[Week 7] LP Announcement by OpenAI

Our mission is to ensure that artificial general intelligence (AGI) benefits all of humanity, primarily by attempting to build safe AGI and share the benefits with the world.

We’ve [experienced](https://openai.com/blog/openai-five/) [firsthand](https://openai.com/blog/better-language-models/) that the most dramatic AI systems use the most [computational power](https://openai.com/blog/ai-and-compute/) in addition to algorithmic innovations, and decided to scale much faster than we’d planned when starting OpenAI. We’ll need to invest billions of dollars in upcoming years into large-scale cloud compute, attracting and retaining talented people, and building AI supercomputers.

We want to increase our ability to raise capital while still serving our mission, and no pre-existing legal structure we know of strikes the right balance. Our solution is to create OpenAI LP as a hybrid of a for-profit and nonprofit—which we are calling a “capped-profit” company.

The fundamental idea of OpenAI LP is that investors and employees can get a capped return if we succeed at our mission, which allows us to raise investment capital and attract employees with startup-like equity. But any returns beyond that amount—and if we are successful, we expect to generate orders of magnitude more value than we’d owe to people who invest in or work at OpenAI LP—are owned by the original OpenAI Nonprofit entity.

Going forward (in this post and elsewhere), “OpenAI” refers to OpenAI LP (which now employs most of our staff), and the original entity is referred to as “OpenAI Nonprofit.”

The mission comes first
-----------------------

We’ve designed OpenAI LP to put our overall mission—ensuring the creation and adoption of safe and beneficial AGI—ahead of generating returns for investors.

The mission comes first even with respect to OpenAI LP’s structure. While we are hopeful that what we describe below will work until our mission is complete, we may update our implementation as the world changes. Regardless of how the world evolves, we are committed—legally and personally—to our mission.

OpenAI LP’s primary fiduciary obligation is to advance the aims of the [OpenAI Charter](/charter), and the company is controlled by OpenAI Nonprofit’s board. All investors and employees sign agreements that OpenAI LP’s obligation to the Charter always comes first, even at the expense of some or all of their financial stake.

![Purple Notice](https://images.openai.com/blob/01038e66-37de-4945-8ee7-554e2ed00300/purple-notice.svg?width=10&height=10&quality=50)Our employee and investor paperwork start with big purple boxes like this. The general partner refers to OpenAI Nonprofit (whose legal name is “OpenAI Inc”); limited partners refers to investors and employees.Only a minority of board members are allowed to hold financial stakes in the partnership at one time. Furthermore, only board members without such stakes can vote on decisions where the interests of limited partners and OpenAI Nonprofit’s mission may conflict—including any decisions about making payouts to investors and employees.

![Corporate Structure](https://images.openai.com/blob/6bd0eda5-61fe-44b5-a186-6bb1fb7624d6/corporate-structure.svg?width=10&height=10&quality=50)Another provision from our paperwork specifies that OpenAI Nonprofit retains control.As mentioned above, economic returns for investors and employees are capped (with the cap negotiated in advance on a per-limited partner basis). Any excess returns go to OpenAI Nonprofit. Our goal is to ensure that most of the value (monetary or otherwise) we create if successful benefits everyone, so we think this is an important first step. Returns for our first round of investors are capped at 100x their investment (commensurate with the risks in front of us), and we expect this multiple to be lower for future rounds as we make further progress.

What OpenAI does
----------------

Our day-to-day work is not changing. Today, we believe we can build the most value by focusing exclusively on developing new AI technologies, not commercial products. Our structure gives us flexibility for how to create a return in the long term, but we hope to figure that out only once we’ve created safe AGI.

OpenAI LP currently employs around 100 people organized into three main areas: capabilities (advancing what AI systems can do), safety (ensuring those systems are aligned with human values), and policy (ensuring appropriate governance for such systems). OpenAI Nonprofit governs OpenAI LP, runs educational programs such as [Scholars](https://openai.com/blog/openai-scholars/) and [Fellows](https://openai.com/blog/openai-fellows-interns-2019/), and hosts policy initiatives. OpenAI LP is continuing (at increased pace and scale) the development roadmap started at OpenAI Nonprofit, which has yielded breakthroughs in [reinforcement learning](https://openai.com/blog/openai-five/), [robotics](https://openai.com/blog/learning-dexterity/), and [language](https://openai.com/blog/better-language-models/).

