I think that's a gross simplification of the possible outcomes.

Admittedly my own calculation looks less like an elaborate graph involving supposed credibility intervals, and, "Do we need to do this? Yes. Can we realistically avoid having to do this? No. Let's start now EOM."

I think you need better planning.

There's a great essay that has been a featured article on the main page for some time now called Levels of Action. Applied to FAI theory:

Level 1: Directly ending human suffering.

Level 2: Constructing an AGI capable of ending human suffering for us.

Level 3: Working on the computer science aspects of AGI theory.

Level 4: Researching FAI theory, which constrains the Level 3 AGI theory.

But for that high-level basic research to have any utility, these levels must be connected to each other: there must be a firm chain where FAI theory informs AGI designs, which are actually used in the construction of an AGI tasked with ending human suffering in a friendly way.

From what I can tell on the outside, the MIRI approach seems to be: (1) find a practical theory of FAI; (2) design an AGI in accordance with this theory; (3) implement that design; (4) mission accomplished!

That makes a certain amount of intuitive sense, having stages laid out end-to-end in chronological order. However as a trained project manager I must tell you this is a recipe for disaster! The problem is that the design space branches out at each link, but without the feedback of follow-on steps, inefficient decision making will occur at earlier stages. The space of working FAI theories is much, much larger than the FAI-theory-space which results in practical AGI designs which can be implemented prior to the UFAI competition and are suitable for addressing real-world issues of human suffering as quickly as possible.

Some examples from the comparably large programs of the Manhattan project and Apollo moonshot are appropriate, if you'll forgive the length (skip to the end for a conclusion):

The Manhattan project had one driving goal: drop a bomb on Berlin and Tokyo before the GIs arrived, hopefully ending the war early. (Of course Germany surrendered before the bomb was finished, and Tokyo ended up so devastated by conventional firebombing that Hiroshima and Nagasaki were selected instead, but the original goal is what matters here.) The location of the targets meant that the bomb had to be small enough to fit in a conventional long-distance bomber, and the timeline meant that the simpler but less efficient U-235 designs were preferred. A program was designed, adequate resources allocated, and the goal achieved on time.

On the other hand it is easy to imagine how differently things might have gone if the strategy was reversed; if instead the US military decided to institute a basic research program into nuclear physics and atomic structure, before deciding on the optimal bomb reactions, then doing detailed bomb design before creating the industry necessary to produce enough material for a working weapon. Just looking at the first stage, there is nothing a priori which makes it obvious that U-235 and Pu-239 are the "interesting" nuclear fuels to focus on. Thorium, for example, was more naturally abundant and already being extracted as a by product of rare earth metal extraction, its reactions generate less lethal radiation and long-lasting waste products, and does generate U-233 which could be used in a nuclear bomb. However the straight-forward military and engineering requirements of making a bomb on schedule, and successfully delivering it on target favored U-235 and Pu-239 based weapon designs, which focused focused the efforts of the physicists involved on those fuel pathways. The rest is history.

The Apollo moonshot is another great example. NASA had a single driving goal: deliver a man to the moon before 1970, and return him safely to Earth. There's a lot of decisions that were made in the first few years driven simply by time and resources available: e.g. heavy-lift vs orbital assembly, direct return vs lunar rendezvous, expendable vs. reuse, staging vs. fuel depots. Ask Wernher von Braun what he imagined an ideal moon mission would look like, and you would have gotten something very different than Apollo. But with Apollo NASA made the right tradeoffs with respect to schedule constraints and programmatic risk.

The follow-on projects of Shuttle and Station are a completely different story, however. They were designed with no articulated long-term strategy, which meant they tried to be everything to everybody and as a result were useful to no one. Meanwhile the basic research being carried out at NASA has little, if anything to do with the long-term goals of sending humans to Mars. There's an entire division, the Space Biosciences group, which does research on Station about the long-term effects of microgravity and radiation on humans, supposedly to enable a long-duration voyage to Mars. Never mind that the microgravity issue is trivially solved by spinning the spacecraft with nothing more than a strong steel rope as a tether, and the radiation issue is sufficiently mitigated by having a storm shelter en route and throwing a couple of Martian sandbags on the roof once you get there.

There's an apocryphal story about the US government spending millions of dollars to develop the "Space Pen" -- a ballpoint pen with ink under pressure to enable writing in microgravity environments. Much later at some conference an engineer in that program meets his Soviet counterpart and asks how they solved that difficult problem. The cosmonauts used a pencil.

Sadly the story is not true -- the "Space Pen" was a successful marketing ploy by inventor Paul Fisher without any ties to NASA, although it was used by NASA and the Russians on later missions -- but it does serve to illustrate the point very succinctly. I worry that MIRI is spending its days coming up with space pens when a pencil would have done just fine.

Let me provide some practical advice. If I were running MIRI, I would still employ mathematicians working on the hail-Mary of a complete FAI theory -- avoiding the Löbian obstacle etc. -- and run the very successful workshops, though maybe just two a year. But beyond that I would spend all remaining resources on a pragmatic AGI design programme:

1) Have a series of workshops with AGI people to do a review of possible AI-influenced strategies for a positive singulatiry -- top-down FAI, seed AI to FAI, Oracle AI to FAI, Oracle AI to human augmentation, teaching a UFAI morals in a nursery environment, etc.

2) Have a series of workshops, again with AGI people to review tactics: possible AGI architectures & the minimal seed AI for each architecture, probabilistically reliable boxing setups, programmatic security, etc.

Then use the output of these workshops -- including reliable constraints on timelines -- to drive most of the research done by MIRI. For example, I anticipate that reliable unfriendly Oracle AI setups will require probabilistically auditable computation, which itself will require a strongly typed, purely functional virtual machine layer from which computation traces can be extracted and meaningfully analyzed in isolation. This is the sort of research MIRI could sponsor a grad student or Ph.d postdoc to perform.

BTW, other gripe: I have yet to see adequate arguments for the "can we realistically avoid having to do this?" from MIRI which aren't strawman arguments.

Do we need to do this = wild guess.

The whole things a Drake Equation

I often find that the narrow AI or machine-learning literature contains a round dozen papers nobody working explicitly on FAI has ever cited, or even appears to know about.

Name three. FAI contains a number of counterintuitive difficulties and it's unlikely for someone to do FAI work successfully by accident. On the other hand, someone with a fuzzier model believing that a paper they found sure sounds relevant, why isn't MIRI citing it, is far more probable from my perspective and prior.

an Oracle AI you can trust

That's a large portion of the FAI problem right there.

EDIT: To clarify, by this I don't mean to imply that FAI is easy, but that (trustworthy) Oracle AI is hard.

Since you believe it's all so wide-open, I'd like to know what you think of as "the FAI problem".

1) Designing a program capable of arbitrary self-modification, yet maintaining guarantees of "correct" behavior according to a goal set that is by necessity included in the modifications as well.

2) Designing such a high level set of goals which ensure "friendliness".

You don't need to solve philosophy to solve FAI, but philosophy is relevant to figuring out, in broad terms, the relative livelihoods of various problems and solutions.