The Most Controversial Idea In Biology

0:00 If you want to know if someone really understands evolution,

0:03 just ask them this one weird question.

0:05 Why does poop smell bad?

0:07 Oh.

0:08 Oh, gosh.

0:09 Because it has bacteria in it, I guess?

0:10 Microbiome, probably.

0:12 Trash- Yeah, trash from the gut.

0:14 of the body.

0:15 The food we eat?

0:16 Because of the chemicals?

0:18 Farts don't always smell bad.

0:20 Yeah.

0:20 Well, that's a different question entirely.

0:22 Do you think it objectively smells bad?

0:25 Yes, I think so.

0:26 Yes.

0:26 How do you think it smells to flies?

0:29 Like the fly?

0:30 Yeah- They like it.

0:31 They love it.

0:31 They like it, yeah.

0:31 They love it.

0:32 Animals love stinky things.

0:34 Yeah.

0:34 They're attracted to it.

0:35 Poop smells good to flies because poop is full of nutrients.

0:39 They use it as food.

0:41 But it's also full of bacteria that can be life-threatening to humans.

0:45 So the real reason poop smells bad to us

0:48 is because if anyone ever thought it smelled good,

0:51 they would probably get really sick, die, and not pass on their genes.

0:55 After all, it's about survival of the fittest.

0:58 But survival of the fittest what?

1:01 I mean, most people think of natural selection

1:02 as being about the survival of the fittest individual animal.

1:06 Individual.

1:06 Individuals.

1:07 Individual animal.

1:08 Animal.

1:09 Okay, so it's like an individual.

1:10 Yeah.

1:11 Which makes sense.

1:12 I mean, individuals best adapted

1:13 to their environment have increased odds of survival,

1:16 and therefore a higher likelihood of passing on their genes.

1:18 So it follows that each individual should

1:21 do everything it can to survive and reproduce.

1:24 That is, it should be selfish.

1:26 But if that's true, then how do you explain this?

1:30 Worker bees will sting predators to protect the hive,

1:34 even though it might kill them in the process.

1:37 Female worker ants are sterile, so they can't reproduce, but regardless,

1:42 they work for the colony for their entire lives until they die.

1:46 Monkeys adopt orphans, wolves bring meat to non-hunting members of the pack,

1:52 and squirrels can let out alarm calls to warn others about nearby predators.

1:58 So if natural selection is all about selfish individuals,

2:01 why do we observe so much altruism in nature?

2:05 The survival is of the species that can adapt.

2:08 I think generally the species.

2:09 For the survival of the species.

2:10 So it's the survival of the species.

2:12 Okay.

2:12 Yeah, you're right.

2:12 Okay.

2:12 But survival of the fittest species or the fittest group also doesn't work.

2:17 I mean, think about what you need for natural selection to occur.

2:21 You need something that replicates itself many times over,

2:25 creating copies, and then you need a pruning process,

2:28 whereby some of those copies get eliminated and some

2:31 thrive to go on and create more copies.

2:33 The problem with groups or species is

2:36 that they don’t typically make copies of themselves.

2:39 So you almost never get copies of groups fighting

2:42 other copies of groups to see which groups win out.

2:45 So if it's not survival of the fittest individual and it's

2:48 not survival of the fittest group, then what is it?

2:51 Well, to explain that, I want to take you on a little journey,

2:55 all the way back to the beginnings of the Earth.

3:06 Where we are now, there is nothing.

3:09 Well, not really nothing, but nothing interesting.

3:12 There are only simple things, like these blobs.

3:15 This one might be a carbon dioxide molecule, or it might be cyanide.

3:19 We don't know for sure what they are,

3:20 but we do know that these compounds are very simple.

3:23 So for now, they'll just be blobs floating around our void.

3:27 In fact, much of what we'll encounter

3:29 along our journey here are just hypotheses.

3:31 A lot of Earth's early history is still a mystery, so keep that in mind.

