0 00:00:00,000 --> 00:00:30,000 Dear viewer, these subtitles were generated by a machine via the service Trint and therefore are (very) buggy. If you are capable, please help us to create good quality subtitles: https://c3subtitles.de/talk/737 Thanks! 1 00:00:14,050 --> 00:00:15,729 Can I have your attention, please? 2 00:00:15,730 --> 00:00:17,709 Maybe with this question, have you been 3 00:00:17,710 --> 00:00:18,870 drinking milk lately? 4 00:00:20,510 --> 00:00:22,609 No, but it's very 5 00:00:22,610 --> 00:00:23,929 good for your health. 6 00:00:23,930 --> 00:00:26,149 What about cheese have been 7 00:00:26,150 --> 00:00:27,979 eating cheese lately. 8 00:00:27,980 --> 00:00:28,679 Yeah. 9 00:00:28,680 --> 00:00:29,839 Huh. 10 00:00:29,840 --> 00:00:30,840 What about hamburger? 11 00:00:31,870 --> 00:00:34,329 Yeah, we said double layered 12 00:00:34,330 --> 00:00:36,639 and double layered hamburger. 13 00:00:36,640 --> 00:00:38,859 Extra cheddar cheese with some 14 00:00:38,860 --> 00:00:39,819 egg on it. 15 00:00:39,820 --> 00:00:41,889 It tastes perfect, doesn't 16 00:00:41,890 --> 00:00:42,890 it? 17 00:00:43,230 --> 00:00:45,029 Now, maybe it's good, but maybe it's also 18 00:00:45,030 --> 00:00:47,099 bad. Have you thought about this, maybe 19 00:00:47,100 --> 00:00:49,799 how irresponsible this is? 20 00:00:49,800 --> 00:00:51,389 How why? 21 00:00:51,390 --> 00:00:53,639 OK, well about this exactly. 22 00:00:53,640 --> 00:00:55,229 We'll talk to us today. 23 00:00:55,230 --> 00:00:57,989 Benjamin Robbert, he is a chemist. 24 00:00:57,990 --> 00:01:00,149 He's an animal rights activist 25 00:01:00,150 --> 00:01:02,099 and also a bio hacker. 26 00:01:02,100 --> 00:01:04,439 He's also I would like to say that 27 00:01:04,440 --> 00:01:06,899 he's also the author 28 00:01:06,900 --> 00:01:09,089 of a very famous paper talking exactly 29 00:01:09,090 --> 00:01:11,609 about how to turn meth into 30 00:01:11,610 --> 00:01:13,829 a treatment for nasal congestion. 31 00:01:13,830 --> 00:01:15,569 Please welcome home was a very warm 32 00:01:15,570 --> 00:01:16,570 applause. 33 00:01:26,960 --> 00:01:29,119 So I want to tell you that the 34 00:01:29,120 --> 00:01:31,219 future is vegan, 35 00:01:31,220 --> 00:01:32,689 we have very little choice about this if 36 00:01:32,690 --> 00:01:34,729 we want to be able to feed the world and 37 00:01:34,730 --> 00:01:36,829 avoid environmental disaster. 38 00:01:36,830 --> 00:01:38,329 But you're not necessarily going to have 39 00:01:38,330 --> 00:01:39,889 to change your diets. You may not have to 40 00:01:39,890 --> 00:01:41,659 give up that hamburger with cheese. 41 00:01:41,660 --> 00:01:43,099 And I'll tell you how. 42 00:01:43,100 --> 00:01:44,779 First, I want to show you a video from 43 00:01:44,780 --> 00:01:46,219 the future. Though I've obtained an 44 00:01:46,220 --> 00:01:48,179 authentic video from the future. 45 00:01:48,180 --> 00:01:51,079 Lieutenant, I was confused. 46 00:01:51,080 --> 00:01:53,179 We no longer enslave animals for food 47 00:01:53,180 --> 00:01:54,180 purposes. 48 00:01:54,980 --> 00:01:58,219 She tweeted, 49 00:01:58,220 --> 00:02:00,499 You've seen something as fresh and tasty 50 00:02:00,500 --> 00:02:03,319 as meat, but organically materialized 51 00:02:03,320 --> 00:02:05,359 out of patterns used by our transporters. 52 00:02:17,270 --> 00:02:19,459 OK, so we're not quite there 53 00:02:19,460 --> 00:02:21,109 yet, but there's some really impressive 54 00:02:21,110 --> 00:02:22,579 technologies out there that I'm going to 55 00:02:22,580 --> 00:02:24,739 tell you about in a few minutes. 56 00:02:24,740 --> 00:02:26,269 But I want to tell you about why we 57 00:02:26,270 --> 00:02:27,889 should care about this. 58 00:02:27,890 --> 00:02:30,289 So I am an animal rights vegan, so 59 00:02:30,290 --> 00:02:31,849 I actually care about it because of the 60 00:02:31,850 --> 00:02:32,779 animals. 61 00:02:32,780 --> 00:02:33,889 There are other reasons. 62 00:02:33,890 --> 00:02:35,959 So these are pictures from factory 63 00:02:35,960 --> 00:02:37,399 farming in the US. 64 00:02:37,400 --> 00:02:38,779 You probably think, well, the US is 65 00:02:38,780 --> 00:02:39,829 actually the worst in the world. That's 66 00:02:39,830 --> 00:02:41,299 true. We are the cruelest to animals in 67 00:02:41,300 --> 00:02:43,699 the world. It's not as bad in Europe. 68 00:02:43,700 --> 00:02:45,709 I would still say we shouldn't do it. 69 00:02:45,710 --> 00:02:47,509 The thing is that the environmental 70 00:02:47,510 --> 00:02:49,969 impact from raising animals is huge 71 00:02:49,970 --> 00:02:51,589 and you actually have a lower greenhouse 72 00:02:51,590 --> 00:02:54,049 gas emission from this intensive farming 73 00:02:54,050 --> 00:02:55,939 than from pasture raising animals, which 74 00:02:55,940 --> 00:02:58,579 is obviously kinder to the animals. 75 00:02:58,580 --> 00:03:00,079 But none of it's actually very good for 76 00:03:00,080 --> 00:03:01,309 the environment. It turns out to be 77 00:03:01,310 --> 00:03:02,599 incredibly inefficient. 78 00:03:02,600 --> 00:03:04,909 So if you care about the environment, 79 00:03:04,910 --> 00:03:06,529 you should care about replacing animals 80 00:03:06,530 --> 00:03:08,629 with something more efficient. 81 00:03:08,630 --> 00:03:10,129 And so I'll show you a few numbers. 82 00:03:13,090 --> 00:03:15,369 So when we talk about greenhouse 83 00:03:15,370 --> 00:03:17,259 gas emissions, we usually talk about 84 00:03:17,260 --> 00:03:20,289 generating electricity, driving cars, 85 00:03:20,290 --> 00:03:22,539 you know, transportation industry, but 86 00:03:22,540 --> 00:03:24,609 actually it's a pretty significant 87 00:03:24,610 --> 00:03:26,259 amount of the greenhouse gas. 88 00:03:26,260 --> 00:03:28,479 Worldwide emissions come from 89 00:03:28,480 --> 00:03:30,339 animal agriculture. 90 00:03:30,340 --> 00:03:33,429 About 18 percent of all of our emissions 91 00:03:33,430 --> 00:03:35,709 are from animal agriculture. 92 00:03:35,710 --> 00:03:37,839 We use one third of the ice free 93 00:03:37,840 --> 00:03:40,329 earth for 94 00:03:40,330 --> 00:03:42,059 raising animals, either for their feed or 95 00:03:42,060 --> 00:03:44,169 if the animals themselves and over half 96 00:03:44,170 --> 00:03:45,969 the water in the US goes to animal 97 00:03:45,970 --> 00:03:47,770 agriculture. So this is a huge impact. 98 00:03:49,680 --> 00:03:51,449 If you look at the environmental impact 99 00:03:51,450 --> 00:03:53,609 of different diets, so 100 00:03:53,610 --> 00:03:55,919 there's three bars for each kind of diet, 101 00:03:55,920 --> 00:03:57,359 the one all the way to the left, the 102 00:03:57,360 --> 00:03:58,919 tallest one is our current state of 103 00:03:58,920 --> 00:03:59,999 technology. 104 00:04:00,000 --> 00:04:02,189 And these other bars are assuming 105 00:04:02,190 --> 00:04:04,739 different technological innovations. 106 00:04:04,740 --> 00:04:07,109 But you can see that the way we currently 107 00:04:07,110 --> 00:04:09,749 eat, there's no level of technological 108 00:04:09,750 --> 00:04:11,819 innovation that was predicted 109 00:04:11,820 --> 00:04:13,949 in this study. At least that gets even 110 00:04:13,950 --> 00:04:16,049 close to as good as a vegan diet is 111 00:04:16,050 --> 00:04:17,369 at the moment. 