Category: Web/Tech
And so it begins…
Researchers, I just uploaded a prominent QJE paper on climate change economics to @RefineDotInk and I loaded my critique of the paper. I asked the AI to offer an impartial verdict. I will make this report public. A new journal could collect these into “Special Issues”.
That is from Matthew E. Kahn.
Chronicle of Higher Education interviews me on AI and education
Scott Carlson did the work (gated, but free with registration), here is one excerpt:
Cowen: …Another proposal I’ve made is that schools should let people do one class, only one class, AI-only — no professor, except a professor grades at the end. The professor grades not answers to an exam but the workflow of how the student used AI to learn the Glorious Revolution, or the Civil War, or whatever the student learned. Eventually, another AI will grade your AI log. But let’s start today with a professor, and try this for one class. I’m not sure how it will go. But especially for small schools that don’t offer that many classes, a student now can create a class in literally everything.
If it’s successful, let them try two classes. I think the students doing this on their own are more likely to figure out how to blend AI and education than we would from a distance with some kind of centralized vision. Let them experiment.
Carlson: Students should be leading the change at colleges and universities?
Cowen: Absolutely. Students know more about AI than their professors do. Some of this comes from cheating, which is unfortunate. But the notion that the professors teach the students AI — I wish. It’s not always possible. You could imagine setting up a class where the students teach the professors AI, and we give the students credit for teaching. I’m half joking, but it’s not, in fact, a crazy idea. We need to experiment along a number of fronts. I’m quite convinced no one knows how to do this. It hasn’t been done before. So again, just one class. In general, schools hate this idea. They see it as opening the door to rendering some professors obsolete.
And this:
Carlson: There are a number of students who are deeply opposed to AI. What place do they have in this university that you’re thinking about?
Cowen: I would love to have different schools with different policies. If some universities try to ban AI altogether, that’s great. Let the competitive process operate. Personally, I would bet against those schools. For a long time, I’ve been worried that within a given tier of school — big state school, community college, Ivy League — there’s not enough diversity. AI policies will force a kind of diversity on us. So I’m all for no-AI schools, even though I would not go to one.
Recommended.
Can Artificial Intelligence Forecast Treatment Effects in Economics?
I use LLMs to forecast treatment effects of research studies pre-registered on the AEA RCT Registry and the Open Science Framework. I focus on studies whose results were first publicly released in 2026 and elicit forecasts from models whose training or knowledge cut-off dates were before the relevant set of papers was released. First, LLM models extract forecasting questions from the pre-registrations or pre-analysis plans. These questions elicit forecasted treatment effects; not all resolve in the subsequent papers, but those that do can be used to consider forecast accuracy. Forecasts were elicited for 889 treatment effects from 39 papers with results disclosed in January 2026 or later, or 673 from 33 papers after screening. Forecasts were elicited in raw units but I focus on those that could be standardized and therefore compared across papers. Currently, the median absolute forecast error is 0.054 SD for the best model, the average absolute error is 0.093 SD, and the MSE is 0.0218. The AI forecasts perform substantially better than benchmarks forecasting 0 effect or forecasting the mean effect of other papers’ results in that time period. Overall, these results suggest considerable scope for forecasts to be of value in informing policy and research.
From the excellent Eva Vivalt. As I have been predicting, so much of economics (and science) is going to change, and radically. Here is Eva’s thread on the paper. Perhaps we should dispense with many of the more mundane inquiries, and as Eva notes: “But can we not shift to working on those projects that have particularly high uncertainty and upside potential?”
She is one of the best young economists in the world, and this stands some chance of being the most important paper of the year.
How Tyler Cowen and Alex Tabarrok Have Changed Their Views on AI and Where They Disagree
From the Archives-written by ChatGPT.
Both became dramatically more confident in AI’s capabilities while remaining optimistic about its economic potential. Their differences concern how benefits arrive, how seriously to weight emerging dangers, and whether AI might possess consciousness. Cowen emphasizes institutions, bottlenecks, and geopolitical competition. Tabarrok emphasizes inexpensive intelligence, scientific discovery, and—especially in his later writing—concrete safety failures.
The dated passages below trace those changes across the public archive. Quotations are the authors’ own words, including excerpts from their columns; outside material they reproduce is identified separately.