3:35 Now, every so often, our blobs get a surplus of energy,

3:38 maybe from a ray of UV light or a nearby hot source.

3:42 This is the first major upgrade to our void, excess energy,

3:46 as it allows our blobs to interact with each other.

3:49 And most of the time, this interaction leads to nothing,

3:52 but sometimes these blobs can combine into more complicated compounds.

3:57 Here's a simple simulated example, where we only have four red blobs.

4:01 Right now, they are all individual particles,

4:03 but each time step we move forward,

4:06 let's say there's a 10% chance that all four combine into one red mega-blob.

4:11 And now imagine this mega-blob isn't very stable.

4:14 For every time step it's alive,

4:16 it has a 95% chance of falling apart back into the four smaller blobs.

4:21 If we add more of these red blobs into the mix,

4:24 you'll notice that they rarely ever come together.

4:26 On average, a mega-blob only exists around 10% of the time.

4:31 But if we were to reduce the chances of the mega-blobs dissolving to only 1%,

4:35 the void would suddenly be filled with them.

4:39 This fact hints at an important law that governs our void, the law of stability.

4:45 Unstable blobs fall apart and vanish.

4:49 Stable ones endure.

4:52 Now, watch what happens if we speed this up dramatically,

4:54 maybe a couple of years per second, maybe even a couple million.

4:58 You can see our blobs keep getting random jolts of energy,

5:01 so they combine with others to form more complex compounds.

5:06 Most attempts fail and fall apart, but every so often, by pure chance,

5:11 you get a compound that is more stable than the blobs it's made of.

5:15 This doesn't happen because the blobs want to build more complex structures.

5:20 It's just because these new configurations happen

5:22 to be more favorable in the environment.

5:24 And now when these complicated compounds become abundant enough,

5:28 they too get a chance to combine, making our void increasingly complex.

5:34 And one day, by accident,

5:36 this causes an extremely unique shape to form, one with a special property.

5:42 See, the blobs it's made of just happen

5:45 to attract similar blobs from the surrounding environment.

5:48 This red blob always attracts green blobs,

5:50 and this purple blob always attracts yellow ones, and piece by piece,

5:54 all these blobs attract their opposites until their counterparts

5:57 suddenly snap into position next to the original shape.

6:01 Now, this shape goes on to do the same thing.

6:03 Its green blobs attract red ones and yellow ones attract

6:07 the purple until another shape yet again snaps into position.

6:11 This new shape looks exactly like the original.

6:14 What just happened fully spontaneously is replication.

6:19 One shape became two.

6:20 This marks the birth of the first replicator.

6:24 We don't know exactly what this replicator looked like.

6:27 It might've been a single standalone molecule

6:30 or a group of molecules that worked together to replicate.

6:33 There's a lot of debate on this today,

6:35 so instead, let's represent the replicator as a character.

6:38 How about this one here?

6:40 Perfect.

6:41 Keep in mind it's still just a lifeless molecule,

6:44 one without any intent or purpose.

6:48 Now, you might think that the chances for the replicator

6:51 to form were extremely unlikely, but in our void,

6:53 where we have hundreds of millions of years to play

6:56 with, what might seem impossible to us becomes virtually inevitable.

7:00 And the thing is, the replicator only has to arise once.

7:04 Once it's here, it can take the simpler compounds available

7:07 in the environment to copy itself at a much faster pace.

7:11 And so it does that, until it entirely fills our void.

7:16 At least, that's what you'd expect, but there is a flaw in the process.

7:21 See, during the replicator's conquest of the void,

7:24 one of its copies makes a mistake.

7:27 Perhaps a stray ray of UV light hits it during the replication process,

7:31 or the replicator uses a building block it wasn't supposed to.

7:35 As a result, what we're left with is a new shape,

7:38 which is slightly different from its parent,

7:40 and so its properties might be slightly different too.

7:44 This error might be harmful.

7:46 For example, it might make the copy less stable.

