1 00:00:00,200 --> 00:00:02,880 Speaker 1: Thanks for tunion to text stuff. If you don't recognize 2 00:00:02,880 --> 00:00:06,000 Speaker 1: my voice, my name is Ozvoloshian and I'm here because 3 00:00:06,040 --> 00:00:09,160 Speaker 1: the inimitable Jonathan Strickland has passed the baton to Cara 4 00:00:09,240 --> 00:00:12,239 Speaker 1: Price and myself to host Tech Stuff. The show will 5 00:00:12,240 --> 00:00:15,000 Speaker 1: remain your home for all things tech, and all the 6 00:00:15,040 --> 00:00:18,759 Speaker 1: old episodes will remain available in this feed. Thanks for listening. 7 00:00:21,680 --> 00:00:25,440 Speaker 1: Welcome to Tech Stuff, a production of iHeart Podcasts and Kaleidoscope. 8 00:00:25,960 --> 00:00:28,640 Speaker 1: I'm Os Voloshian, and today will bring you the headlines 9 00:00:28,680 --> 00:00:32,400 Speaker 1: this week, including the UK government's demand for a back 10 00:00:32,479 --> 00:00:37,080 Speaker 1: door into all iPhone users encrypted accounts. Then, on today's 11 00:00:37,080 --> 00:00:41,080 Speaker 1: Tech Support, a new finding that using AI might make 12 00:00:41,200 --> 00:00:46,280 Speaker 1: human cognition quote atropeed and unprepared in our final segment, 13 00:00:46,680 --> 00:00:49,839 Speaker 1: and look at Elon Musk's government contracts. All of that 14 00:00:50,000 --> 00:00:53,760 Speaker 1: on this Week in Tech. It's Friday, February fourteenth. Happy 15 00:00:53,840 --> 00:01:02,160 Speaker 1: Valentine's another week, another lonely studio, especially painful on Valentine's Day. 16 00:01:02,240 --> 00:01:05,280 Speaker 1: Cara Price, we miss you, but hosting solo means I 17 00:01:05,319 --> 00:01:07,880 Speaker 1: do get to share a few extra headlines today, and 18 00:01:07,959 --> 00:01:10,520 Speaker 1: producer Eliza Dennis is here to go through them with me. 19 00:01:10,959 --> 00:01:14,560 Speaker 1: Eliza are you ready, absolutely, So let's start with a 20 00:01:14,600 --> 00:01:18,520 Speaker 1: story that's in your backyard. Lithium ion batteries in La. 21 00:01:19,120 --> 00:01:23,360 Speaker 2: Right, Lithium ion batteries are in all kinds of tech products, 22 00:01:23,440 --> 00:01:27,600 Speaker 2: so I'm positive there are just tons of abandoned batteries 23 00:01:27,600 --> 00:01:30,279 Speaker 2: across LA because of the wildfires. 24 00:01:30,800 --> 00:01:34,160 Speaker 1: Yes, in fact, the most in history, and the LAist 25 00:01:34,200 --> 00:01:37,080 Speaker 1: has a fascinating story on the cleanup. One of the 26 00:01:37,120 --> 00:01:40,039 Speaker 1: big issues is how easy it is for damage batteries 27 00:01:40,120 --> 00:01:43,880 Speaker 1: to ignite or even explode, and if they do catch fire, 28 00:01:44,000 --> 00:01:46,600 Speaker 1: they may take up to ten times more water to contain, 29 00:01:46,800 --> 00:01:49,440 Speaker 1: so you can imagine this is really not helpful in 30 00:01:49,440 --> 00:01:50,520 Speaker 1: a wildfire situation. 31 00:01:51,080 --> 00:01:54,000 Speaker 2: So the Palisades and Eaten fires out in LA are 32 00:01:54,280 --> 00:01:58,200 Speaker 2: one hundred percent contains now, thank god. But yes, this 33 00:01:58,400 --> 00:02:00,960 Speaker 2: definitely is a concern for them future. 34 00:02:01,000 --> 00:02:04,560 Speaker 1: Yes, but even more immediately once the fires are over. 35 00:02:05,040 --> 00:02:08,280 Speaker 1: The batteries can still explode weeks after they were initially overheated, 36 00:02:08,720 --> 00:02:11,239 Speaker 1: which makes cleaning up and disposing of them pretty difficult. 