112 00:04:17,370 --> 00:04:19,559 So really going vegan, 113 00:04:19,560 --> 00:04:21,958 you have a much lower impact 114 00:04:21,959 --> 00:04:23,459 on your greenhouse gas emissions. 115 00:04:24,900 --> 00:04:27,059 A few other interesting numbers. 116 00:04:29,100 --> 00:04:31,169 This study showing that if we want 117 00:04:31,170 --> 00:04:32,909 to hit our environmental targets, it 118 00:04:32,910 --> 00:04:34,589 would cost about half as much to do. 119 00:04:34,590 --> 00:04:36,689 So if we transition to a 120 00:04:36,690 --> 00:04:37,619 low meat diet. 121 00:04:37,620 --> 00:04:40,019 So it actually costs less to do this. 122 00:04:42,140 --> 00:04:44,269 In the US alone, if we switch to from 123 00:04:44,270 --> 00:04:46,699 animal agriculture to just vegetable 124 00:04:46,700 --> 00:04:48,829 and grain farming, we could feed one 125 00:04:48,830 --> 00:04:50,419 billion more people than we currently do 126 00:04:50,420 --> 00:04:52,309 with the same resources. 127 00:04:52,310 --> 00:04:53,929 And actually, I like the study. 128 00:04:53,930 --> 00:04:55,729 The name of the study is redefining 129 00:04:55,730 --> 00:04:58,129 agricultural yields from tonnes 130 00:04:58,130 --> 00:04:59,929 to people nourish per hectare. 131 00:04:59,930 --> 00:05:01,549 And this is a much better way to think 132 00:05:01,550 --> 00:05:03,199 about agricultural output. 133 00:05:03,200 --> 00:05:04,309 Why do you care about the weight of the 134 00:05:04,310 --> 00:05:06,109 food you get? How about caring about how 135 00:05:06,110 --> 00:05:07,429 many people you can feed with those 136 00:05:07,430 --> 00:05:08,430 resources? 137 00:05:10,460 --> 00:05:13,039 And a study from the USDA, 138 00:05:13,040 --> 00:05:14,149 which is not opposed to animal 139 00:05:14,150 --> 00:05:15,559 agriculture in general, they usually 140 00:05:15,560 --> 00:05:17,779 promote it, saying that we probably 141 00:05:17,780 --> 00:05:19,579 can't hit our greenhouse gas emissions 142 00:05:19,580 --> 00:05:21,649 targets and keep 143 00:05:21,650 --> 00:05:23,629 farming animals, basically, we just can't 144 00:05:23,630 --> 00:05:24,630 do it. 145 00:05:25,320 --> 00:05:26,610 So now for something slightly different, 146 00:05:27,790 --> 00:05:29,099 I don't know how many people have seen 147 00:05:29,100 --> 00:05:30,149 this movie Sharknado. 148 00:05:33,590 --> 00:05:35,269 It is completely ridiculous, I've never 149 00:05:35,270 --> 00:05:36,649 actually seen it, but somehow there's a 150 00:05:36,650 --> 00:05:38,629 tornado and sharks in it and this menaces 151 00:05:38,630 --> 00:05:40,849 people, you know, not realistic, 152 00:05:40,850 --> 00:05:43,429 but cows, 153 00:05:43,430 --> 00:05:45,349 absolutely the worst as far as greenhouse 154 00:05:45,350 --> 00:05:47,749 gas emissions intensity resources 155 00:05:47,750 --> 00:05:49,609 needed for calories out. 156 00:05:49,610 --> 00:05:51,769 So farming cows increases, global 157 00:05:51,770 --> 00:05:53,629 temperatures, increase in global 158 00:05:53,630 --> 00:05:56,059 temperatures, leads to more storms. 159 00:05:56,060 --> 00:05:58,489 More storms can lead to more tornadoes. 160 00:05:58,490 --> 00:06:00,559 So a realistic threat is the 161 00:06:00,560 --> 00:06:01,560 tornado. 162 00:06:09,270 --> 00:06:10,829 So watch out for this. 163 00:06:12,490 --> 00:06:14,039 OK, that's entertaining, but this problem 164 00:06:14,040 --> 00:06:15,450 is not actually funny, right? 165 00:06:19,330 --> 00:06:21,029 Fifty percent of the world's population 166 00:06:21,030 --> 00:06:23,189 lives in areas that are 167 00:06:23,190 --> 00:06:25,409 at danger of coastal flooding 168 00:06:25,410 --> 00:06:26,730 over the next few hundred years. 169 00:06:27,810 --> 00:06:28,810 That's a lot of people. 170 00:06:30,300 --> 00:06:31,979 We've already had millions of people a 171 00:06:31,980 --> 00:06:34,139 year displaced due to climate change. 172 00:06:34,140 --> 00:06:35,790 And this is going to increase greatly. 173 00:06:37,110 --> 00:06:39,209 And we there's the coastal 174 00:06:39,210 --> 00:06:41,669 areas. But then if you live inland, 175 00:06:41,670 --> 00:06:44,219 most people rely on snowpacks 176 00:06:44,220 --> 00:06:45,389 and glaciers that are not being 177 00:06:45,390 --> 00:06:45,929 replenished. 178 00:06:45,930 --> 00:06:48,389 So they're going to experience extreme 179 00:06:48,390 --> 00:06:49,739 drought if we keep going the way we're 180 00:06:49,740 --> 00:06:52,169 going. So, yeah, this affects everybody. 181 00:06:52,170 --> 00:06:53,279 It's a really bad problem. 182 00:06:54,510 --> 00:06:56,399 So are we condemned to tofu? 183 00:06:58,440 --> 00:06:59,489 Tofu is great. 184 00:06:59,490 --> 00:07:01,109 You shouldn't think of it that way. 185 00:07:01,110 --> 00:07:02,639 You should you should eat tofu. 186 00:07:02,640 --> 00:07:03,969 You can ignore the rest of my talking. 187 00:07:03,970 --> 00:07:05,669 Just go vegan. Now, that's what I would 188 00:07:05,670 --> 00:07:06,670 prefer, but. 189 00:07:08,120 --> 00:07:09,859 If you don't want to do that, there are 190 00:07:09,860 --> 00:07:10,860 other options. 191 00:07:12,140 --> 00:07:14,089 There is a new generation of vegan food 192 00:07:14,090 --> 00:07:16,369 that which is currently being developed, 193 00:07:16,370 --> 00:07:18,499 which aims to either exactly replace 194 00:07:18,500 --> 00:07:20,809 meat with something that's the same 195 00:07:20,810 --> 00:07:21,860 that's animal cells 196 00:07:23,990 --> 00:07:25,489 and is indistinguishable because it's 197 00:07:25,490 --> 00:07:26,719 actually the same thing. 198 00:07:26,720 --> 00:07:28,999 Or there's also some genetic engineering 199 00:07:29,000 --> 00:07:30,319 approaches. I'm going to tell you about 200 00:07:30,320 --> 00:07:31,819 that. Replace single proteins that are 201 00:07:31,820 --> 00:07:33,949 the most important for whatever 202 00:07:33,950 --> 00:07:35,239 food you're interested in. 203 00:07:35,240 --> 00:07:37,459 And I'll talk about some of those. 204 00:07:37,460 --> 00:07:38,809 So the kinds of things I'm going to talk 205 00:07:38,810 --> 00:07:40,519 about first is food science and 206 00:07:40,520 --> 00:07:42,469 engineering. And this is not exactly high 207 00:07:42,470 --> 00:07:44,329 tech. This is the kind of technology 208 00:07:44,330 --> 00:07:46,549 we've been working with for nearly 100 209 00:07:46,550 --> 00:07:48,649 years. It's just usually using a 210 00:07:48,650 --> 00:07:50,839 scientific approach to 211 00:07:50,840 --> 00:07:52,789 making better foods. 212 00:07:52,790 --> 00:07:54,529 And so any processed food you've had 213 00:07:55,550 --> 00:07:57,229 falls under this category, food 214 00:07:57,230 --> 00:07:58,849 packaging, food, transportation. 215 00:07:58,850 --> 00:08:00,110 So this is a well-established 216 00:08:02,300 --> 00:08:03,300 discipline. 