7:48 It could be beneficial, making the copy better at replicating,

7:52 or it could be neutral, not changing the replicator in any meaningful way.

7:56 This marks the final milestone in our void, mutation.

8:00 Many species of replicators now occupy the void,

8:03 and what they do is they replicate themselves.

8:07 The problem is they all need the same limited resources,

8:10 and so our void turns into a battleground.

8:13 So which replicator will win?

8:15 What kind of properties will the void favor?

8:18 Well, let's try to simulate what happens.

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9:52 and now back to our simulation.

9:54 To simulate what a replicator battle might look like,

9:56 let's assign simplified traits to each of the replicators,

9:59 starting with the first one.

10:01 This replicator is special, since it's the only one that can

10:05 form spontaneously from smaller building blocks.

10:07 So we'll give it a spawn rate.

10:09 This should be quite rare,

10:10 so let's set the chance of formation to 1% per time step.

10:15 Just keep in mind we're just making these numbers up.

10:18 The simulation is purely illustrative.

10:19 Now, once the replicator spawns, let's say it's governed by three key traits.

10:24 First, a death rate,

10:25 the chance of it falling apart or being destroyed with each time step.

10:29 Let's set that to be, say, 2%.

10:31 Second, a replication rate, the chance to copy itself with each time step.

10:36 Let's say 4%.

10:37 And finally, a mutation rate, the chance a copy comes out mutated.

10:41 If it's 4%, roughly one in 25 copies will be a mutation.

10:46 So every time a new mutation spawns,

10:48 it will inherit the replication death and mutation stats from its parent,

10:53 but slightly randomized.

10:54 Notice that we won't give any of these secondary replicators a spawn rate.

10:58 They'll only be able to form as mutations from previous generations.

11:01 So if all of their copies die out, they'll be gone for good.

11:06 Now, before we run the simulation,

11:07 I want to quickly shout out the YouTube channel Primer.

11:09 Our setup was inspired by his amazing

11:12 in-depth simulations on evolutionary biology.

11:14 You should really check him out.

11:16 Okay, let's run it.

11:18 The graph on the right will show how the populations grow,

11:20 and this box on the left will show a slice of the void,

11:23 with all the winning replicators and the correct ratios.

11:26 You can see how the first replicator appears and then immediately disappears,

11:31 because it just happened to die before it got the chance to replicate.

11:35 But that's okay.

11:36 The original replicator can be created from smaller blobs,

11:39 so it'll come back at some point.

11:41 This time, it's starting to take off.

11:44 You can also see that it spawns some mutations,

11:47 but they're struggling to keep up.

11:48 Eventually, though, superior mutations pop up

11:51 and start to replicate faster than the original.

11:54 But you can see almost all of them are growing exponentially,

11:57 which is unrealistic.

11:58 That's because we're missing the final

12:00 piece of our simulation, limited resources.

12:03 The building blocks should eventually run out.

12:05 We can simulate this effect by introducing a sort

12:08 of resource factor to each species' replication rate.

12:11 This factor should depend on the total number of replicators in the void,

12:16 N, which will also divide with an arbitrary crowding factor, C.

12:20 C lets us define the maximum number of replicators we'll allow into the void.

12:25 Say C is 10,000.

12:26 Then once there are 10,000 replicators,

12:29 the two terms cancel out and drive the replication rate down to zero,

12:33 meaning none of the replicators will be able

12:35 to make copies until the population drops again.

12:37 So let's see how this changes our simulation.

12:41 Okay.

12:41 Like the last time, the original replicator starts to grow,

12:44 after which it's quickly taken over by its mutations,

12:49 but this time, most of these mutation populations start to decline.

12:54 Because of the scarce resources, the new best population,

12:57 the lime one, actually starts stealing resources from the others.

13:02 After that, a few more mutations pop up, even more powerful than the lime.

13:06 Ultimately, the purple replicator takes over,

13:09 occupying around 9,000 of the 10,000 available spaces.

