eight, and don't be late?
But, baby, I ain't got no money, honey

nope

Game of Trades
@GameofTrades_
Existing home sales have been collapsing
And point to a sharp contraction in S&P 500 earnings

12:40 PM · Jul 14, 2023
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32.7K
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Ok this one goes to the top of the worst prediction list.

From two years ago. We're 28% higher now, plus the dividend.

Simon Ree
@simon_ree
If you’re a long-term, long-only buy-and-holder, chances are you’re going to hate this market the next 18-24 months.
If you’re a trader, there will continue to be great opportunities but you’re going to have to fight your natural instincts to avoid getting chopped up
9:02 PM · Jul 15, 2022