217 00:08:04,570 --> 00:08:06,789 A newer one is in vitro meat. 218 00:08:06,790 --> 00:08:08,499 This is where people take an actual 219 00:08:08,500 --> 00:08:10,899 sample of animal cells, 220 00:08:10,900 --> 00:08:13,239 grow it up in a petri dish, and then 221 00:08:13,240 --> 00:08:15,069 harvest it and make hamburger or whatever 222 00:08:15,070 --> 00:08:16,779 other meat out of it. 223 00:08:16,780 --> 00:08:18,529 I'll talk a little bit more about that. 224 00:08:18,530 --> 00:08:19,899 And then there's also the genetic 225 00:08:19,900 --> 00:08:22,149 engineering approach where a 226 00:08:22,150 --> 00:08:24,729 non animal organism 227 00:08:24,730 --> 00:08:26,949 gets the DNA from an animal and 228 00:08:26,950 --> 00:08:28,089 we make some protein that we're 229 00:08:28,090 --> 00:08:29,079 interested in. 230 00:08:29,080 --> 00:08:30,339 I'll talk about some of those efforts as 231 00:08:30,340 --> 00:08:31,509 well. 232 00:08:31,510 --> 00:08:33,548 Before I go on with those, I want to say 233 00:08:33,549 --> 00:08:35,408 again, there's a lot of great vegan food 234 00:08:35,409 --> 00:08:37,298 out there already. These are traditional 235 00:08:37,299 --> 00:08:40,058 chef kind of approaches 236 00:08:40,059 --> 00:08:41,379 all over the world. There's really good 237 00:08:41,380 --> 00:08:43,569 vegan food to go check some 238 00:08:43,570 --> 00:08:44,570 of that out. 239 00:08:46,010 --> 00:08:48,319 But getting back to technology, so 240 00:08:48,320 --> 00:08:50,059 food science is basically, like I said, 241 00:08:50,060 --> 00:08:52,579 just the scientific approach to 242 00:08:52,580 --> 00:08:55,190 any aspect of food production and 243 00:08:56,270 --> 00:08:58,189 for the purposes of what I'm talking 244 00:08:58,190 --> 00:09:01,549 about for vegan replacement foods, 245 00:09:01,550 --> 00:09:03,649 is taking an engineering approach to 246 00:09:05,030 --> 00:09:07,639 plant proteins, plant starches, 247 00:09:07,640 --> 00:09:09,679 other plant material, and finding a way 248 00:09:09,680 --> 00:09:11,869 to engineer that to be a 249 00:09:11,870 --> 00:09:13,549 convincing meat alternative. 250 00:09:14,930 --> 00:09:15,919 There's some really good ones out there 251 00:09:15,920 --> 00:09:16,920 right now. 252 00:09:17,510 --> 00:09:20,479 There's good egg replacements already. 253 00:09:20,480 --> 00:09:22,400 So Hampton Creek Foods is one company. 254 00:09:23,540 --> 00:09:25,249 Bill Gates is one of their investors. 255 00:09:25,250 --> 00:09:27,589 And he made the point that we're only 256 00:09:27,590 --> 00:09:29,359 we've only explored about eight percent 257 00:09:29,360 --> 00:09:31,769 of the plant proteins in the world. 258 00:09:31,770 --> 00:09:32,839 We don't know what most of the rest of 259 00:09:32,840 --> 00:09:35,149 them do or if they can be used 260 00:09:35,150 --> 00:09:37,249 as replacements 261 00:09:37,250 --> 00:09:38,569 for animal products. 262 00:09:38,570 --> 00:09:40,789 So Hampton Creek Foods went search 263 00:09:40,790 --> 00:09:41,989 for a bunch of these. 264 00:09:41,990 --> 00:09:44,119 They found a specific protein that 265 00:09:44,120 --> 00:09:45,519 was very similar to 266 00:09:46,790 --> 00:09:49,009 the to some proteins and eggs and found 267 00:09:49,010 --> 00:09:50,809 that they could make some pretty good egg 268 00:09:50,810 --> 00:09:53,089 replacements and Mayos first product. 269 00:09:53,090 --> 00:09:54,379 That's actually one of the easier things 270 00:09:54,380 --> 00:09:57,229 to replace. But they're doing quite well. 271 00:09:57,230 --> 00:09:59,629 But they're using this first 272 00:09:59,630 --> 00:10:01,819 investment and sales of these products to 273 00:10:01,820 --> 00:10:04,279 make a huge database and explore 274 00:10:04,280 --> 00:10:05,719 all of these proteins or as many as they 275 00:10:05,720 --> 00:10:07,099 can. 276 00:10:07,100 --> 00:10:09,139 There's also a very realistic scrambled 277 00:10:09,140 --> 00:10:10,999 egg replacement. 278 00:10:11,000 --> 00:10:13,069 And I found a good Amazon quote that 279 00:10:13,070 --> 00:10:15,409 that supports my point where 280 00:10:15,410 --> 00:10:17,569 a lot of people say that they 281 00:10:17,570 --> 00:10:18,589 can't really tell the difference, that 282 00:10:18,590 --> 00:10:20,329 this stuff is very convincing for 283 00:10:20,330 --> 00:10:21,330 scrambled eggs. 284 00:10:23,300 --> 00:10:25,460 So those may not seem that far out there. 285 00:10:26,480 --> 00:10:29,239 There's a replacement seafood that is 286 00:10:29,240 --> 00:10:31,309 also no genetic engineering, no 287 00:10:31,310 --> 00:10:32,449 cell culturing. 288 00:10:32,450 --> 00:10:33,949 This is just plants that they've taken 289 00:10:33,950 --> 00:10:35,330 and made shrimp out of. 290 00:10:37,050 --> 00:10:39,379 So they're reviews 291 00:10:39,380 --> 00:10:41,539 calling it insanely realistic. 292 00:10:41,540 --> 00:10:43,609 One thing about this, you get to 293 00:10:43,610 --> 00:10:45,859 start about start to think about new 294 00:10:45,860 --> 00:10:47,209 things that you can get out of these 295 00:10:47,210 --> 00:10:48,979 plants. It's not exactly just a 296 00:10:48,980 --> 00:10:50,389 replacement in this case. 297 00:10:50,390 --> 00:10:51,769 There's no allergy problems for most 298 00:10:51,770 --> 00:10:53,720 people. You know, seafood allergies 299 00:10:55,010 --> 00:10:56,029 are fairly common. 300 00:10:56,030 --> 00:10:58,369 There's a lot of people who have them. 301 00:10:58,370 --> 00:10:59,659 So this gets around. For most people, 302 00:10:59,660 --> 00:11:00,660 that allergy problem, 303 00:11:01,850 --> 00:11:03,409 it gets around some religious laws. 304 00:11:03,410 --> 00:11:05,539 There's this company called Lime 305 00:11:05,540 --> 00:11:08,299 Sushi that is using the shrimp 306 00:11:08,300 --> 00:11:09,949 and a big one for shrimp. 307 00:11:09,950 --> 00:11:12,109 Child slavery in South East Asia 308 00:11:12,110 --> 00:11:14,179 is really sort 309 00:11:14,180 --> 00:11:16,609 of vital to the current shrimp 310 00:11:16,610 --> 00:11:18,139 production chain. 311 00:11:18,140 --> 00:11:19,639 And so this gets rid of that problem. 312 00:11:23,600 --> 00:11:25,729 OK, so that's the engineering approach. 313 00:11:25,730 --> 00:11:28,069 That is a fairly traditional 314 00:11:28,070 --> 00:11:29,719 way of doing things. 315 00:11:29,720 --> 00:11:31,069 Now we get into some of the really new 316 00:11:31,070 --> 00:11:32,070 stuff. 317 00:11:33,560 --> 00:11:35,809 Such as the in vitro meat 318 00:11:35,810 --> 00:11:37,849 also sort of being rebranded as clean 319 00:11:37,850 --> 00:11:39,499 meat, and there's some good reasons for 320 00:11:39,500 --> 00:11:41,359 this kind of sounds like a marketing 321 00:11:41,360 --> 00:11:41,659 term. 322 00:11:41,660 --> 00:11:43,849 But but it really 323 00:11:43,850 --> 00:11:45,409 does have some good reasons. 324 00:11:45,410 --> 00:11:47,209 You would call it that. But let me 325 00:11:47,210 --> 00:11:48,849 describe first how this works. 