13:13 It completely curbs all the other populations.

13:16 It goes without saying that the environment

13:18 plays a massive role in which replicator wins.

13:21 If you change the environment, you likely change the outcome.

13:24 But let's look at the stats of the replicator that came out on top this time.

13:29 That winning species has a replication rate of 20%,

13:32 compared to the 17% average across all populations.

13:36 Obviously, being able to replicate quickly pays off here.

13:39 Its death rate is below average.

13:41 Replicators that fall apart less quickly can make more copies.

13:44 And finally, it has a 1% mutation rate, compared to the average of 3.73%.

13:50 Although mutations help by injecting diversity,

13:53 for any single species, fewer mutations mean more faithful copies.

13:58 If we rerun the simulation,

14:00 you'll notice the outcomes are always slightly different,

14:04 but the winning species consistently have high

14:07 replication and low death and mutation rates.

14:10 Now, in the real void, things wouldn't have been as simple.

14:12 Instead of just tweaking these three stats,

14:14 the replicators would have to mutate all

14:16 sorts of different ways to gain an advantage.

14:19 For example, one replicator might mutate a trait that lets it destroy other

14:23 individuals and then use their building blocks to make more copies of itself.

14:27 This looks like strategy, but it's really just chemistry that gets copied

14:31 over and over because it helps the replicator survive.

14:35 Naturally, a risk of offense would

14:37 likely favor mutations that result in defense.

14:40 So an opposing replicator might stumble upon a mutation

14:43 that helps it form protective barriers from nearby materials,

14:46 letting it endure those attacks.

14:48 These barriers would also help protect the fragile

14:51 replicators from environmental damage, like UV light.

14:54 This marks an important threshold.

14:56 The replicator's traits aren't limited to just

14:58 determining the properties of the molecules themselves.

15:01 They can also shape the environment.

15:04 So by chance, the replicators inevitably mutate in ways that build

15:08 scaffolding around themselves to increase the chances of their survival.

15:12 They stumble upon ways of making structures to propel themselves around.

15:16 They develop senses and ways of storing energy.

15:19 They even mix, exchange, and steal traits from each other.

15:23 Through billions of years of trial and error,

15:26 this scaffolding gets more and more complex, and as a result, the replicator's

15:31 interactions with the void become exceedingly indirect.

15:35 They build complex survival machines for themselves,

15:38 machines whose sole purpose is to protect the replicators inside.

15:42 These machines became such experts at surviving,

15:46 they're still around some 4 billion years later.

15:49 They are the bacteria, plants, fungi, and animals all around you.

15:54 Everything alive, including you,

15:57 was built as a survival vessel for these replicators.

16:01 But today, you'd barely recognize them as replicators.

16:04 Now we just call them genes.

16:08 They're hidden deep within every living creature,

16:11 strands of DNA made from the sequences of A, T, G, and C nucleotides.

16:16 Now, one of the leading theories is that those earliest replicators

16:19 were actually something closer to RNA molecules, but then over time,

16:23 this must have evolved into a more stable system of storing information,

16:27 the DNA and proteins we use today.

16:29 They are the code that shapes our traits.

16:31 We’re taught that these traits are here solely to help

16:34 ensure our survival the survival of the individual or the species.

16:38 But do we have this the wrong way around?

16:41 When you have a child, what do you pass on?

16:43 The DNA.

16:43 The DNA, the genes.

16:44 Yeah, the genes.

16:44 Genes.

16:44 Okay, yeah.

16:46 These tiny replicators are still fighting the same

16:49 battle that started billions of years ago,

16:51 and the logic behind them hasn't changed.

16:53 The traits just become more convoluted.

16:56 Replicators that produce traits poorly suited

16:58 to their environment tend to become less common,

17:00 while those that produce advantageous traits

17:02 become more numerous in the population.

17:04 So it's not about the fittest individual or group,

17:07 it's fundamentally about the survival of the fittest genes.