Safety
------

We are excited by the potential for AGI to help solve planetary-scale problems in areas where humanity is failing and there is no obvious solution today. However, we are also concerned about AGI’s potential to cause rapid change, whether through machines pursuing goals misspecified by their operator, malicious humans subverting deployed systems, or an out-of-control economy that grows without resulting in improvements to human lives. As described in our [Charter](https://openai.com/charter/), we are willing to merge with a value-aligned organization (even if it means reduced or zero payouts to investors) to avoid a competitive race which would make it hard to prioritize safety.

Who’s involved
--------------

* OpenAI Nonprofit’s board consists of OpenAI LP employees Greg Brockman (Chairman & CTO), Ilya Sutskever (Chief Scientist), and Sam Altman (CEO), and non-employees Adam D’Angelo, Holden Karnofsky, Reid Hoffman, Shivon Zilis, and Tasha McCauley.[^board]
* Elon Musk left the [board of OpenAI Nonprofit](https://openai.com/blog/openai-supporters/) in February 2018 and is not formally involved with OpenAI LP. We are thankful for all his past help.
* Our investors include Reid Hoffman’s charitable foundation and Khosla Ventures, among others. We feel lucky to have mission-aligned, impact-focused, helpful investors!
*We are traveling a hard and uncertain path, but we have designed our structure to help us positively affect the world should we succeed in creating AGI—which we think will have as broad impact as the computer itself and improve healthcare, education, scientific research, and many aspects of people’s lives. If you’d like to help us make this mission a reality, we’re*[*hiring*](https://openai.com/jobs/)*:)!*

We’ve created OpenAI LP, a new “capped-profit” company that allows us to rapidly increase our investments in compute and talent while including checks and balances to actualize our mission. *The original text contained 1 footnote which was omitted from this narration.*

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[https://openai.com/blog/openai-lp](https://openai.com/blog/openai-lp?utm_source=TYPE_III_AUDIO&utm_medium=Podcast&utm_content=Source+URL+in+episode+description&utm_campaign=ai_narration)

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 Narrated by [TYPE III AUDIO](https://type3.audio/?utm_source=TYPE_III_AUDIO&utm_medium=Podcast&utm_content=Narrated+by+TYPE+III+AUDIO&utm_term=agi_sf&utm_campaign=ai_narration).


[Week 7] OpenAI Charter

This document reflects the strategy we’ve refined over the past two years, including feedback from many people internal and external to OpenAI. The timeline to AGI remains uncertain, but our Charter will guide us in acting in the best interests of humanity throughout its development.

OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome. To that end, we commit to the following principles:

Broadly distributed benefits
----------------------------

We commit to use any influence we obtain over AGI’s deployment to ensure it is used for the benefit of all, and to avoid enabling uses of AI or AGI that harm humanity or unduly concentrate power.

Our primary fiduciary duty is to humanity. We anticipate needing to marshal substantial resources to fulfill our mission, but will always diligently act to minimize conflicts of interest among our employees and stakeholders that could compromise broad benefit.

Long-term safety
----------------

We are committed to doing the research required to make AGI safe, and to driving the broad adoption of such research across the AI community.

We are concerned about late-stage AGI development becoming a competitive race without time for adequate safety precautions. Therefore, if a value-aligned, safety-conscious project comes close to building AGI before we do, we commit to stop competing with and start assisting this project. We will work out specifics in case-by-case agreements, but a typical triggering condition might be “a better-than-even chance of success in the next two years.”

Technical leadership
--------------------

To be effective at addressing AGI’s impact on society, OpenAI must be on the cutting edge of AI capabilities—policy and safety advocacy alone would be insufficient.

We believe that AI will have broad societal impact before AGI, and we’ll strive to lead in those areas that are directly aligned with our mission and expertise.

Cooperative orientation
-----------------------

We will actively cooperate with other research and policy institutions; we seek to create a global community working together to address AGI’s global challenges.

We are committed to providing public goods that help society navigate the path to AGI. Today this includes publishing most of our AI research, but we expect that safety and security concerns will reduce our traditional publishing in the future, while increasing the importance of sharing safety, policy, and standards research.

Our Charter describes the principles we use to execute on OpenAI’s mission. ---

[https://openai.com/charter](https://openai.com/charter?utm_source=TYPE_III_AUDIO&utm_medium=Podcast&utm_content=Source+URL+in+episode+description&utm_campaign=ai_narration)

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 Narrated by [TYPE III AUDIO](https://type3.audio/?utm_source=TYPE_III_AUDIO&utm_medium=Podcast&utm_content=Narrated+by+TYPE+III+AUDIO&utm_term=agi_sf&utm_campaign=ai_narration).


