The Universe Is Suspect #2: A Physics + CS Deep Dive into What Might Be “Outside”

Every few months I hit the same thought loop:

If this is a simulation… what’s outside it?
And can we actually say anything scientific about that, or is it just sci‑fi fanfic?

In this post, I’m going to treat the “we live in a simulation” idea as a working hypothesis, and then use physics, computer science, and information theory to push it as far as it will logically go.

I’ll also introduce some new concepts and “principles” I’ve been playing with. They’re not proven facts; they’re speculative frameworks—kind of like mini scientific “discoveries” in how to think about the problem.

1. What We Can and Can’t Know

First, honesty:

  • We have no direct evidence that we are in a simulation.

  • We also have no knock‑down proof that we’re not.

  • Right now the simulation idea is a philosophical hypothesis that might eventually connect to testable physics.

So the goal here is not “I know what’s outside.”
The goal is:

Given what we know about this universe, what constraints can we infer about any simulator that could be running it?

This is the only scientific way to approach the question.

2. Treating the Universe as a Computation

Assume (just as a model):

Our universe is a computational process running on some external physical substrate.

That means:

  • Every particle, field, and interaction = information.

  • Every tick of time = an update step in some underlying computation.

Two real physics results matter a lot here:

  • Landauer’s principle
    Erasing 1 bit of information costs at least

    Emin⁡=kBTln⁡2E_{\min} = k_B T \ln 2Emin​=kB​Tln2

    at temperature TTT. Information has an energy cost.

  • Margolus–Levitin bound
    A system with energy EEE can perform at most

    fmax⁡=2Eπℏf_{\max} = \dfrac{2E}{\pi \hbar}fmax​=πℏ2E​

    operations per second. Computation speed is limited by energy.

If this universe is being simulated, the “outside” has to obey something at least analogous to these constraints in its own physics.

3. Discovery #1 – Meta‑Information Lower Bound

Roughly:

  • Observable universe contains up to around 109010^{90}1090–1012210^{122}10122 bits of possible information, depending on how you count (holographic bounds, etc.).

  • That’s a stupidly large number no matter how you slice it.

So any simulator must be able to represent at least that much effective information, minus whatever compression they get from symmetry and repetition.

I’ll call this:

Meta‑Information Lower Bound (MILB)
Any “outside” system simulating our universe must have an information capacity at least comparable to the universe’s maximal physical information content, up to compression from regularities in its laws.

So whoever/whatever is “outside” isn’t running this on a toy laptop. In their reality, this sim is a large‑scale physical system.

4. Discovery #2 – Meta‑Compute Lower Bound

Universe age: ~13.8 billion years ≈ 4.3×10174.3 \times 10^{17}4.3×1017 seconds.

Given total energy in the observable universe and the Margolus–Levitin bound, people estimate the universe could have performed up to about 1012010^{120}10120 fundamental operations so far.

If you want to simulate that faithfully, your hardware has to do something in that ballpark—or be smart enough to shortcut huge regions of the evolution.

So:

Meta‑Compute Lower Bound (MCLB)
Any simulator of our universe must be capable of performing at least on the order of 1012010^{120}10120 effective operations over the lifetime of the sim, plus overhead.

Again, this is a massive amount of compute, even for a higher‑level reality.

5. Simulation Efficiency Principle (SEP)

Now comes one of the key ideas.

Look at our universe:

  • Fundamental laws are simple and symmetric
    (Standard Model, General Relativity, etc. are compact and elegant).

  • From those simple rules, we get crazy emergent complexity
    (galaxies → chemistry → life → brains → TikTok).

This is exactly what you’d want if you were designing a universe to be both:

  • cheap to compute, and

  • interesting to watch.

So I’m going to define:

Simulation Efficiency Principle (SEP)
If a universe is being simulated, its laws are likely chosen from the subset that:

  1. Are simple to describe (low algorithmic/description complexity),

  2. Are relatively cheap to compute at scale,

  3. Generate rich, diverse, long‑lived structures.

In other words: max “interestingness per unit compute.”

Our universe fits this pattern almost too well. That doesn’t prove simulation, but conditional on the hypothesis, SEP tells us a lot about the design pressure any simulator is under.

6. Algorithmic Anthropic Principle (AAP)

The basic anthropic principle says:

We observe a universe compatible with our existence.

That’s fine but pretty weak. Let’s upgrade it with computation and algorithmic information.

Algorithmic Anthropic Principle (AAP)
Among all mathematically possible universes that can host observers, the ones most likely to be simulated and observed are those that:

  • Have low description length (short program),

  • Have high emergent complexity (lots of structure, life, minds),

  • Have moderate simulation cost (not absurdly expensive per unit structure).