17:10 They are the core unit of natural selection.

17:14 But why would natural selection care exactly for the gene?

17:17 Why not something smaller or something bigger?

17:21 Well, for something to undergo selection,

17:23 it needs to have three characteristics.

17:25 First, it needs to be able to make near identical copies of itself.

17:28 Second, it needs to exhibit traits

17:30 that affect its interaction with the environment which,

17:33 third, affect the probability of survival and reproduction of the replicator.

17:37 Something small like a single nucleotide doesn't work,

17:40 because, sure, it'll make identical copies of itself,

17:43 but alone, it doesn't exhibit a trait that could be selected for.

17:47 What about something bigger, like a chromosome?

17:50 Well, each chromosome affects potentially thousands

17:52 of traits that could influence its survival,

17:54 but when most creatures reproduce, sections of chromosomes get swapped around.

17:59 So a chromosome doesn't stay together as a cohesive replicating unit,

18:03 and therefore it can't be selected for.

18:06 But a gene is somewhere in the middle.

18:08 It's a long enough stretch of DNA that it can independently influence a trait,

18:12 but it's also short and stable enough

18:14 to be faithfully copied over into future generations.

18:17 This is why the gene is the unit of natural selection.

18:21 This perspective led to one of the most

18:23 powerful and controversial ways of seeing evolution,

18:25 one popularized by Richard Dawkins in his book The Selfish Gene.

18:29 Based on the work of evolutionary biologists in the 1960s and 1970s.

18:32 And as a response against the, then very popular, group selection theory.

18:36 Dawkins argued that just about every trait,

18:39 from animals helping each other to being completely selfish,

18:42 is a strategy that helps their genes survive and replicate.

18:46 Genes that maximize their own survival are the genes that propagate best,

18:49 even if they do so at the expense of others.

18:52 Or in Dawkins' words, we are survival machines,

18:56 robot vehicles blindly programmed to preserve

18:59 the selfish molecules known as genes.

19:02 Now, you might think this framework isn't all that groundbreaking.

19:06 I mean, take the emperor penguins in Antarctica for example.

19:09 They hesitate to jump into the water until

19:12 they are sure there are no seals around.

19:14 So what kind of genes could help a penguin survive in this environment?

19:18 Well, if the penguin’s set of genes make it more likely to be timid,

19:21 the penguin might stay back until someone braver tests the water.

19:25 That way, the penguin is at a lower risk of being eaten,

19:27 and has a better chance to survive, reproduce and pass on its ‘timid’ genes.

19:32 Here, you can think about this either as ‘the timid

19:35 genes help the penguin’ or ‘the penguin helps the timid genes’.

19:38 Either way works.

19:40 So is there any real benefit to viewing things from the gene's perspective?

19:44 Well, look at what happens when you

19:46 use these two frameworks to explain altruistic behavior,

19:48 which appears in a lot of places in nature.

19:51 Take California ground squirrels for example.

19:54 Females will let out alarm calls if they spot a predator, like a fox or a hawk,

19:58 to warn other nearby squirrels, even though this puts her survival at risk.

20:04 The genes influencing this behavior surely don’t help the squirrel.

20:08 But can the squirrel still help the genes?

20:10 I think this is a bit more clear if you

20:12 think about the fact that most living things reproduce sexually.

20:16 Right.

20:16 So a squirrel will get half its DNA from its mom and half from its dad.

20:21 So it's actually sharing half its genes with each parent.

20:24 But also, any child that it has,

20:26 it's also going to share half of its genes with the child,

20:29 but also any siblings.

20:31 But then if you take a step out to an uncle or up to a grandparent,

20:35 then it's sharing one-quarter, and then another step out is one-eighth.

20:38 All to say, you share a lot of genes with your immediate family.

20:43 And California ground squirrels, females in particular, they live around family.