“[Week 7] ’12 tentative ideas for US AI policy’ by Luke Muehlhauser” by Luke Muehlhauser

Published: April 17, 2023  | by [Luke Muehlhauser](/about/team/luke-muehlhauser)
About two years ago, I [wrote](https://www.openphilanthropy.org/research/our-ai-governance-grantmaking-so-far/) that “it’s difficult to know which ‘intermediate goals’ [e.g. policy goals] we could pursue that, if achieved, would clearly increase the odds of eventual good outcomes from transformative AI.” Much has changed since then, and in this post I give an update on 12 ideas for US policy goals[[1]](#f+13628+2+1)Many of these policy options would plausibly also be good to implement in other jurisdictions, but for most of them the US is a good place to start (the US is plausibly the most important jurisdiction anyway, given the location of leading companies, and many other countries sometimes follow the … [Continue reading](#f+13628+2+1) that I tentatively think would increase the odds of good outcomes from [transformative AI](https://docs.google.com/document/d/15siOkHQAoSBl_Pu85UgEDWfmvXFotzub31ow3A11Xvo/edit).[[2]](#f+13628+2+2)For more on intermediate goals, see [Survey on intermediate goals in AI governance](https://forum.effectivealtruism.org/posts/g4fXhiJyj6tdBhuBK/survey-on-intermediate-goals-in-ai-governance).

I think the US generally over-regulates, and that most people underrate the [enormous benefits of rapid innovation](https://www.vox.com/future-perfect/2018/11/8/18052076/human-history-in-one-chart-industrial-revolution). However, when 50% of the (survey-responding) experts on a specific technology [think](https://aiimpacts.org/what-do-ml-researchers-think-about-ai-in-2022/) there is a reasonable chance it will result in outcomes that are “extremely bad (e.g. human extinction),” I think ambitious and thoughtful regulation is warranted.[[3]](#f+13628+2+3)This paragraph was added on April 18, 2023.

First, some caveats:

* These are my own tentative opinions, not Open Philanthropy’s.[[4]](#f+13628+2+4)Besides my day job at Open Philanthropy, I am also a Board member at [Anthropic](https://www.anthropic.com/), though I have no shares in the company and am not compensated by it. Again, these opinions are my own, not Anthropic’s. I might easily change my opinions in response to further analysis or further developments.
* My opinions are premised on a strategic picture similar to the one outlined in my colleague Holden Karnofsky’s [Most Important Century](https://www.cold-takes.com/most-important-century-series-roadmap/) and [Implications of…](https://www.cold-takes.com/tag/implicationsofmostimportantcentury/) posts. In other words, I think transformative AI could bring enormous benefits, but I also take full-blown existential risk from transformative AI as a plausible and urgent concern, and I am more agnostic about this risk’s likelihood, shape, and tractability than e.g. [a recent *TIME* op-ed](https://time.com/6266923/ai-eliezer-yudkowsky-open-letter-not-enough/).
* None of the policy options below have gotten sufficient scrutiny (though they have received far more scrutiny than is presented here), and there are many ways their impact could turn out — upon further analysis or upon implementation — to be net-negative, even if my basic picture of the strategic situation is right.
* To my knowledge, none of these policy ideas have been worked out in enough detail to allow for immediate implementation, but experts have begun to draft the potential details for most of them (not included here). None of these ideas are original to me.
* This post doesn’t explain much of my *reasoning* for tentatively favoring these policy options. All the options below have complicated mixtures of pros and cons, and many experts oppose (or support) each one. This post isn’t intended to (and shouldn’t) *convince* anyone. However, **in the wake of recent AI advances and discussion, many people have been asking me for these kinds of policy ideas, so I am sharing my opinions here**.
* Some of these policy options are more politically tractable than others, but, as I think we’ve seen recently, the political landscape sometimes shifts rapidly and unexpectedly.