Early views on unequal gains and abundance
Cowen’s early framework grew out of chess. On March 11, 2011, he observed that stronger computers required stronger human partners:
“The required education level — for the team’s ‘wage premium’ — is ratcheted up.”
His concern was already distributional: computers could increase productivity while changing which human skills commanded a premium. He also emphasized learning how to work with computers. What I learn from chess and computers.
Tabarrok’s April 12, 2016 argument was more explicitly reassuring:
“AI will be cheap and ownership will be widespread.”
He compared AI to an increase in the world’s supply of educated people: more intelligence produces useful ideas, and competition spreads the benefits to consumers. But his forecast of comprehensive substitution was cautious—AI becoming a perfect substitute for human intelligence could be “100 years or more” away. Will the proliferation of affordable AI decimate the middle class?
Neither position was absolute. Cowen warned in February 2017 that the Industrial Revolution involved decades of painful adjustment; historical progress did not guarantee quick wage gains. Tabarrok, in October 2019, praised the argument that mediocre automation could displace workers without creating enough additional wealth to compensate. See Cowen’s historical warning and Tabarrok’s Beware the Mediocre Robots!
Rising confidence in AI capabilities from 2020 to 2025
Cowen was already optimistic about language models in July 2020. After publishing a skeptical reader’s assessment of GPT-3, he added:
“I remain bullish, but it is always worth considering other opinions.”
That attribution matters: the skeptical arguments in the post were the reader’s, not Cowen’s. The case against the import of GPT-3, July 23, 2020.
Tabarrok’s shift toward near-term transformative AI became unmistakable by March 2, 2023:
“I never expected to witness the birth of aliens.”
Later that month, he endorsed Microsoft researchers’ description of GPT-4 as exhibiting an incomplete form of general intelligence: “They are correct.” This is a substantial acceleration from his 2016 outlook, although perfect substitution for humans and early general intelligence are different thresholds. See AGI is Coming and Baby AGI is Here, March 24, 2023.
Cowen reached an especially explicit formulation on April 16, 2025, discussing o3:
“I think it is AGI, seriously.”
But he immediately separated capability from adoption:
“It still will take us a long time to use it properly.”
His growing enthusiasm about the technology did not imply an equally dramatic forecast for immediate economic growth. o3 and AGI, is April 16th AGI day?
Economic transformation and the pace of adoption
On January 6, 2025, Tabarrok wrote:
“The intelligence revolution is going to be bigger, more impactful and more wrenching than the industrial revolution.”
This is his own concluding sentence, following a quotation from Sam Altman. The Intelligence Revolution.
Cowen’s February 23, 2025 response to rapid-takeoff expectations focused on a different constraint:
“Human bottlenecks become more important, the more productive is AI.”
Energy, regulation, organizational habits, and weak human collaborators can constrain the economic value of better models. He offered an explicitly tentative estimate of an additional half percentage point of annual growth—large when compounded, but far from an immediate economic explosion. Why I think AI take-off is relatively slow.
By September 3, 2026, Tabarrok was developing the distributional implications:
“A smaller share of a much larger economy can still mean more income for labor.”
He argued that even substantial displacement could leave aggregate labor income higher, or make compensation affordable. He explicitly acknowledged painful losses for particular workers. His high-growth calculations are scenarios, not forecasts. How Much Redistribution Will AI Require?
They also converge more than their rhetoric suggests. In May 2026, Cowen rejected mass unemployment while identifying transitional hiring problems. In August 2026, Tabarrok interpreted business-survey evidence as consistent with their shared position that AI had not yet broadly reduced employment. See Cowen, May 13, and Tabarrok, August 31.
Growing attention to safety and institutional limits
Cowen stated his central position on November 19, 2023:
“I view AI as more likely to lower than to raise net existential risks.”
His reasoning was comparative: the world already faces serious dangers, and stronger science could improve our defenses. My summary views on AI existential risk.
Tabarrok’s later writing places more weight on specific vulnerabilities. On April 8, 2026, he described:
“the worrying combination that very powerful AI’s are very vulnerable.”
He was discussing cybersecurity capabilities alongside agents’ susceptibility to manipulation. AI Risks.
On September 17, 2026, he sharpened the institutional objection to assuming that beneficial science will automatically offset dangerous capabilities:
“But faster science does not automatically mean faster defense.”