20:48 So if a squirrel has a set of genes

20:50 that make her call out when it spots a predator,

20:52 there is a very good chance that the squirrels

20:54 that hear her warning call also carry those genes.

20:57 Now, as a result of her alarm call,

20:59 let's say the squirrel attracts a predator her way,

21:02 and it ends up getting eaten.

21:04 This action cost the ‘call’ genes the chance to pass

21:07 themselves on to any future offspring of that squirrel.

21:10 But, if the warning call saved at least 2 copies

21:13 of those genes in two of the squirrel’s relatives… well then,

21:16 in total, these 2 squirrels have a better chance of passing

21:20 on the genes through their offspring than the single squirrel did.

21:23 From the gene's perspective, this could be a good trade-off.

21:27 It doesn’t matter which individual helps the genes replicate,

21:30 only that as many copies as possible survive.

21:34 This principle, that altruistically helping your close

21:36 relatives helps preserve your own genes, is known as kin selection.

21:40 And the payoff behind any altruistic gesture under kin selection depends

21:44 heavily on how related you are to the individuals you're helping,

21:48 because the less related you are, the smaller the chances that you will

21:52 share that particular gene with another individual.

21:54 And you can see this in nature.

21:56 Male squirrels that don't live near

22:00 relatives almost never give out warning calls.

22:03 Now, there is a big question this gene-centric view still has to address.

22:08 If selection really favors genes that replicate well,

22:11 then why would sex ever evolve as a means of replication,

22:14 if it throws away roughly half the genes?

22:17 Most animals reproduce sexually.

22:19 So why do it, when some organisms, like certain plants and fungi,

22:22 get to pass on all of their genes through asexual reproduction?

22:26 From a gene's perspective, this seems like a much better deal.

22:30 When it comes to sexual reproduction, people like to say, "Okay.

22:34 Well, it mixes up the genes.

22:36 It's like shuffling a deck of cards,

22:38 and isn't that better for creating more variation?

22:41 And clearly, that's advantageous." Another way this has been explained is

22:45 if the genes that regulate

22:47 sexual reproduction benefit from replicating sexually,

22:51 then they're going to keep pushing for these genes.

22:53 Right.

22:54 Even if it's a net negative to all the other genes in the genome.

22:58 Yeah.

22:59 So if it benefits them, they'll keep pushing for it.

23:02 So are there any problems with how The Selfish Gene explains natural selection?

23:06 Well, yes.

23:07 I mean, it turns out the framework comes with a lot of controversy.

23:14 One of the biggest criticisms against The Selfish

23:16 Gene is that it leaves little to chance.

23:18 It implies that every gene present in the genome is

23:21 there because it actively got selected for, by natural selection,

23:24 over many generations.

23:26 But many genes are actually invisible to natural selection,

23:29 because they don't really exhibit meaningful traits in the population.

23:33 Yet, they can still evolve over time.

23:36 Imagine 20 blind cave fish, 10 with green eyes and 10 with blue.

23:40 Since they’re blind, we’ll assume that their eye color traits make no difference

23:44 to their survival so they get passed down purely by chance.

23:47 Now, to form the next generation,

23:49 randomly “pick” any fish from the first group and replicate it.

23:52 If you repeat this 20 times, you get a 2nd generation.

23:55 By chance alone, one color will probably appear more often than the other.

23:59 And if you repeat this process over many generations,

24:03 one color might eventually completely take over.

24:06 Not because it’s better, but purely due to random sampling.

24:09 This shift in the frequency of gene variants is called genetic drift.

24:13 It’s most apparent in small populations and for traits

24:15 that aren't pruned for by natural selection.

24:18 But it doesn’t only apply to silent genes.

24:22 Even when genes exhibit meaningful traits,

24:24 there is a chance that genetic drift overrides natural selection,

24:27 and a less fit gene will spread through the population just by chance.

24:31 Look back at our replicator battle.

24:33 If we run our simulation enough times,

24:35 sometimes the winning gene won’t be the one

24:37 with the traits that maximize its own survival.