Those caveats in hand, below are some of my current personal guesses about US policy options that would reduce existential risk from AI in expectation (in no order).[[5]](#f+13628+2+5)There are many other policy options I have purposely not mentioned here. These include:

Hardware export controls. The US has already implemented major export controls on semiconductor manufacturing equipment and high-end chips. These controls have both pros and cons from my perspective, though … [Continue reading](#f+13628+2+5)

1. **Software export controls****.** Control the export (to anyone) of “frontier AI models,” i.e. models with highly general capabilities over some threshold, or (more simply) models trained with a compute budget over some threshold (e.g. as much compute as $1 billion can buy today). This will help limit the proliferation of the models which probably pose the greatest risk. Also restrict API access in some ways, as API access can potentially be used to generate an optimized dataset sufficient to train a smaller model to reach performance similar to that of the larger model.
2. **Require hardware security features on cutting-edge chips****.** Security features on chips can be leveraged for many useful compute governance purposes, e.g. to verify compliance with export controls and domestic regulations, monitor chip activity without leaking sensitive IP, limit usage (e.g. via interconnect limits), or even intervene in an emergency (e.g. remote shutdown). These functions can be achieved via firmware updates to already-deployed chips, though some features would be more tamper-resistant if implemented on the silicon itself in future chips.
3. **Track stocks and flows of cutting-edge chips, and license big clusters.** Chips over a certain capability threshold (e.g. the one used for the October 2022 export controls) should be tracked, and a license should be required to bring together large masses of them (as required to cost-effectively train frontier models). This would improve government visibility into potentially dangerous clusters of compute. And without this, other aspects of an effective compute governance regime can be rendered moot via the use of undeclared compute.
4. **Track and require a license to develop frontier AI models****.** This would improve government visibility into potentially dangerous AI model development, and allow more control over their proliferation. Without this, other policies like the information security requirements below are hard to implement.
5. **Information security requirements****.** Require that frontier AI models be subject to extra-stringent information security protections (including cyber, physical, and personnel security), including during model training, to limit unintended proliferation of dangerous models.
6. **Testing and evaluation requirements****.** Require that frontier AI models be subject to extra-stringent safety testing and evaluation, including some evaluation by an independent auditor meeting certain criteria.[[6]](#f+13628+2+6)See e.g. p. 15-16 of the [GPT-4 system card report for an illustration.](https://cdn.openai.com/papers/gpt-4-system-card.pdf)
7. **Fund specific genres of alignment, interpretability, and model evaluation R&D****.** Note that if the genres are not specified well enough, such funding can effectively widen (rather than shrink) the gap between cutting-edge AI capabilities and available methods for alignment, interpretability, and evaluation. See e.g. [here](https://www.openphilanthropy.org/request-for-proposals-for-projects-in-ai-alignment-that-work-with-deep-learning-systems/) for one possible model.
8. **Fund defensive information security R&D**, again to help limit unintended proliferation of dangerous models. Even the broadest funding strategy would help, but there are many ways to target this funding to the development and deployment pipeline for frontier AI models.
9. **Create a narrow antitrust safe harbor for AI safety & security collaboration****.** Frontier-model developers would be more likely to collaborate usefully on AI safety and security work if such collaboration were more clearly allowed under antitrust rules. Careful scoping of the policy would be needed to retain the basic goals of antitrust policy.
10. **Require certain kinds of AI incident reporting**, similar to incident reporting requirements in other industries (e.g. aviation) or to data breach reporting requirements, and similar to some vulnerability disclosure regimes. Many incidents wouldn’t need to be reported publicly, but could be kept confidential within a regulatory body. The goal of this is to allow regulators and perhaps others to track certain kinds of harms and close-calls from AI systems, to keep track of where the dangers are and rapidly evolve mitigation mechanisms.
11. **Clarify the liability of AI developers for concrete AI harms****,** especially clear physical or financial harms, including those resulting from negligent security practices. A new framework for AI liability should in particular address the risks from frontier models carrying out actions. The goal of clear liability is to incentivize greater investment in safety, security, etc. by AI developers.
12. **Create means for rapid shutdown of large compute clusters and training runs****.** One kind of “off switch” that may be useful in an emergency is a non-networked power cutoff switch for large compute clusters. As far as I know, most datacenters don’t have this.[[7]](#f+13628+2+7)E.g. the lack of an off switch exacerbated the fire that destroyed a datacenter in Strasbourg; see section VI.2.1 – iv of [this report](https://www.igedd.developpement-durable.gouv.fr/IMG/pdf/rapport_ovh_67_vdif_cle01cf13.pdf). Remote shutdown mechanisms on chips (mentioned above) could also help, though they are vulnerable to interruption by cyberattack. Various additional options could be required for compute clusters and training runs beyond particular thresholds.