This can be read as a qualification of Cowen’s earlier optimism: defensive discoveries must pass through institutions that may move slowly. It also applies Cowen’s own bottleneck logic to safety. Pandemic Preparation is AI Safety.
Cowen nevertheless supports safeguards. In August 2026, he proposed government-overseen, industry-led safety audits. In September, he opposed restrictions on recursive improvement as impractical to enforce internationally. His position is therefore selective regulation combined with continued development. See his August 27 proposal and September 22 critique.
The September 2026 exchange about AI leaders
On September 16, 2026, Tabarrok rejected the claim that executives’ safety warnings were merely marketing or regulatory capture:
“Amodei actually believes what he’s saying.”
He did not thereby establish that the warnings or proposed policies were correct. His argument concerned sincerity and incentives. What Regulatory Capture Actually Looks Like.
Cowen replied the next day:
“I agree with Alex that the standard model of regulatory capture does not apply here”
But he emphasized competition for power: each executive sincerely thinks their own company should win. For Cowen, that complicates treating their warnings as disinterested policy advice. They agree that concern is sincere; they differ over which incentives best explain behavior. The correct model of AI CEO behavior, September 17, 2026.
Consciousness and Tabarrok’s warning about infant pain
On March 17, 2025, Tabarrok’s What Did We Learn From Torturing Babies? drew a moral lesson from medicine’s historical dismissal of infant pain. He described how theories about immature brains allowed physicians to discount babies’ observable distress and perform procedures without adequate anesthesia. His warning explicitly extended to judgments about animals and AI:
“If we must err, let us err on the side of compassion.”
Tabarrok’s position is stronger than mere openness to AI consciousness: he argues for moral caution under uncertainty. The analogy does not establish that AI suffers. It challenges confidence that a theory ruling out suffering is sufficient grounds to disregard possible evidence of it.
On October 14, 2025, he went further in his own commentary following an essay by Jack Clark:
“these systems are becoming self-aware”
He distinguished self-awareness from sentience, while warning against confidently dismissing the latter. His link accompanying that warning points back to the infant-pain essay, making the connection explicit. We Turned the Light On—and the AI Looked Back.
Cowen’s position is much more categorical. On August 31, 2026, he wrote:
“I am very much opposed to the view that the AIs are sentient, or might be sentient.”
He treats AI sentience as a category error, although he accepts that human-like descriptions can sometimes help predict AI behavior. Anthropomorphizing AI?
The disagreement concerns both what AI might experience and how to act when we cannot confidently know. Tabarrok’s historical analogy places greater weight on the moral cost of mistakenly denying consciousness; Cowen expresses much greater confidence that sentience is the wrong category to apply. These are their philosophical assessments, not established findings about AI consciousness.
AI dominates human peer reviews
We show that AI-generated article reviews are now better, by every measure we could construct, than reviews written by well-compensated human experts and judged to be of unusually high quality by journal editors and authors. We then discuss how this result may inform journal policy choices, with concerning downsides as well as potential paths to increase publication speed and quality; reduce adversarial interactions; improve the experience of editors, reviewers, authors, and readers; and advance science.
By Gary King and Steven Worthington, not even using what are now the very best models, rather based on Claude Opus 4.8. The details of the dominance, as outlined in the paper, are striking. It is not close.
On my Saez and Zucman post, I find it striking that some people are complaining all of a sudden. Yet I have been offering, citing, and linking to commentary on published papers for twenty-four years now (and every single day). Sometimes it might just be a casual offhand remark from me, whether positive or negative. Typically this is simply taken in stride. It is when you pull out the bazookas, and offer oodles of high-powered analytics, more astute than human referees, and combined with the ability to pursue follow-up inquiries, that people start getting worried. Model that.
You can pick at individual criticisms from the AIs all you want, but the most damning observation is that the errors go in the ideological direction of the authors 97% of the time. That is exactly what (the human) Phil Magness said years ago, based on an extensive canvassing of their work. Basically we already knew that was true, and the AIs confirmed the human estimate.
On our side, we have taken steps, most of all the new MR archive, to make AI analysis of our work easier. Soon you will be able to read on MR what the AIs think are our worst posts, respectively (Alex has prepared this).
In the meantime, I encourage the NSF, prize committees, universities, tenure committees, journals, and so on to do as many of their own AI-enabled audits as possible. So far the world is not jumping at this opportunity. You can model that too.