326 00:11:49,890 --> 00:11:52,109 So cells 327 00:11:52,110 --> 00:11:54,299 from a cow are taken and this doesn't 328 00:11:54,300 --> 00:11:56,429 have to be invasive, you'll only need 329 00:11:56,430 --> 00:11:58,559 a few cells and you get to grow these 330 00:11:58,560 --> 00:12:00,759 cells up basically in a VAT, maybe 331 00:12:00,760 --> 00:12:02,159 doesn't sound that appetizing, but 332 00:12:03,600 --> 00:12:06,449 you grow them up. And then interestingly, 333 00:12:06,450 --> 00:12:08,489 just like real muscle cells, you don't 334 00:12:08,490 --> 00:12:10,379 get the right texture unless you exercise 335 00:12:10,380 --> 00:12:12,119 them. So people actually put pieces of 336 00:12:12,120 --> 00:12:13,889 Velcro on either side and move them back 337 00:12:13,890 --> 00:12:15,359 and forth. And this gives it the correct 338 00:12:15,360 --> 00:12:16,360 texture. 339 00:12:18,810 --> 00:12:20,819 From there, you get to formulate it into 340 00:12:20,820 --> 00:12:21,839 a hamburger. 341 00:12:21,840 --> 00:12:22,919 Now there's a few things 342 00:12:24,420 --> 00:12:25,859 and of course, other meat will be 343 00:12:25,860 --> 00:12:28,049 possible after that. 344 00:12:28,050 --> 00:12:29,909 Hamburger is the easiest, but you should 345 00:12:29,910 --> 00:12:31,859 be able to make a steak eventually once 346 00:12:31,860 --> 00:12:33,619 we get really good at this. 347 00:12:33,620 --> 00:12:35,879 This is a little bit more complex 348 00:12:35,880 --> 00:12:37,259 than some other things we'll be talking 349 00:12:37,260 --> 00:12:39,299 about, because meat is not just muscle 350 00:12:39,300 --> 00:12:41,669 cells. You also need fats, 351 00:12:41,670 --> 00:12:42,899 you need some other things. 352 00:12:42,900 --> 00:12:44,309 And so you actually need to get these 353 00:12:44,310 --> 00:12:46,409 cells to grow together correctly 354 00:12:46,410 --> 00:12:49,199 and the right 3D architecture. 355 00:12:49,200 --> 00:12:50,459 So this is something people are working 356 00:12:50,460 --> 00:12:51,719 on. I don't think we're quite there yet. 357 00:12:52,890 --> 00:12:55,019 And also it's kind of expensive 358 00:12:55,020 --> 00:12:55,919 at the moment. 359 00:12:55,920 --> 00:12:58,229 Mark posts first hamburger costs nearly 360 00:12:58,230 --> 00:12:59,789 half a million dollars. 361 00:12:59,790 --> 00:13:01,049 Most of us probably are not going to pay 362 00:13:01,050 --> 00:13:02,050 that much for a hamburger. 363 00:13:03,150 --> 00:13:05,939 Also right now, it requires 364 00:13:05,940 --> 00:13:07,859 at least most of the efforts rely on 365 00:13:07,860 --> 00:13:09,359 fetal bovine serum. 366 00:13:09,360 --> 00:13:11,099 And even if you don't know what that is, 367 00:13:11,100 --> 00:13:12,239 you can probably guess that it is not 368 00:13:12,240 --> 00:13:13,240 vegan. 369 00:13:15,900 --> 00:13:18,299 Also, Michael Isen, who actually 370 00:13:18,300 --> 00:13:20,579 works for a competing company 371 00:13:20,580 --> 00:13:22,739 here, so he makes 372 00:13:22,740 --> 00:13:25,109 his point that this is kind of disgusting 373 00:13:25,110 --> 00:13:27,209 both to vegans and to everybody else as 374 00:13:27,210 --> 00:13:28,210 well. 375 00:13:31,020 --> 00:13:33,149 But that turns out, I'll say and it 376 00:13:33,150 --> 00:13:34,859 turns out about how you frame the 377 00:13:34,860 --> 00:13:36,389 question, but, yeah, a lot of people are 378 00:13:36,390 --> 00:13:37,440 kind of weirded out by this. 379 00:13:38,740 --> 00:13:40,139 You know, it doesn't have to really be 380 00:13:40,140 --> 00:13:42,389 that that disgusting. 381 00:13:42,390 --> 00:13:45,409 You know, when you grow this these cells, 382 00:13:45,410 --> 00:13:47,279 you know, most people think food grown in 383 00:13:47,280 --> 00:13:49,319 a test tube doesn't sound that great. 384 00:13:49,320 --> 00:13:51,359 But the way you actually do this is you 385 00:13:51,360 --> 00:13:53,279 put this in a big bioreactor that looks a 386 00:13:53,280 --> 00:13:54,959 lot like brewing beer. 387 00:13:54,960 --> 00:13:56,999 You know, most people are OK with brewing 388 00:13:57,000 --> 00:13:57,929 beer. 389 00:13:57,930 --> 00:14:00,269 These cells grow in a very similar way. 390 00:14:01,600 --> 00:14:03,269 You don't usually have to exercise beer. 391 00:14:03,270 --> 00:14:05,639 We still have to do this where 392 00:14:05,640 --> 00:14:07,589 it's going to need the stage where it's 393 00:14:07,590 --> 00:14:09,059 stretched and exercised. 394 00:14:10,590 --> 00:14:11,999 But look at this. 395 00:14:12,000 --> 00:14:13,259 The batch process can be as short as 396 00:14:13,260 --> 00:14:14,279 seven days. 397 00:14:14,280 --> 00:14:16,379 So you can up this huge amount 398 00:14:16,380 --> 00:14:19,319 of muscle cells and just a week 399 00:14:19,320 --> 00:14:21,749 it can be 10 percent the greenhouse 400 00:14:21,750 --> 00:14:23,039 gas emissions. 401 00:14:23,040 --> 00:14:25,109 As for the same meat grinder and animal, 402 00:14:25,110 --> 00:14:27,089 so, you know, there are some big 403 00:14:27,090 --> 00:14:29,219 advantages to this fast, low 404 00:14:29,220 --> 00:14:30,209 impact. 405 00:14:30,210 --> 00:14:31,949 And, you know, there have been surveys 406 00:14:31,950 --> 00:14:33,479 where people have been asked about this, 407 00:14:33,480 --> 00:14:34,559 where it's been framed differently. 408 00:14:34,560 --> 00:14:36,659 If you say it's grown in a 409 00:14:36,660 --> 00:14:38,609 similar way to fermenting beer, would you 410 00:14:38,610 --> 00:14:40,469 be willing to try this? 411 00:14:40,470 --> 00:14:43,319 A lot more people say yes compared to 412 00:14:43,320 --> 00:14:45,329 when you asked them if it's meat from a 413 00:14:45,330 --> 00:14:47,069 test tube, would you be willing to to try 414 00:14:47,070 --> 00:14:49,379 it? So, yeah, it kind of depends 415 00:14:49,380 --> 00:14:50,380 on the marketing little bit. 416 00:14:51,810 --> 00:14:54,059 And the one other thing, when 417 00:14:54,060 --> 00:14:56,039 you slaughter animals, there's always a 418 00:14:56,040 --> 00:14:58,139 chance of contamination from other some 419 00:14:58,140 --> 00:14:59,609 other part of the animal back into the 420 00:14:59,610 --> 00:15:01,679 meat. So mad cow disease 421 00:15:01,680 --> 00:15:02,729 is an example of this. 422 00:15:02,730 --> 00:15:04,889 And I know in the UK a few years 423 00:15:04,890 --> 00:15:06,209 ago, there were a lot of bans on 424 00:15:06,210 --> 00:15:07,619 importing beef. 425 00:15:07,620 --> 00:15:09,299 And I think the EU may have had some as 426 00:15:09,300 --> 00:15:11,519 well because of mad cow disease. 427 00:15:11,520 --> 00:15:12,960 There's also E. coli 428 00:15:13,980 --> 00:15:15,719 contamination from the intestines of the 429 00:15:15,720 --> 00:15:18,449 animals that sometimes cause pathogenic 430 00:15:18,450 --> 00:15:19,450 E. coli. 431 00:15:20,640 --> 00:15:22,349 You don't have that risk in this case. 432 00:15:22,350 --> 00:15:24,509 So there's that's why 433 00:15:24,510 --> 00:15:25,499 one of the reasons that people are 434 00:15:25,500 --> 00:15:26,879 calling it clean meat is because you 435 00:15:26,880 --> 00:15:28,589 actually have a much lower chance of 436 00:15:28,590 --> 00:15:29,590 contamination. 437 00:15:31,080 --> 00:15:33,269 So this seems kind of far off, but 438 00:15:33,270 --> 00:15:34,649 actually maybe not quite. 