Of course, even if one agrees with some of these high-level opinions, I haven’t provided enough detail in this short post for readers to know what, exactly, to advocate for, or how to do it. If you have useful skills, networks, funding, or other resources that you might like to direct toward further developing or advocating for one or more of these policy ideas, please indicate your interest in [this short Google Form](https://forms.gle/4EZmFbxnJ8pp6Qip8). (The information you share in this form will be available to me [Luke Muehlhauser] and some other Open Philanthropy employees, but we won’t share your information beyond that without your permission.)

 Footnotes[+]Footnotes[−]

Footnotes| [1](#r+13628+2+1 "Alt+ ←") | Many of these policy options would plausibly also be good to implement in other jurisdictions, but for most of them the US is a good place to start (the US is plausibly the most important jurisdiction anyway, given the location of leading companies, and many other countries sometimes follow the US), and I know much less about politics and policymaking in other countries. |
| [2](#r+13628+2+2 "Alt+ ←") | For more on intermediate goals, see [Survey on intermediate goals in AI governance](https://forum.effectivealtruism.org/posts/g4fXhiJyj6tdBhuBK/survey-on-intermediate-goals-in-ai-governance). |
| [3](#r+13628+2+3 "Alt+ ←") | This paragraph was added on April 18, 2023. |
| [4](#r+13628+2+4 "Alt+ ←") | Besides my day job at Open Philanthropy, I am also a Board member at [Anthropic](https://www.anthropic.com/), though I have no shares in the company and am not compensated by it. Again, these opinions are my own, not Anthropic’s. |
| [5](#r+13628+2+5 "Alt+ ←") | There are many other policy options I have purposely not mentioned here. These include:* Hardware export controls. The US has already implemented [major export controls](https://en.wikipedia.org/wiki/United_States_New_Export_Controls_on_Advanced_Computing_and_Semiconductors_to_China) on semiconductor manufacturing equipment and high-end chips. These controls have both pros and cons from my perspective, though it’s worth noting that they may be a necessary complement to some of the policies I tentatively recommend in this post. For example, the controls on semiconductor manufacturing equipment help to preserve a unified supply chain to which future risk-reducing compute governance mechanisms can be applied. These hardware controls will likely need ongoing maintenance by technically sophisticated policymakers to remain effective.
* “US boosting” interventions, such as semiconductor manufacturing subsidies or AI R&D funding. One year ago I was weakly in favor of these policies, but recent analyses have nudged me into weakly expecting these interventions are net-negative given e.g. the likelihood that they shorten AI timelines. But more analysis could flip me back. “US boosting” by increasing high-skill immigration may be an exception here because it relocates rather than creates a key AI input (talent), but I’m unsure, e.g. because skilled workers may accelerate AI faster in the US than in other jurisdictions. As with all the policy opinions in this post, it depends on the magnitude and certainty of multiple effects pushing in different directions, and those figures are difficult to estimate.
* AI-slowing regulation that isn’t “directly” helpful beyond slowing AI progress, e.g. a law saying that the “fair use” doctrine doesn’t apply to data used to train large language models. Some things in this genre might be good to do for the purpose of buying more time to come up with needed AI alignment and governance solutions, but I haven’t prioritized looking into these options relative to the options listed in the main text, which simultaneously buy more time and are “directly” useful to mitigating the risks I’m most worried about. Moreover, I think creating the ability to slow AI progress during the most dangerous period (in the future) is more important than slowing AI progress now, and most of the policies in the main text help with slowing AI progress in the future, whereas some policies that slow AI today don’t help much with slowing AI progress in the future.
* Launching new multilateral agreements or institutions to regulate AI globally. Global regulation is needed, but I haven’t yet seen proposals in this genre that I expect to be both feasible and effective. My guess is that the way to work toward new global regulation is similar to how the October 2022 export controls have [played out](https://www.nytimes.com/2023/01/28/business/economy/netherlands-japan-china-chips.html): the US can move first with an effective policy on one of the topics above, and then persuade other influential countries to join it.
* A [national research cloud](https://hai.stanford.edu/policy/national-ai-research-resource). I’d guess this is unhelpful because it accelerates AI R&D broadly and creates a larger number of people who can train dangerously large models, though the implementation details matter.