Errors in the research of Emmanuel Saez and Gabriel Zucman
138 papers audited
2,035 problems found
147 serious enough to change a headline number
69% of papers have at least one
97% of major and moderate errors with a clear direction overstate the paper’s claim
And on the method:
Two independent audits of each paper. One by Claude Opus 5.5, one by OpenAI’s GPT-6 Astra, neither seeing the other’s work. Each obtained the paper, its appendices and its replication data, recomputed the headline numbers from the authors’ own files and, where possible, from the original sources (tax statistics, national accounts, Federal Reserve and survey data), checked that cited sources say what is claimed, and probed every assumption the conclusions rest on.
Then an adjudicator. A third agent re-verified every claimed problem, tried to refute each one, discarded those that did not survive, searched for problems both auditors missed, and graded what remained by how far it undermines the paper’s own headline claim: major changes a core number materially, moderate affects secondary results or a core result modestly, minor is a presentation or arithmetic slip. Confidence is graded separately.
There is much, much more at the link, which goes through the research paper by paper, and with further sustained analyses behind the links. The survey is both extensive and thorough. (And I thank an anonymous MR reader for this work.)
I am not even sure what to say at this point. We need a radical revaluation of this work, and of the associated policy arguments. More generally, this is only a single step in the story of how AI is going to revise economic science, and indeed past research more generally. As I stress in many of my recent talks, there will be significant gains and losses in status, with the losses often outweighing the gains. I see this as one of those cases.
How is AI shaping economic research?
Does generative AI narrow or widen productivity gaps in research? We study the distributional effects of AI on research productivity in economics using evidence from three sources: NBER working-paper and publication records, text-based measures of LLM-assisted writing, and an original survey of 227 academic economists measuring AI use across research tasks. We first show that productivity gaps among NBER authors narrowed following ChatGPT’s release: authors without research assistants (RAs) published a larger share of papers in top-tier journals, while authors from smaller teams gained in working-paper output and publication speed. We then use the survey to examine how individual AI use affects research productivity. We find weak evidence that AI affects working-paper output on average, but there is substantial heterogeneity. Triple-difference estimates show that greater AI exposure narrows the output gap between researchers with and without RAs but widens the gap between senior and junior researchers. Selection into AI use follows a different pattern: junior researchers use AI more intensively and are more likely to use agentic tools. Thus, these differential productivity effects reflect differences in the returns to AI rather than selection into AI use. A model with diminishing returns to execution, increasing returns to conceptual capacity, and complementarity between the two explains how AI can alleviate disadvantages in research support while amplifying the returns to accumulated expertise. Over time, however, early convergence may give way to divergence as execution constraints recede and AI’s conceptual capabilities improve.
That is from Tianyu Jiang, who is this year a job market candidate from the University of Michigan. The paper is co-authored with Yuqing (Claire) Wang.
AI hacking risks
That is the topic of my latest Free Press column (now there are gift links!), here is one excerpt, noting this is a worst-case scenario not a prediction:
Yet current systems are likely to prove more robust than that suggests. For one thing, they use a variety of different methods of protection, including public key cryptography, AES, and cryptographic hash functions, the latter not qualifying as encryption per se. While it is possible that all could fall at the same time, that seems unlikely.
The more likely scenario, assuming this comes to pass at all, is that public key encryption would become vulnerable first, but the American economy would shift toward the more secure forms of communication, most of all those that have secure cryptographic keys already established. We would also work harder to substitute into the “post-quantum” methods of encryption that are being developed and already are being used in browsers.
The transition would be very costly, but the end result would not be total insecurity. Furthermore, today’s major financial institutions already have invested in various backup systems, including offline security methods and alternative payment-processing channels. Smaller businesses would face more serious problems of transition, although they also would be lesser targets in the first place. As for you the individual, you might have to show up in person at your bank to make a major transaction, and that requirement, imposed collectively, would strain our systems to the utmost.
A second layer of protection is that the new math techniques likely would not be available to everyone. They might require technical sophistication and a willingness to break the law in some very serious ways and also risk hacking retaliation. In addition, many math breakthroughs simply shave down the safety margins on systems, rather than breaking them altogether, and in those cases the criminals still might need a lot of computing power at considerable expense.