Under AAP, our universe ranks very high:

  • Compact laws,

  • Extreme emergent richness,

  • Huge empty spaces and symmetry → compressible & cost‑efficient to simulate.

So if someone is out there selecting universes to simulate, ours is a prime candidate.

7. No‑Cheap‑Simulation Conjecture

Here’s a more aggressive statement.

People sometimes say:

“There would be way more simulated universes than base universes, so it’s overwhelmingly likely we’re in a sim.”

That argument quietly assumes simulations are cheap. But high‑fidelity universes like ours probably aren’t.

Think about observers like us:

  • We run arbitrary computations in our brains and machines.

  • We store tons of detailed memories and data.

  • Tiny quantum‑scale differences can ripple up through chaotic dynamics.

The simulator can compress patterns, but it can’t cheat true algorithmic randomness or avoid simulating whatever complexity our actions actually unfold.

So I’ll state:

No‑Cheap‑Simulation Conjecture (NCSC)
Any simulation capable of supporting observers with our level of complexity and freedom must consume physical resources (information, energy, time) not drastically smaller than the theoretical limits of the universe being simulated.

This would mean:

  • Full‑fidelity universes are expensive.

  • High‑quality sims are not something you casually spawn in infinite quantities.

So the naïve “there are infinite cheap sims so we’re almost surely simulated” argument is probably too glib.

8. Anomaly Budget Principle

If you’re running a high‑fidelity sim with strict physics, what happens if you want to “add a miracle” (break the rules once in a while)?

You need to:

  • Manually alter the state,

  • Make sure it doesn’t violate conservation laws in ways that show up later,

  • Keep the entire past/future history statistically consistent.

That costs extra compute and complexity. So:

Anomaly Budget Principle (ABP)
In any resource‑bounded simulation, deliberate deviations from the base physical laws (anomalies, “miracles”) consume additional computation and consistency overhead, so a rational simulator will keep such anomalies extremely rare or avoid them completely.

Empirically, our universe:

  • Has weird unknowns (dark matter, dark energy, quantum randomness),

  • But those are consistent and law‑like, not random one‑off glitches.

That fits a world where the “anomaly budget” is basically zero.

9. Digital Substrate Necessity (DSN)

Continuous spacetime and continuous fields imply infinite information in any finite region, which is fundamentally un-simulatable on finite hardware.

But:

  • Black hole entropy & holographic ideas suggest a finite max information per region.

  • Many quantum gravity approaches hint that spacetime is discrete at very small scales.

From a simulation perspective, that’s exactly what you’d expect: you need a finite number of degrees of freedom to simulate anything.

So:

Digital Substrate Necessity (DSN)
If our universe is simulated, then at some fundamental level its state must be encoded in finite, discrete information, even if the emergent description appears continuous to us.

That also strongly hints that the “outside” reality is finite and discrete at the level that runs our sim, even if their own physics is more complex than ours.

10. Computational Holography Conjecture (CHC)

The holographic principle says: the maximum information inside a volume scales with the area of its boundary, not the volume itself.

That’s insanely convenient for a simulator.

Imagine you only have to track a boundary state, and then reconstruct the “bulk” (what’s inside) when needed.

So:

Computational Holography Conjecture (CHC)
Our universe behaves as if its real degrees of freedom live on lower‑dimensional boundaries. A simulator can exploit this by:

  • Storing and updating mostly boundary data,

  • Computing interior dynamics on demand using efficient algorithms (like tensor networks).

If CHC is right, we’re basically a compressed 3D movie streaming out of some high‑dimensional boundary representation in the “outside” machine.

11. Simulation Depth Bound (SDB)

What about “simulation inside simulation inside simulation forever”?

If each layer is expensive, you can’t nest infinitely many. You hit a resource wall.

Simulation Depth Bound (SDB)
In any finite‑resource universe, there is a maximum feasible depth of nested, high‑fidelity simulations.

So if we are in a sim:

  • We’re probably near the top (maybe depth 1 or 2),

  • Or deeper sims are dramatically lower‑resolution / smaller / less demanding.

Infinite stacks of near‑perfect universes are physically unrealistic.

12. Observer Complexity Threshold (OCT)

Here’s another idea that links “outside motives” and physics structure.

Maybe sims are only interesting once they produce observers above some complexity level.