37 00:02:11,440 --> 00:02:14,440 Speaker 1: And also, even if they look undamaged, it turns out 38 00:02:14,480 --> 00:02:16,720 Speaker 1: they can be releasing toxic gas into the air or 39 00:02:16,800 --> 00:02:20,040 Speaker 1: leaching chemicals into the ground. And the EPA which is 40 00:02:20,160 --> 00:02:23,720 Speaker 1: overseeing the cleanup, said these fires burn more electric vehicle 41 00:02:23,760 --> 00:02:26,799 Speaker 1: batteries and home energy storage systems than any fires that 42 00:02:26,880 --> 00:02:31,040 Speaker 1: ever burnt before. According to Weiss, as of October last year, 43 00:02:31,400 --> 00:02:34,840 Speaker 1: there were over four hundred and thirty thousand teslas alone 44 00:02:34,880 --> 00:02:37,640 Speaker 1: in the LA area, and a number of these vehicles burn. 45 00:02:37,760 --> 00:02:40,240 Speaker 1: So the scale of the cleanup is pretty huge, and 46 00:02:40,280 --> 00:02:42,799 Speaker 1: it's a delicate operation, right. The EPA workers collect the 47 00:02:42,840 --> 00:02:46,600 Speaker 1: batteries and seal drums, bring them to staging areas, and 48 00:02:46,840 --> 00:02:48,920 Speaker 1: if they still have charge, the batteries are put in 49 00:02:48,960 --> 00:02:52,560 Speaker 1: this saltwater brine solution to decharge them, and then you 50 00:02:52,560 --> 00:02:54,600 Speaker 1: have to find a recycler who can salvage the minerals 51 00:02:54,600 --> 00:02:56,560 Speaker 1: from the batteries, which remain in high demand. 52 00:02:57,040 --> 00:02:59,560 Speaker 2: Yeah, this is so interesting because I just never thought 53 00:02:59,639 --> 00:03:02,520 Speaker 2: that an electric vehicle would kind of change the way 54 00:03:02,560 --> 00:03:06,080 Speaker 2: that we do disaster recovery. But here we are. So 55 00:03:06,880 --> 00:03:08,040 Speaker 2: you have another story for. 56 00:03:08,120 --> 00:03:10,519 Speaker 1: Me, Yes, Well, I can't resist stories coming out of 57 00:03:10,560 --> 00:03:13,240 Speaker 1: the UK, and I saw this headline the Washington Post 58 00:03:13,240 --> 00:03:16,440 Speaker 1: and I was pretty shocked. Actually, so security officials in 59 00:03:16,480 --> 00:03:19,760 Speaker 1: the UK have demanded that Apple create a backdoor that 60 00:03:19,760 --> 00:03:23,480 Speaker 1: would allow authorities to access any Apple user's data uploaded 61 00:03:23,520 --> 00:03:26,200 Speaker 1: to the cloud. Now, this wasn't a request to access 62 00:03:26,280 --> 00:03:29,480 Speaker 1: a specific user account, which law enforcement quite often does. 63 00:03:30,080 --> 00:03:33,240 Speaker 1: This was a request to have an unlimited back door. 64 00:03:34,200 --> 00:03:38,240 Speaker 2: Wow. I mean this feels like a real invasion of privacy. 65 00:03:38,680 --> 00:03:41,600 Speaker 1: Not only that the order was actually issued secretly. So 66 00:03:42,320 --> 00:03:46,080 Speaker 1: thanks to the Washington Post, democracy dies in darkness. While 67 00:03:46,080 --> 00:03:49,040 Speaker 1: you may think you're safe outside of the UK, that's 68 00:03:49,080 --> 00:03:52,520 Speaker 1: just not true if this order goes ahead, because anyone 69 00:03:52,560 --> 00:03:54,920 Speaker 1: around the world can be subject to these demands from 70 00:03:54,920 --> 00:03:57,760 Speaker 1: the UK government. So if for some reason the British 71 00:03:57,760 --> 00:04:00,560 Speaker 1: government want to look at your encrypted phone day and 72 00:04:00,600 --> 00:04:02,760 Speaker 1: this order goes through, they would just be able to 73 00:04:02,800 --> 00:04:03,080 Speaker 1: do that. 74 00:04:03,560 --> 00:04:06,360 Speaker 2: Can you tell me, like why now? iPhones have sort 75 00:04:06,360 --> 00:04:07,440 Speaker 2: of been around for a while. 