That situation is hardly the same as a complete and sudden “open season” on all methods of cryptography. Furthermore, some of the wrongdoers in this position, even assuming they felt they could get away with their crimes, might prefer to siphon off money slowly, rather than bring down the entire financial system, which could hurt their own net wealth as well.
A further source of protection is that the math breakthroughs might come in stages, rather than all at once. For an example from another part of math, OpenAI has proven a quasi-Riemann hypothesis, and Terry Tao told me recently that this may open the door to solving the Riemann hypothesis proper. We now have advance notice that the Riemann hypothesis may be proven (or less likely, disproven). In similar fashion, intermediate advances may signal that cryptographic protections are vulnerable. We thus would have some time to adjust…
The most likely scenarios would play out in terms of probabilities, as people deal with risk before any large-scale hacking attack would come. Right now the chance of public key cryptography breaking down is on the radar screens of a relatively small number of people. Imagine that some advances in math occur, and the more sophisticated market participants start seeing something like a 10 percent chance that some forms of encryption might be vulnerable. Perhaps without much fanfare, some people would start selling their bitcoin, or they might wire their money to more secure banks, or perhaps they would hold more gold and short the market. Some hackers would invest more in the activity, including hacks without advanced AI, under the philosophy of “harvest now, decrypt later,” pulling in classified or confidential materials that previously were not worth capturing. Overall, it would be like the early days of Covid, when major bad news was pending but only a few individuals were acting on it. That state of affairs could persist for some period of time.
In any case it would not be good.
And this: “If you have thoughts on my latest column, or questions about AI or anything else, join me for an AMA (Ask Me Anything) this week on The Free Press Forum.
Paid subscribers can submit questions now. Not a subscriber? For a limited time, you can get 20% off at thefp.com/tyler.”
NYT debate on the rules for AI companies
Emily Bazelon was the moderator, there were five of us in the symposium. Excerpt from me:
Bores: Do you think the federal government pre-empting the states is consistent with democracy?
Cowen: Absolutely. We have federal laws that pre-empt state lawmaking in many other areas. Courts have upheld it many, many times.
Bores: OK, but is it pro-democracy, in giving people a say?
Cowen: It is pro-American Constitution.
Most of the panel disagreed with most of my points.
Why are cybersecurity losses not worse?
Here’s the first part of my theory: cybercriminals are not the organized geniuses we implicitly imagine. Anyone building a criminal gang has to find candidates willing to forgo more stable opportunities in the formal economy. My modeling estimated that something like 720,000 people are involved in the online criminal economy, 90% of them in scam and fraud call-center type operations, some of them human-trafficked. Of the 76k or so actual hackers, the vast majority work for governments. Of the remaining few thousand criminal hackers, most work in ransomware. Some demographic estimates of these workforces are below.
Criminal organizations need to base themselves in middle and lower income countries, where they can pay less and hire people who have few formal-economy options. For scams and fraud, picture an illegal call center building in a small city in Nigeria. For ransomware gangs, picture kids working in the informal economy in Belarus. I think folks in the AI safety and cyber world sometimes imagine adversaries as their Waluigi twins, implicit MIT graduates; in fact, in cybercrime we’re dealing with a lower-skill demographic. The chart below estimates attacker wages relative to default economy wages…
Here is more from Joshua Saxe, very good throughoiut. There are bottlenecks on the down side too…
Cybersecurity ETF
Joe Weisenthal: “The Red Line is the day that Anthropic introduced Mythos”
Podcast with Rabbi David Wolpe
Here is their episode summary:
Economist Tyler Cowen joins David Wolpe for a conversation about AI, religion, and what makes us human. Tyler makes the case that AI’s biggest impact may be on status: who gains it, who loses it, and what happens to experts like rabbis when anyone can ask ChatGPT all their questions. He also suggests that losing our monopoly on intelligence may make us feel more unique as humans, rather than less.
You can listen here.
Will bystander economies be able to finance their governments?
From Jesús Fernández-Villaverde:
Will AI poison the public finances of many modern states? There is a real risk that it will.
Yesterday, I circulated a new paper, “Saving for the Machines,” which makes a simple point. Building the AI transformation will require enormous investment. The only way to induce agents to save for that investment is through a large increase in the real interest rate. For instance, in my benchmark scenario, the ten-year real yield jumps from 5.1% to 13.6% on impact.