 |
| [6](#r+13628+2+6 "Alt+ ←") | See e.g. p. 15-16 of the [GPT-4 system card report for an illustration.](https://cdn.openai.com/papers/gpt-4-system-card.pdf) |
| [7](#r+13628+2+7 "Alt+ ←") | E.g. the lack of an off switch exacerbated the fire that destroyed a datacenter in Strasbourg; see section VI.2.1 – iv of [this report](https://www.igedd.developpement-durable.gouv.fr/IMG/pdf/rapport_ovh_67_vdif_cle01cf13.pdf). |

About two years ago, I wrote that “it’s difficult to know which ‘intermediate goals’ [e.g. policy goals] we could pursue that, if achieved, would clearly increase the odds of eventual good outcomes from transformative AI.” Much has changed since then, and in this post I give an update on 12 ideas for US policy goals[1]Many […] *The original text contained 7 footnotes which were omitted from this narration.*

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[https://www.openphilanthropy.org/research/12-tentative-ideas-for-us-ai-policy](https://www.openphilanthropy.org/research/12-tentative-ideas-for-us-ai-policy?utm_source=TYPE_III_AUDIO&utm_medium=Podcast&utm_content=Source+URL+in+episode+description&utm_campaign=ai_narration)

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 Narrated by [TYPE III AUDIO](https://type3.audio/?utm_source=TYPE_III_AUDIO&utm_medium=Podcast&utm_content=Narrated+by+TYPE+III+AUDIO&utm_term=agi_sf&utm_campaign=ai_narration).


Career Resources on AI Strategy Research

(Last updated August 31, 2022) Summary and Introduction One potential way to improve the impacts of AI is helping various actors figure out good AI strategies—that is, good high-level plans focused on AI.  To support people who are interested in that, we compile some relevant career i ---

[https://aisafetyfundamentals.com/governance-blog/ai-strategy-careers](https://aisafetyfundamentals.com/governance-blog/ai-strategy-careers?utm_source=TYPE_III_AUDIO&utm_medium=Podcast&utm_content=Source+URL+in+episode+description&utm_campaign=ai_narration)

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 Narrated by [TYPE III AUDIO](https://type3.audio/?utm_source=TYPE_III_AUDIO&utm_medium=Podcast&utm_content=Narrated+by+TYPE+III+AUDIO&utm_term=agi_sf&utm_campaign=ai_narration).


My current impressions on career choice for longtermists

This post summarizes the way I currently think about career choice for longtermists. I have put much less time into thinking about this than 80,000 Hours, but I think it’s valuable for there to be multiple perspectives on this topic out there.

Edited to add: see below for why I chose to focus on longtermism in this post.

While the jobs I list overlap heavily with the jobs 80,000 Hours lists, I organize them and conceptualize them differently. 80,000 Hours tends to emphasize “paths” to particular roles working on particular causes; by contrast, I emphasize “aptitudes” one can build in a wide variety of roles and causes (including non-effective-altruist organizations) and then apply to a wide variety of longtermist-relevant jobs (often with options working on more than one cause). Example aptitudes include: “helping organizations achieve their objectives via good business practices,” “evaluating claims against each other,” “communicating already-existing ideas to not-yet-sold audiences,” etc.

(Other frameworks for career choice include starting with causes (AI safety, biorisk, etc.) or heuristics (“Do work you can be great at,” “Do work that builds your career capital and gives you more options.”) I tend to feel people should consider multiple frameworks when making career choices, since any one framework can contain useful insight, but risks being too dogmatic and specific for individual cases.)

For each aptitude I list, I include ideas for how to explore the aptitude and tell whether one is on track. Something I like about an aptitude-based framework is that it is often relatively straightforward to get a sense of one’s promise for, and progress on, a given “aptitude” if one chooses to do so. This contrasts with cause-based and path-based approaches, where there’s a lot of happenstance in whether there is a job available in a given cause or on a given path, making it hard for many people to get a clear sense of their fit for their first-choice cause/path and making it hard to know what to do next. This framework won’t make it easier for people to get the jobs they want, but it might make it easier for them to start learning about what sort of work is and isn’t likely to be a fit.

**Source:**

[https://forum.effectivealtruism.org/posts/bud2ssJLQ33pSemKH/longtermist-career-choice](https://feeds.type3.audio/agi-safety-fundamentals--governance.rss)

Narrated for [AI Safety Fundamentals](https://feeds.type3.audio/agi-safety-fundamentals--governance.rss)by [TYPE III AUDIO](https://feeds.type3.audio/agi-safety-fundamentals--governance.rss).

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