76 00:04:07,960 --> 00:04:11,520 Speaker 1: Yeah. So this backdoor is for a specific type of 77 00:04:11,520 --> 00:04:14,280 Speaker 1: cloud storage that Apple started offering in twenty twenty two 78 00:04:14,400 --> 00:04:19,000 Speaker 1: called Advanced Data Protection. Most iCloud storage can be unlocked 79 00:04:19,000 --> 00:04:22,320 Speaker 1: by the user and by Apple, but with Advanced Data Protection, 80 00:04:22,680 --> 00:04:26,320 Speaker 1: only the user can access their data stare in the cloud. Basically, 81 00:04:26,400 --> 00:04:28,839 Speaker 1: it extends the end to end encryption of I messages 82 00:04:28,880 --> 00:04:31,760 Speaker 1: and FaceTime to all data on iCloud, but it's a 83 00:04:31,760 --> 00:04:33,760 Speaker 1: setting that users have to opt into. It's not turn 84 00:04:33,839 --> 00:04:37,320 Speaker 1: on my default and advanced data protection obviously offers more 85 00:04:37,320 --> 00:04:41,040 Speaker 1: security protections from hacking, but also from law enforcement who 86 00:04:41,120 --> 00:04:44,159 Speaker 1: often serve Apple with search warrants to access iCloud storage 87 00:04:44,200 --> 00:04:47,320 Speaker 1: and backups, often without the user ever knowing. 88 00:04:47,800 --> 00:04:50,239 Speaker 2: Right, And I do believe that this is a tension 89 00:04:50,400 --> 00:04:53,400 Speaker 2: that a lot of tech companies feel. There's definitely a 90 00:04:53,440 --> 00:04:56,760 Speaker 2: friction between law enforcement and the data privacy of their users. 91 00:04:57,200 --> 00:05:00,600 Speaker 1: Absolutely. You probably remember back in twenty six when the 92 00:05:00,640 --> 00:05:03,800 Speaker 1: FBI tried to order Apple to unlock the phone of 93 00:05:03,880 --> 00:05:07,320 Speaker 1: a dead terrorist. Apple fought back and the two groups 94 00:05:07,320 --> 00:05:10,279 Speaker 1: eventually came to a compromise letting the government scan the 95 00:05:10,279 --> 00:05:13,440 Speaker 1: phone for illegal material. But there's a similar tension around 96 00:05:13,440 --> 00:05:16,239 Speaker 1: this story. The Washington Post, who broke the story noted 97 00:05:16,240 --> 00:05:18,400 Speaker 1: that when Apple was warned about this order in March, 98 00:05:18,880 --> 00:05:22,680 Speaker 1: they threatened to quote publicly withdraw critical security features from 99 00:05:22,680 --> 00:05:26,440 Speaker 1: the UK market, depriving UK users of these protections. 100 00:05:27,320 --> 00:05:29,800 Speaker 2: Wow, I mean sounds like fighting words. 101 00:05:30,240 --> 00:05:33,719 Speaker 1: Yeah. And there's this brewing larger fight between Britain and 102 00:05:33,800 --> 00:05:37,359 Speaker 1: various EU countries and the American tech giants, which is 103 00:05:37,360 --> 00:05:40,520 Speaker 1: super interesting and something I'm sure we'll cover soon, but 104 00:05:41,000 --> 00:05:43,360 Speaker 1: that's a side point on this story. The key thing 105 00:05:43,400 --> 00:05:45,640 Speaker 1: here is that Apple knows, as I'm sure we all 106 00:05:45,640 --> 00:05:50,080 Speaker 1: do by now, that any backdoor or security workaround is also, 107 00:05:50,240 --> 00:05:53,960 Speaker 1: by definition, available to bad actors. On a brighter note, 108 00:05:54,080 --> 00:05:56,840 Speaker 1: this week, I'd like to shout out the latest web 109 00:05:56,880 --> 00:06:00,479 Speaker 1: telescope images which show the most beautiful image of a 110 00:06:00,560 --> 00:06:02,839 Speaker 1: new star forming in vivid definition. 