But that means government borrowing will become much more expensive. Here is a back-of-the-envelope calculation. Take a government that owes 100% of GDP in 2025 (a typical case in Western Europe) and runs a balanced primary budget from then on (not so typical but never mind; I want to build an optimistic scenario). That means the debt ratio changes only with the gap between the real interest rate and growth.
I input the interest rate and the growth rate for my benchmark transition to full automation, and I roll over the debt at the short rate. I consider two economies. The first economy is an AI adopter, for example, the U.S., which experiences an AI boom at home. The government pays the new interest rate but also enjoys faster growth.
The second economy is a bystander, for example, Spain. This economy lives in an integrated financial market but does not adopt AI (this is an extreme case of zero adoption, just to make the exercise transparent): it pays the same world interest rate and continues growing at its old 1.6% trend.
Let’s first consider the adopter. With an intertemporal elasticity of substitution of one-half, the Euler equation implies that consumption growth is about half the real interest rate. Hence, the gap between the interest rate and growth does not close during the AI-driven growth boom. Instead, it widens. For example, with a unit elasticity of substitution among tasks, the real rate rises from 5.1% to 42% by 2040, while growth increases only from 1.6% to 22%. Debt doubles by 2038, reaches 296% of GDP in 2040, and 907% in 2045. To hold the ratio at 100%, the government needs a primary surplus of 10% of GDP in 2035 and 24% from 2045 onward. With a low elasticity of substitution of 0.2, the numbers are milder, but the pattern is the same: 204% in 2040, 397% in 2045, and a stabilizing surplus of 13% of GDP.
But the adopter has a way out. The capital share goes to one, so net capital income is about 88% of GDP, and a flat tax on it can cover the surplus. A 36% tax on net capital income holds the debt at 100% of GDP in the limit with unit elasticity, and a 20% tax does so with strong complementarity across tasks. The tax is above the gap because it slows growth, from 21.9% to 13.6% with unit elasticity and from 11.7% to 9.1% with an elasticity of 0.2, while it does nothing to the rate the Treasury pays, which technology pins at the machines’ net return. Painful, but the boom survives and the adopter can pay its debt.
The grim news is that the bystander economy has no way out. Its capital income is still a third of GDP, which grows at 1.6%, and the surplus it needs is 11% of GDP in 2030, 26% in 2035, and 44% forever with unit elasticity (18% and 24% with an elasticity of 0.2). On a balanced primary budget, its debt doubles by 2033 and triples by 2035. No tax system raises that.
Two more bad pieces of news for the bystander. First, long yields jump on impact, so a Treasury that issues ten-year debt pays more, and sooner, than rolling over at the short rate assumes: it is the same bill but front-loaded. Second, the bystander’s own capital flows to the machines abroad, so its GDP and tax base fall below trend. One relief is that households can save for other people’s machines and get rich on the returns, which the bystander’s government can tax to some extent. This back-of-the-envelope calculation is a benchmark, not a forecast of what is to come. But the arithmetic is simple, and it runs counter to the hope that an AI-fueled growth boom will take care of public debt, because growth comes with an interest rate that is twice as high. Not a pretty future for most of Western Europe. Due to AI, the choice is between default, inflation, or, most likely, financial repression: capital controls that keep domestic savers from investing in machines abroad.
I am somewhat more optimistic than this. The assumption of zero Spanish adoption already is noted, but also ideas are public goods, and Spain can benefit from American (and Chinese) AI, and the resulting scientific advances. The long-run supply of capital also might be quite elastic, we will see.
A simple point about the intertemporal regulation of AI
At some point AI will be more regulated than it is today. Furthermore, I believe that most parts of the American economy, once they are regulated, end up overregulated.
Thus it is likely that the AI sector will, at some point, end up overregulated.
Given that reality, perhaps the AI sector should be underregulated today. After all, overregulation will have decades, maybe centuries, to catch up and even the ledger.
Many regulatory issues can be considered within this framework. You can think that AI is “underregulated today”, in the static sense of that term, and still not favor additional regulation right now. You might prefer to keep it underregulated for some while to come.
One reason I do not love AI writing, or even AI-assisted writing
Of course I read to absorb the content, but I also am in a position to meet many authors, have contact with them, and spend time with them. So reading anything also is a potential option on those future connections. But when I am reading an AI piece, the value of that option comes pretty close to zero.