24:39 Here, you can see that the winning population actually

24:42 has a higher than average mutation rate, just by chance.

24:45 And the average mutation rate is also higher than the starting value.

24:49 These are simplified examples,

24:50 but there is an ongoing argument about how much of evolution was actually

24:54 due to natural selection and how much of it was up to chance.

24:59 Another major criticism of The Selfish Gene is

25:01 about the unavoidable implication behind the word selfish.

25:04 It seems to imply that genes have agency,

25:07 like they know what they're doing and they understand the consequences,

25:11 but it's only a metaphor, just as portraying them as characters was a way

25:15 for us to make the story more engaging.

25:17 Of course, molecules don't know what they're doing.

25:20 They don't decide to replicate or conspire to out-compete others.

25:25 They just react according to the laws of physics.

25:28 So what may look like intention is just

25:31 simple chemistry that happens to work well and propagates.

25:34 But perhaps the most obvious and easiest to understand criticism

25:37 is that the whole framework is an oversimplification, and that's true.

25:41 Genes are much more complicated than we thought.

25:43 It's not as simple as one gene equals one trait.

25:46 One gene can influence many traits,

25:48 and one trait can be influenced by many genes.

25:51 There are genes that are wholly contained within other genes.

25:54 There are genes that inhibit or activate others,

25:57 and then there are even genes that seem to not encode for anything,

26:00 the so-called non-coding DNA.

26:03 All to say, we still have a lot to learn about genes.

26:06 Not to mention that the environment itself, like whether it's hot or cold,

26:09 or how much food there is, also affects how different genes get expressed.

26:13 Genes are much less deterministic than they might seem!

26:16 So you might think that a single gene would rarely have

26:19 a large enough effect that natural selection can directly impact it,

26:23 but it doesn't matter how convoluted the pathway is.

26:27 If a gene has a measurable effect on its own survival and replication,

26:30 it will be subject to some amount of natural selection.

26:34 And surely, the whole theory is a simplification,

26:37 but any theory or framework of nature is.

26:40 And what we're covering in this video is

26:42 an even more simplified picture of that framework,

26:44 but that doesn't take away the fact that viewing the world through

26:47 this lens has an incredible power

26:50 to help us understand the process of evolution.

26:53 It helps us understand why we see such

26:55 a range of different behaviors in our world because fundamentally,

26:58 those traits tend to cause the increasing

27:00 prevalence of the genes they are associated with.

27:03 It's like the whole point is figure out what's true.

27:08 Get to the truth.

27:09 And this, to me, is the baseline truth of evolution.

27:13 This is what I love about making Veritasium,

27:16 is that we get to sort of unpack and dig under the hood.

27:21 And it's what I loved about reading The Selfish Gene book,

27:25 is that it really opened my eyes to this.

27:27 Previously, I'd always just probably thought at the level of the individual,

27:31 but it makes more sense to think at the level of the gene.

27:35 The feeling that you and every other living organism is being

27:40 driven by some molecules deep in every cell is fundamentally unsettling,

27:46 and seems to remove agency from you as an acting, thinking being in the world.

27:52 Yeah, it's pretty grim.

27:54 But whether or not you agree with the fact that we

27:58 might be controlled by our genes and we're simply their flesh robots,

28:03 I think it's kind of unreasonable and unrealistic to go

28:07 through life thinking that every decision is governed by this.

28:09 It doesn't really do you any good, because we perceive the world as individuals.

28:14 So I think it's very beneficial to see

28:18 yourself as your own thing, as your own unit.

28:25 I want to give a big shout-out to Joe

28:27 Hanson from BeSmart for helping us out with this video,

28:29 and another shout-out to Primer for letting

28:31 us adapt his simulation on the first replicators.

28:34 I have put links to their channels down in the description,

28:37 so please check them out.

28:38 And finally, I want to say a huge shout-out to you.

28:41 Thank you for watching.