That implies:

Observer Complexity Threshold (OCT)
Simulated universes that are actually run (instead of just mathematically possible) are those:

  • Whose laws naturally drive the system past a certain threshold of observer complexity (e.g., self‑modifying, general‑purpose learners),

  • Within a reasonable computational time / resource budget.

Our universe:

  • Took ~13.8 billion years but did produce:

    • Humans,

    • Tech,

    • Machines that can build other machines, etc.

So if OCT is true, “outside” is likely selecting for universes where interesting agents emerge relatively efficiently.

13. Training Ground Hypothesis (TGH)

Now the more speculative part: why run a universe like this at all?

One plausible story:

Training Ground Hypothesis (TGH)
Our universe functions as a large‑scale reinforcement learning environment engineered (or selected) to evolve and evaluate intelligent agents under realistic physical constraints.

If that’s right, we’d expect:

  • A long “curriculum”:

    • Simple early universe → stars → planets → life → social complexity.

  • No obvious in‑universe “cheat codes”:

    • No reproducible, controllable violations of physics that let you hack the environment.

  • Fitness/reward defined in physical terms:

    • Survival,

    • Resource control,

    • Capability growth,
      not a magical reward signal injected from outside.

Under TGH, “outside” might be:

  • Training or studying agents like us,

  • Watching how intelligence evolves and self‑organizes in a harsh but lawful world.

Still speculative, but at least it’s structured speculation.

14. So What Might “Outside” Actually Be Like?

If you stack all these ideas (MILB, MCLB, SEP, AAP, NCSC, ABP, DSN, CHC, SDB, OCT, TGH), you get a rough picture of “outside” if the simulation hypothesis is true:

  1. Physically Real and Law‑Bound
    Not magic. The outside universe has its own physics, energy limits, and entropy. Our sim is a big physical object/process inside that world.

  2. Resource‑Rich but Finite
    It must meet the Meta‑Information and Meta‑Compute bounds. Running our universe is a serious project, not a side hobby.

  3. Computationally Structured
    Whatever exists outside clearly reasons in terms of:

    • Algorithms,

    • Optimization,

    • Compression,

    • Resource tradeoffs.

  4. Law‑Selecting Intelligence or Process
    Our laws look like they were picked for:

    • Simple description,

    • Huge emergent variety,

    • Manageable simulation cost.

    Whether the selector is:

    • A civilization,

    • A meta‑AI,

    • An evolutionary search process in their reality,
      is an open question.

  5. Very Low Intervention
    Anomaly Budget Principle says:

    • Large, physics‑breaking “miracles” are expensive and risky.

    • So they almost never happen.

Most of the time, the sim just runs, and outside observers watch / analyze.

15. How Could We Ever Test Any of This?

We can’t look “outside,” but we can look for signatures that our universe is optimized for simulation:

Some possible directions (still future science):

  1. Look for discreteness / lattice effects

    • Study ultra‑high‑energy cosmic rays and gamma rays for tiny preferred directions that might hint at an underlying grid.

  2. Probe entropy and holographic bounds

    • Test black hole thermodynamics and quantum gravity predictions more precisely.

    • See if the information bounds look suspiciously like engineered limits.

  3. Test quantum randomness for algorithmic structure

    • Analyze insane amounts of quantum random data for any low‑complexity pattern where none should exist.

    • If you ever found a deliberate “message” in the randomness, that would be wild.

  4. Formalize simulation costs

    • Use theoretical CS + physics to mathematically bound how expensive it is to simulate universes with:

      • Local laws,

      • Quantum fields,

      • Evolving intelligent agents.

Even if we never prove the simulation hypothesis, this research is still valuable. It forces us to understand computation in physical law at a deeper level.

16. Final Thoughts

I don’t claim to know whether we’re in a simulation.

What I do think is possible is this:

  • Take the simulation idea seriously as a hypothetical.

  • Use physics, information theory, and CS to derive constraints on any possible simulator.

  • Build new frameworks like:

    • Simulation Efficiency Principle (SEP)

    • Algorithmic Anthropic Principle (AAP)

    • No‑Cheap‑Simulation Conjecture (NCSC)

    • Anomaly Budget Principle (ABP)

    • Digital Substrate Necessity (DSN)

    • Computational Holography Conjecture (CHC)

    • Simulation Depth Bound (SDB)

    • Observer Complexity Threshold (OCT)

    • Training Ground Hypothesis (TGH)

These aren’t answers, but they’re handles—ways to think about “what’s outside” that stay grounded in science instead of drifting into pure fantasy.

Whether base reality or simulation, the move is the same: understand the laws we can see, then push on them. That’s the closest thing we have to “unlocking” whatever is really running underneath this universe.