439 00:15:34,650 --> 00:15:36,659 So this is the first time a meatball has 440 00:15:36,660 --> 00:15:38,879 ever been with beef sales, 441 00:15:38,880 --> 00:15:40,979 did not require how to be slaughtered 442 00:15:42,520 --> 00:15:43,520 like a. 443 00:15:46,030 --> 00:15:48,699 So there has actually been 444 00:15:48,700 --> 00:15:50,439 there's this company in Memphis Meats, 445 00:15:50,440 --> 00:15:52,659 which has made they've 446 00:15:52,660 --> 00:15:54,249 made a meatball, but 447 00:15:55,960 --> 00:15:58,059 it's supposed to be very, very realistic. 448 00:15:58,060 --> 00:16:00,459 They are scaling this up now. 449 00:16:00,460 --> 00:16:02,649 It's still fairly expensive. 450 00:16:02,650 --> 00:16:04,059 Their price right now is supposed to be 451 00:16:04,060 --> 00:16:06,189 eighteen thousand dollars a pound, 452 00:16:06,190 --> 00:16:07,629 down from half a million dollars a 453 00:16:07,630 --> 00:16:10,009 hamburger. But it's the right direction. 454 00:16:10,010 --> 00:16:11,619 But Mark Post, the one who made this half 455 00:16:11,620 --> 00:16:13,869 million dollar hamburger, says that his 456 00:16:13,870 --> 00:16:16,479 burgers are now 11 dollars a patty. 457 00:16:16,480 --> 00:16:17,829 He's not actually selling them, so we 458 00:16:17,830 --> 00:16:18,849 won't know till they're actually on the 459 00:16:18,850 --> 00:16:20,919 market. But he claims that that's 460 00:16:20,920 --> 00:16:22,239 where you can get right now. 461 00:16:22,240 --> 00:16:23,679 And he has also said that he has a 462 00:16:23,680 --> 00:16:25,899 replacement for the bovine 463 00:16:25,900 --> 00:16:27,459 fetal serum. 464 00:16:27,460 --> 00:16:28,359 I hope all that's true. 465 00:16:28,360 --> 00:16:29,529 We haven't actually seen proof of that 466 00:16:29,530 --> 00:16:30,969 yet, as far as I know. 467 00:16:30,970 --> 00:16:33,189 But but 468 00:16:33,190 --> 00:16:35,079 I think this is still a few years off 469 00:16:35,080 --> 00:16:37,209 before it becomes a real mainstream 470 00:16:37,210 --> 00:16:39,399 thing that that you can go out and buy. 471 00:16:39,400 --> 00:16:41,169 But the technology is moving there. 472 00:16:42,910 --> 00:16:44,379 So I'm going to talk about slightly 473 00:16:44,380 --> 00:16:46,359 different technology now, one that I work 474 00:16:46,360 --> 00:16:48,139 on for one of the projects. 475 00:16:48,140 --> 00:16:50,649 I work on genetic engineering. 476 00:16:50,650 --> 00:16:51,609 And I know this is a slightly 477 00:16:51,610 --> 00:16:54,139 controversial topic, especially in 478 00:16:54,140 --> 00:16:55,569 in Europe. 479 00:16:55,570 --> 00:16:57,729 But specifically, I want 480 00:16:57,730 --> 00:17:00,339 to talk about transgenic organisms. 481 00:17:00,340 --> 00:17:02,229 This is where you take the DNA from one 482 00:17:02,230 --> 00:17:04,509 organism and put it into another, 483 00:17:04,510 --> 00:17:05,589 hopefully for some good reason. 484 00:17:08,770 --> 00:17:10,299 And let me explain the process a little 485 00:17:10,300 --> 00:17:11,318 bit. 486 00:17:11,319 --> 00:17:13,539 This can be it's actually not 487 00:17:13,540 --> 00:17:14,919 that hard to do. 488 00:17:14,920 --> 00:17:17,199 I do this at a hackerspace 489 00:17:17,200 --> 00:17:18,399 in Oakland, California. 490 00:17:20,210 --> 00:17:21,489 And basically you have to start with a 491 00:17:21,490 --> 00:17:22,419 DNA sample. 492 00:17:22,420 --> 00:17:24,098 Well, from humans, you can get a DNA 493 00:17:24,099 --> 00:17:25,809 sample from spit or scraping a little bit 494 00:17:25,810 --> 00:17:26,810 from the inside of the mouth. 495 00:17:27,790 --> 00:17:29,949 You then take the sample and you 496 00:17:29,950 --> 00:17:31,839 get the DNA sequenced. 497 00:17:31,840 --> 00:17:33,009 We don't do this in my lab. 498 00:17:33,010 --> 00:17:34,390 It's kind of hard to do 499 00:17:35,740 --> 00:17:37,779 with an expensive equipment, but it's not 500 00:17:37,780 --> 00:17:39,519 actually that expensive to have DNA 501 00:17:39,520 --> 00:17:40,989 sequence. You can send it out to 502 00:17:40,990 --> 00:17:42,069 commercial services. 503 00:17:42,070 --> 00:17:44,229 Anybody can do this and 504 00:17:44,230 --> 00:17:45,759 it's not that expensive. 505 00:17:47,520 --> 00:17:48,989 Once you have that sequence, you put it 506 00:17:48,990 --> 00:17:50,849 into your nice modern computer where you 507 00:17:52,350 --> 00:17:54,449 need to optimize the DNA sequence 508 00:17:54,450 --> 00:17:56,219 for the organism that you're putting the 509 00:17:56,220 --> 00:17:57,539 DNA into. 510 00:17:57,540 --> 00:18:00,059 So if I have a sequence from a cow, 511 00:18:00,060 --> 00:18:02,189 that exact DNA sequence will not make the 512 00:18:02,190 --> 00:18:03,509 same protein and yeast. 513 00:18:03,510 --> 00:18:05,669 So we need to do this optimization. 514 00:18:05,670 --> 00:18:07,799 The interesting thing is that there are 515 00:18:07,800 --> 00:18:09,809 free online tools that allow you to do 516 00:18:09,810 --> 00:18:12,269 this if you say this is a mammalian 517 00:18:12,270 --> 00:18:14,219 piece of DNA. I want to grow this protein 518 00:18:14,220 --> 00:18:15,329 in yeast. 519 00:18:15,330 --> 00:18:17,189 You can find online tools for free that 520 00:18:17,190 --> 00:18:18,719 will do this and they will check your 521 00:18:18,720 --> 00:18:19,720 work. 522 00:18:20,370 --> 00:18:22,019 So this is actually pretty accessible, 523 00:18:22,020 --> 00:18:23,190 not very hard to use. 524 00:18:25,730 --> 00:18:27,319 This part's a little bit harder, takes a 525 00:18:27,320 --> 00:18:28,729 little more practice, but again, there 526 00:18:28,730 --> 00:18:31,159 are free online tools that will help you. 527 00:18:31,160 --> 00:18:33,469 You then get the DNA synthesized. 528 00:18:33,470 --> 00:18:35,539 You can do this as a commercial service. 529 00:18:35,540 --> 00:18:36,679 Again, not that expensive. 530 00:18:36,680 --> 00:18:38,089 You can do it for a few hundred dollars. 531 00:18:40,540 --> 00:18:42,399 You put the DNA that you're interested in 532 00:18:42,400 --> 00:18:44,499 into this circular piece of DNA and 533 00:18:44,500 --> 00:18:46,539 you get to put other instructions on here 534 00:18:46,540 --> 00:18:47,799 and this is really important, you get to 535 00:18:47,800 --> 00:18:49,779 do a lot of cool things. 536 00:18:49,780 --> 00:18:52,509 One thing you can do is tell the organism 537 00:18:52,510 --> 00:18:54,099 when you've made the protein, 538 00:18:55,660 --> 00:18:56,829 kick it out of the cell. 539 00:18:56,830 --> 00:18:58,659 So if you have yeast that has one of 540 00:18:58,660 --> 00:19:00,129 these plasmids in it and you're making a 541 00:19:00,130 --> 00:19:02,049 protein, the yeast will then kick the 542 00:19:02,050 --> 00:19:03,489 protein out of the cell and then you get 543 00:19:03,490 --> 00:19:04,959 to collect it much more easily than if 544 00:19:04,960 --> 00:19:07,059 you had to grind the step to get 545 00:19:07,060 --> 00:19:08,559 it out of the cells. 546 00:19:08,560 --> 00:19:10,509 You can put kill switches into this so 547 00:19:10,510 --> 00:19:12,249 that if this escapes from your 548 00:19:12,250 --> 00:19:13,989 bioreactor, it cannot spread in the 549 00:19:13,990 --> 00:19:15,219 environment. 550 00:19:15,220 --> 00:19:17,439 So this is important. Also, you can 551 00:19:17,440 --> 00:19:18,909 consider it a safety feature that you can 552 00:19:18,910 --> 00:19:19,910 program in. 553 00:19:21,940 --> 00:19:23,379 From there, this is a picture of 554 00:19:23,380 --> 00:19:25,629 bacteria, but this works in 555 00:19:25,630 --> 00:19:27,819 other organisms as well. 556 00:19:27,820 --> 00:19:30,069 You get your circular DNA, the plasmid 557 00:19:30,070 --> 00:19:32,229 into the cell of the 558 00:19:32,230 --> 00:19:33,849 organism that you want to produce your 559 00:19:33,850 --> 00:19:34,850 protein. 560 00:19:36,340 --> 00:19:38,439 If you leave it as this standalone 561 00:19:38,440 --> 00:19:40,329 piece of DNA, it will produce your 562 00:19:40,330 --> 00:19:42,489 proteins. You can also recombine it with 563 00:19:42,490 --> 00:19:44,709 the genome of 564 00:19:44,710 --> 00:19:46,269 this cell. 565 00:19:46,270 --> 00:19:48,069 If you do that, then future generations 566 00:19:48,070 --> 00:19:49,569 will also have the ability to make these 567 00:19:49,570 --> 00:19:50,409 proteins. 568 00:19:50,410 --> 00:19:51,699 So that's generally what you want to do 569 00:19:51,700 --> 00:19:53,140 if you're making a large scale product 570 00:19:54,490 --> 00:19:56,349 again when you scale this up. 571 00:19:56,350 --> 00:19:57,699 It's very much like brewing beer. 572 00:19:57,700 --> 00:19:59,139 In fact, if you're working with yeast, 573 00:19:59,140 --> 00:20:00,759 it's almost exactly like brewing beer. 574 00:20:02,290 --> 00:20:04,689 Of course, it needs nutrients. 575 00:20:04,690 --> 00:20:06,129 Sugar is one of the main things you have 576 00:20:06,130 --> 00:20:07,689 to feed these things. And this is where 577 00:20:07,690 --> 00:20:09,309 and this whole process most of your 578 00:20:09,310 --> 00:20:11,679 greenhouse gas emissions come from. 579 00:20:11,680 --> 00:20:13,059 Of course, sugar takes some energy to 580 00:20:13,060 --> 00:20:14,859 grow, take some resources. 581 00:20:14,860 --> 00:20:17,079 But this is a very small amount 582 00:20:17,080 --> 00:20:18,489 of resources compared to growing an 583 00:20:18,490 --> 00:20:20,799 animal. So this is still a big advantage. 584 00:20:22,940 --> 00:20:24,439 You don't have to separate out, and this 585 00:20:24,440 --> 00:20:26,239 can be one of the harder parts of this 586 00:20:26,240 --> 00:20:28,339 whole process, you need to separate 587 00:20:28,340 --> 00:20:29,989 your protein to have the pure material. 588 00:20:31,670 --> 00:20:33,619 And this actually is what people often 589 00:20:33,620 --> 00:20:35,989 spend the longest optimizing. 590 00:20:35,990 --> 00:20:37,579 But once you've done that, you have your 591 00:20:37,580 --> 00:20:38,819 pure protein. 592 00:20:38,820 --> 00:20:41,899 You can go on and and make your 593 00:20:41,900 --> 00:20:42,900 food replacement. 594 00:20:45,710 --> 00:20:47,959 So this is the project I work on, real 595 00:20:47,960 --> 00:20:50,119 vegan cheese, so we're a bunch 596 00:20:50,120 --> 00:20:52,240 of bio hackers in Oakland, 597 00:20:53,270 --> 00:20:55,369 we are engineering yeast to make 598 00:20:55,370 --> 00:20:57,499 milk proteins so that we can 599 00:20:57,500 --> 00:20:59,629 make cheese that is identical to real 600 00:20:59,630 --> 00:21:01,219 animal cheese because it's got the same 601 00:21:01,220 --> 00:21:02,220 protein in it. 602 00:21:03,380 --> 00:21:05,749 But it came from years, not from animals. 603 00:21:07,400 --> 00:21:09,409 And so our process is very much what I 604 00:21:09,410 --> 00:21:11,599 just described where we we didn't 605 00:21:11,600 --> 00:21:13,909 do this thing where we 606 00:21:13,910 --> 00:21:15,409 sequenced the genome ourselves. 607 00:21:15,410 --> 00:21:17,779 We found this on a database. 608 00:21:17,780 --> 00:21:19,999 We got the DNA synthesized, we put 609 00:21:20,000 --> 00:21:21,949 it into yeast. 610 00:21:21,950 --> 00:21:23,359 We get protein out and then we will 611 00:21:23,360 --> 00:21:25,009 formulate milk and make cheese. 612 00:21:25,010 --> 00:21:26,389 We haven't actually gotten to cheese yet, 613 00:21:26,390 --> 00:21:28,010 but yeah, this is the idea. 614 00:21:29,780 --> 00:21:31,999 We picked a particularly hard project 615 00:21:32,000 --> 00:21:33,469 and that most of the other things I 616 00:21:33,470 --> 00:21:35,749 mentioned are single 617 00:21:35,750 --> 00:21:38,539 proteins that people are using. 618 00:21:38,540 --> 00:21:39,739 We're actually doing something. 619 00:21:39,740 --> 00:21:42,649 Kason is a complex of four proteins. 620 00:21:42,650 --> 00:21:44,359 It's got a lot of other stuff in it. 621 00:21:44,360 --> 00:21:46,459 And so it turns out that we have a lot of 622 00:21:46,460 --> 00:21:48,169 work to do to get this whole myself to 623 00:21:48,170 --> 00:21:49,789 reform. 624 00:21:49,790 --> 00:21:51,949 But this is this is really 625 00:21:51,950 --> 00:21:53,119 what makes cheese cheese. 626 00:21:53,120 --> 00:21:55,159 It's this complex protein that we're that 627 00:21:55,160 --> 00:21:56,160 we're synthesizing. 628 00:21:58,070 --> 00:21:59,299 I'm not going to go through all the details 629 00:21:59,300 --> 00:22:01,459 here. I gave a talk at camp that 630 00:22:01,460 --> 00:22:03,169 if you want to see a lot of details on 631 00:22:03,170 --> 00:22:05,539 this project can be done in that talk. 632 00:22:08,240 --> 00:22:10,399 So I'll go just very 633 00:22:10,400 --> 00:22:12,109 briefly through our progress. 634 00:22:12,110 --> 00:22:14,029 Basically, we've made these proteins in 635 00:22:14,030 --> 00:22:15,439 very small amounts, we're pretty sure, 636 00:22:15,440 --> 00:22:17,149 but we had a lot of trouble identifying 637 00:22:17,150 --> 00:22:18,919 that we made the right ones because yeast 638 00:22:18,920 --> 00:22:20,809 makes other proteins the same size and 639 00:22:20,810 --> 00:22:22,909 our inexpensive method only tells us 640 00:22:22,910 --> 00:22:24,739 the size of the proteins we're making. 641 00:22:24,740 --> 00:22:26,089 So we went back and redesigned the 642 00:22:26,090 --> 00:22:27,709 protein to have a tag that will make it 643 00:22:27,710 --> 00:22:28,790 easier to find. 644 00:22:29,870 --> 00:22:32,539 We're also looking for 645 00:22:32,540 --> 00:22:34,669 other yeasts, not bakeries, 646 00:22:34,670 --> 00:22:36,379 that will give us higher output. 647 00:22:36,380 --> 00:22:38,449 But one important thing, most of us 648 00:22:38,450 --> 00:22:39,619 are not biologists. 649 00:22:39,620 --> 00:22:42,079 We don't put a lot of time into this. 650 00:22:42,080 --> 00:22:43,579 But we really want it to work, so we're 651 00:22:43,580 --> 00:22:45,949 actually just filed 652 00:22:45,950 --> 00:22:48,259 for a grant where we are 653 00:22:48,260 --> 00:22:49,729 trying to get some money to hire people 654 00:22:49,730 --> 00:22:51,349 to do this, but we're still going to keep 655 00:22:51,350 --> 00:22:53,929 it open source. So if we have people paid 656 00:22:53,930 --> 00:22:55,699 working on this, we'll still release all 657 00:22:55,700 --> 00:22:56,700 the data as we get it. 658 00:22:59,100 --> 00:23:00,809 So what else can you do with this? 659 00:23:00,810 --> 00:23:02,819 It turns out it's not just food you can 660 00:23:02,820 --> 00:23:03,719 make leather. 661 00:23:03,720 --> 00:23:05,969 Leather is mostly collagen 662 00:23:05,970 --> 00:23:08,549 and you can get used to make collagen 663 00:23:08,550 --> 00:23:11,129 and you can make very realistic leather. 664 00:23:11,130 --> 00:23:12,929 There's a company called Modern Medo and 665 00:23:12,930 --> 00:23:15,479 Brooklyn that is doing this. 666 00:23:15,480 --> 00:23:17,039 And another important thing, as I 667 00:23:17,040 --> 00:23:18,509 mentioned before, you get to optimize 668 00:23:18,510 --> 00:23:20,639 past just replacing the animal 669 00:23:20,640 --> 00:23:21,640 product. 670 00:23:22,310 --> 00:23:23,429 They say that they can make leather 671 00:23:23,430 --> 00:23:24,839 that's actually better than animal 672 00:23:24,840 --> 00:23:26,849 leather for many like strength's, tests 673 00:23:26,850 --> 00:23:28,829 for the way it feels. 674 00:23:28,830 --> 00:23:31,470 You get to engineer past what evolved. 675 00:23:32,740 --> 00:23:34,869 Another interesting thing that's not 676 00:23:34,870 --> 00:23:37,449 exactly replacing a current product, 677 00:23:37,450 --> 00:23:39,039 this company bought threads, is making 678 00:23:39,040 --> 00:23:41,469 spider silk garments 679 00:23:41,470 --> 00:23:43,569 and growing spider silk, and this 680 00:23:43,570 --> 00:23:46,059 is probably easier than farming spiders. 681 00:23:46,060 --> 00:23:48,189 I haven't tried myself, but 682 00:23:48,190 --> 00:23:49,390 I think I go with a yeast. 683 00:23:52,220 --> 00:23:53,989 So there's another egg replacement, and 684 00:23:53,990 --> 00:23:56,089 this is a genetic engineering one, again, 685 00:23:56,090 --> 00:23:58,249 in yeast and egg whites, 686 00:23:58,250 --> 00:24:00,259 there's really one important protein 687 00:24:00,260 --> 00:24:01,260 albumin. 688 00:24:03,260 --> 00:24:04,459 This can be made in yeast. 689 00:24:04,460 --> 00:24:06,619 And again, you get to engineer it, maybe 690 00:24:06,620 --> 00:24:08,959 even be better than egg whites. 691 00:24:08,960 --> 00:24:10,849 But another important thing, a couple of 692 00:24:10,850 --> 00:24:11,959 years ago in the US, 693 00:24:13,130 --> 00:24:15,019 a very large percentage of the egg laying 694 00:24:15,020 --> 00:24:16,639 hens died of avian flu. 695 00:24:16,640 --> 00:24:19,079 There was this huge outbreak. 696 00:24:19,080 --> 00:24:21,139 All these hens died. 697 00:24:21,140 --> 00:24:23,149 Egg prices went up 80 percent over a few 698 00:24:23,150 --> 00:24:24,150 months. 699 00:24:24,620 --> 00:24:26,419 It turns out the yeast is a much more 700 00:24:26,420 --> 00:24:27,529 secure supply chain. 701 00:24:28,850 --> 00:24:31,129 Disease is not likely to wipe 702 00:24:31,130 --> 00:24:32,659 out a bunch of stuff that you have in 703 00:24:32,660 --> 00:24:34,459 these fermenters that are separated from 704 00:24:34,460 --> 00:24:35,809 each other. 705 00:24:35,810 --> 00:24:37,129 You're not going to spread diseases 706 00:24:37,130 --> 00:24:37,579 easily. 707 00:24:37,580 --> 00:24:39,559 So this is very attractive from from that 708 00:24:39,560 --> 00:24:41,749 viewpoint. Also, you also 709 00:24:41,750 --> 00:24:42,950 are not going to get salmonella. 710 00:24:44,030 --> 00:24:46,129 And the other thing is that intensive 711 00:24:46,130 --> 00:24:48,229 bird and pig farming operations are where 712 00:24:48,230 --> 00:24:50,089 most human flus develop. 713 00:24:50,090 --> 00:24:51,739 They're spread from farms largely in 714 00:24:51,740 --> 00:24:54,079 China into human populations. 715 00:24:54,080 --> 00:24:55,699 That's why it's called swine flu. 716 00:24:55,700 --> 00:24:57,529 Bird flu, avian flu. 717 00:25:00,370 --> 00:25:02,769 They have this quote, that's not exactly 718 00:25:02,770 --> 00:25:05,139 appetizing, exactly, talking about 719 00:25:05,140 --> 00:25:06,729 their baking and binding applications 720 00:25:06,730 --> 00:25:08,499 team, but again, they're just saying that 721 00:25:08,500 --> 00:25:09,459 they can make something that's even 722 00:25:09,460 --> 00:25:10,269 better than egg whites. 723 00:25:10,270 --> 00:25:12,099 You get to optimize past the animal 724 00:25:12,100 --> 00:25:13,100 product. 725 00:25:14,380 --> 00:25:15,849 Another thing, gelatin, 726 00:25:16,990 --> 00:25:19,149 again, you can make it in yeast, it's 727 00:25:19,150 --> 00:25:21,879 usually made from animal bones and skin, 728 00:25:21,880 --> 00:25:23,979 but you can make it and said and some 729 00:25:23,980 --> 00:25:25,989 things you might recognize jello 730 00:25:25,990 --> 00:25:28,089 marshmallows if you take anything 731 00:25:28,090 --> 00:25:30,339 in a gel capsule pill that doesn't say 732 00:25:30,340 --> 00:25:32,529 that's vegetarian and that is gelatin. 733 00:25:32,530 --> 00:25:34,059 It's also used as an additive and 734 00:25:34,060 --> 00:25:36,099 thousands of processed foods. 735 00:25:36,100 --> 00:25:38,469 So this is a very common thing, something 736 00:25:38,470 --> 00:25:39,759 cool. 737 00:25:39,760 --> 00:25:42,729 This company made Mastodon gummy bears. 738 00:25:42,730 --> 00:25:44,259 They actually took the genetic sequence 739 00:25:44,260 --> 00:25:45,129 from mastodons. 740 00:25:45,130 --> 00:25:47,259 They have been they have been 741 00:25:47,260 --> 00:25:49,419 sequenced and used 742 00:25:49,420 --> 00:25:51,549 it to make gummy bears as 743 00:25:51,550 --> 00:25:53,710 if they had ground up mastodon bones. 744 00:25:54,850 --> 00:25:56,079 Pretty sure this is the only way you're 745 00:25:56,080 --> 00:25:57,969 ever going to have a Maston gummy bear, 746 00:25:59,200 --> 00:26:00,759 so again, you get to do completely new 747 00:26:00,760 --> 00:26:02,829 things with these technologies. 748 00:26:05,440 --> 00:26:07,509 So with genetic 749 00:26:07,510 --> 00:26:08,619 engineering, I haven't really talked 750 00:26:08,620 --> 00:26:10,689 about meat yet, 751 00:26:10,690 --> 00:26:12,309 and so this is the last thing I'll talk 752 00:26:12,310 --> 00:26:13,310 about. 753 00:26:14,490 --> 00:26:15,900 Where does cooked meat 754 00:26:17,280 --> 00:26:18,269 come from? 755 00:26:18,270 --> 00:26:19,560 The flavors, textures? 756 00:26:20,770 --> 00:26:22,349 Well, of course you need the protein you 757 00:26:22,350 --> 00:26:24,599 need the fat is a very sad but something 758 00:26:24,600 --> 00:26:25,589 else that's really important. 759 00:26:25,590 --> 00:26:27,209 If you want it to be a really realistic 760 00:26:27,210 --> 00:26:29,639 flavor, you need basically 761 00:26:29,640 --> 00:26:31,619 blood. You need the hemoglobin type 762 00:26:31,620 --> 00:26:32,620 proteins. 763 00:26:33,420 --> 00:26:36,029 So these proteins have a very specific 764 00:26:36,030 --> 00:26:37,319 flavor themselves. 765 00:26:37,320 --> 00:26:38,609 But something else really important, they 766 00:26:38,610 --> 00:26:40,599 do, they catalyze this reaction. 767 00:26:40,600 --> 00:26:42,839 So when you cook your 768 00:26:42,840 --> 00:26:44,849 proteins plus your team containing 769 00:26:44,850 --> 00:26:47,279 proteins together, you get 770 00:26:47,280 --> 00:26:49,109 I gave just a few examples here. 771 00:26:49,110 --> 00:26:50,429 There's actually thousands of small 772 00:26:50,430 --> 00:26:52,049 molecules that are made and this is what 773 00:26:52,050 --> 00:26:53,519 meat flavor is. 774 00:26:53,520 --> 00:26:55,919 So if you want to make your meat taste 775 00:26:55,920 --> 00:26:58,769 really like meat, you need to make these 776 00:26:58,770 --> 00:27:01,199 make these things, make him containing 777 00:27:01,200 --> 00:27:03,629 proteins occur almost 778 00:27:03,630 --> 00:27:05,039 entirely in animals. 779 00:27:05,040 --> 00:27:06,329 It turns out that there are some plant 780 00:27:06,330 --> 00:27:07,330 sources. 781 00:27:09,210 --> 00:27:11,729 So there's a company and possible foods 782 00:27:11,730 --> 00:27:12,730 near San Francisco. 783 00:27:13,830 --> 00:27:15,809 They're making a burger that is mostly 784 00:27:15,810 --> 00:27:17,579 made of wheat protein. 785 00:27:17,580 --> 00:27:19,799 And this is interesting because wheat 786 00:27:19,800 --> 00:27:21,929 protein has been used for over a thousand 787 00:27:21,930 --> 00:27:24,749 years in China to make meat substitutes. 788 00:27:24,750 --> 00:27:26,460 It's sometimes called Satan and 789 00:27:27,840 --> 00:27:30,149 but it has other names in China. 790 00:27:30,150 --> 00:27:32,039 It makes a very good texture, the flavor. 791 00:27:32,040 --> 00:27:33,239 It has very little flavor, but the 792 00:27:33,240 --> 00:27:34,469 textures, it's pretty good. 793 00:27:35,790 --> 00:27:37,679 So impossible. Foods also uses coconut 794 00:27:37,680 --> 00:27:39,269 fat. It turns out that animal fats are 795 00:27:39,270 --> 00:27:40,469 not really that unique. 796 00:27:40,470 --> 00:27:42,059 You can use a lot of other substitutes in 797 00:27:42,060 --> 00:27:43,060 there. OK. 798 00:27:44,430 --> 00:27:46,499 But they're there innovation, really, 799 00:27:47,530 --> 00:27:49,649 they take engineered yeast that doesn't 800 00:27:49,650 --> 00:27:52,889 show up in the final product, but they 801 00:27:52,890 --> 00:27:54,869 have engineered it to produce a hymn 802 00:27:54,870 --> 00:27:56,579 containing protein. So they basically get 803 00:27:56,580 --> 00:27:58,559 blood out of yeast. 804 00:27:58,560 --> 00:28:00,659 And so putting all this 805 00:28:00,660 --> 00:28:01,660 together 806 00:28:03,090 --> 00:28:05,039 and there's a there's a bigger picture of 807 00:28:05,040 --> 00:28:06,149 all their ingredients. 808 00:28:06,150 --> 00:28:07,369 That's what goes into their burger. 809 00:28:08,490 --> 00:28:09,809 There's a picture of what it looks like 810 00:28:09,810 --> 00:28:12,329 cooking, and there's a picture of the 811 00:28:12,330 --> 00:28:14,249 burger put together. 812 00:28:14,250 --> 00:28:15,629 I've had one of these. 813 00:28:15,630 --> 00:28:17,879 It is disturbing, I think, 814 00:28:17,880 --> 00:28:19,499 to me, because I'm a vegan and it tastes 815 00:28:19,500 --> 00:28:20,500 like a real hamburger. 816 00:28:23,460 --> 00:28:26,099 But also people who eat meat have 817 00:28:26,100 --> 00:28:27,989 largely said some people say it's not 818 00:28:27,990 --> 00:28:29,699 quite there, but most people say it's a 819 00:28:29,700 --> 00:28:32,039 pretty good burger replacement. 820 00:28:32,040 --> 00:28:33,040 So this is 821 00:28:34,740 --> 00:28:36,449 very, very similar. And is because of 822 00:28:36,450 --> 00:28:38,519 that catalyzing those reactions, 823 00:28:38,520 --> 00:28:40,679 you know, who are working to getting 824 00:28:40,680 --> 00:28:41,779 the texture right. 825 00:28:41,780 --> 00:28:43,319 But really, the flavor is that him 826 00:28:43,320 --> 00:28:44,999 protein that came from the yeast there, 827 00:28:45,000 --> 00:28:47,519 plant blood, as they call it, making 828 00:28:47,520 --> 00:28:49,019 the same reactions that make these small 829 00:28:49,020 --> 00:28:50,020 molecules. 830 00:28:51,120 --> 00:28:53,249 OK, so you can make all these different 831 00:28:53,250 --> 00:28:54,250 kinds of 832 00:28:55,680 --> 00:28:57,429 meat replacements, other animal product 833 00:28:57,430 --> 00:28:59,220 replacements by genetic engineering, 834 00:29:00,690 --> 00:29:03,629 by growing individual cells. 835 00:29:03,630 --> 00:29:05,629 But I've mostly besides my project, the 836 00:29:05,630 --> 00:29:07,349 Bacon Cheese Project I've just been 837 00:29:07,350 --> 00:29:08,430 telling you about. 838 00:29:09,570 --> 00:29:10,799 These are all startups I've talked about, 839 00:29:10,800 --> 00:29:11,759 actually. 840 00:29:11,760 --> 00:29:13,919 And so, yeah, that makes you think, well, 841 00:29:13,920 --> 00:29:16,529 OK, so is capitalism going to solve this 842 00:29:16,530 --> 00:29:19,050 and not entirely. 843 00:29:25,650 --> 00:29:26,999 But it depends on what question you're 844 00:29:27,000 --> 00:29:28,000 actually asking. 845 00:29:28,980 --> 00:29:31,079 So what capitalism is good at is 846 00:29:31,080 --> 00:29:32,549 if you have a new product and you think 847 00:29:32,550 --> 00:29:34,919 you can make money from it, making 848 00:29:34,920 --> 00:29:37,049 something that fits into that market, 849 00:29:37,050 --> 00:29:38,129 capitalism is really good. 850 00:29:38,130 --> 00:29:40,109 And there's another part they haven't 851 00:29:40,110 --> 00:29:42,239 been talking about, and that is that we 852 00:29:42,240 --> 00:29:43,559 already actually make enough food. 853 00:29:43,560 --> 00:29:46,049 It's got to have an environmental impact, 854 00:29:46,050 --> 00:29:47,459 but we're not feeding everybody. 855 00:29:47,460 --> 00:29:49,109 Capitalism's not going to solve that. 856 00:29:50,130 --> 00:29:52,229 So that part of the problem, nothing 857 00:29:52,230 --> 00:29:53,399 I've talked about here is going to 858 00:29:53,400 --> 00:29:54,400 address. 859 00:29:57,150 --> 00:29:59,309 So, again, we could all go vegan now 860 00:29:59,310 --> 00:30:00,659 and have a much lower impact. 861 00:30:00,660 --> 00:30:02,549 But I suspect that's not going to happen, 862 00:30:02,550 --> 00:30:03,749 which is why I'm excited about these 863 00:30:03,750 --> 00:30:05,909 projects. Basically, what they will allow 864 00:30:05,910 --> 00:30:07,769 you to do is if you're not willing to 865 00:30:07,770 --> 00:30:09,119 change your diet, you don't really have 866 00:30:09,120 --> 00:30:11,249 to. There will be things that probably 867 00:30:11,250 --> 00:30:14,369 whether you think or not, now, 868 00:30:14,370 --> 00:30:15,989 most of you will probably eventually be 869 00:30:15,990 --> 00:30:18,329 convinced by some of these products. 870 00:30:18,330 --> 00:30:20,129 The other thing is that they will be 871 00:30:20,130 --> 00:30:22,349 cheaper and once they're cheaper 872 00:30:22,350 --> 00:30:23,759 and if you think they're good enough, 873 00:30:23,760 --> 00:30:25,019 that's where people are going to start 874 00:30:25,020 --> 00:30:26,020 adopting them. 875 00:30:28,140 --> 00:30:29,190 And that's it. Thank you.