111 00:06:03,240 --> 00:06:06,800 Speaker 2: Okay, yes, for our listeners, if you haven't seen any 112 00:06:06,800 --> 00:06:09,480 Speaker 2: of these images from the web telescope, please stop what 113 00:06:09,480 --> 00:06:12,800 Speaker 2: you're doing and go look at them. They are absolutely unbelievable. 114 00:06:13,120 --> 00:06:15,400 Speaker 1: They really are. So the Web is this space telescope 115 00:06:15,440 --> 00:06:18,560 Speaker 1: that takes stunningly sharp images and it looks deeper into 116 00:06:18,560 --> 00:06:22,760 Speaker 1: space than any previous telescope, which means actually looking further 117 00:06:22,839 --> 00:06:26,839 Speaker 1: back in time, around thirteen point five billion years into 118 00:06:26,880 --> 00:06:27,320 Speaker 1: the past. 119 00:06:27,640 --> 00:06:30,960 Speaker 2: Yeah. That will never stop blowing my mind. That looking 120 00:06:31,000 --> 00:06:33,960 Speaker 2: at the space pins looking back in time. So just 121 00:06:34,360 --> 00:06:36,960 Speaker 2: keep that in mind when you're looking at these images. 122 00:06:36,880 --> 00:06:40,400 Speaker 1: Exactly Well, this week's images really areline blowing. How to 123 00:06:40,440 --> 00:06:43,359 Speaker 1: describe an audio that, think of a vertical collision of 124 00:06:43,440 --> 00:06:48,040 Speaker 1: pain and blue translucent light, almost kaleidoscopic and what's truly 125 00:06:48,120 --> 00:06:51,200 Speaker 1: unbelievable is that this gorgeous array of colours contains newly 126 00:06:51,279 --> 00:06:54,680 Speaker 1: forming stars, and the image may help us understand a 127 00:06:54,680 --> 00:06:57,720 Speaker 1: little more about the environment in which planets are born. 128 00:06:58,200 --> 00:07:02,159 Speaker 2: This story makes me so happy. I absolutely love the 129 00:07:02,200 --> 00:07:03,680 Speaker 2: web telescope. 130 00:07:03,279 --> 00:07:06,039 Speaker 1: METO and there's actually this really beautiful documentary I saw 131 00:07:06,080 --> 00:07:08,760 Speaker 1: a few months ago called Deep Sky, all about the 132 00:07:08,760 --> 00:07:11,160 Speaker 1: Web Telescope. I saw it in Imax and it was 133 00:07:11,160 --> 00:07:14,600 Speaker 1: an incredible experience that I really do recommend. But that's 134 00:07:14,640 --> 00:07:17,240 Speaker 1: it for the headlines this week. Thanks for joining me, Eliza, 135 00:07:17,480 --> 00:07:20,960 Speaker 1: Happy to do it. Coming up on this week's Tech Support, 136 00:07:21,520 --> 00:07:24,600 Speaker 1: we discussed the potential impacts of AI on our cognition 137 00:07:25,040 --> 00:07:33,520 Speaker 1: stay with us, so today for our Tech Support segment, 138 00:07:33,520 --> 00:07:36,800 Speaker 1: we're turning to four or four Media's Immanuel Myberg to 139 00:07:36,880 --> 00:07:40,160 Speaker 1: discuss a question and a lingering fear I and I 140 00:07:40,240 --> 00:07:43,560 Speaker 1: think many of us have about the rise of generative AI, 141 00:07:44,240 --> 00:07:47,760 Speaker 1: which is will this make us dumber? That's definitely a 142 00:07:47,800 --> 00:07:50,600 Speaker 1: reductive statement, but it's not a million miles from what 143 00:07:50,640 --> 00:07:54,040 Speaker 1: a study from Microsoft recently found, which shows that AI 144 00:07:54,240 --> 00:07:57,760 Speaker 1: might affect our critical thinking skills. It's something that's been 145 00:07:57,800 --> 00:08:01,040 Speaker 1: asked every time a new technology is invented. For example, 146 00:08:01,040 --> 00:08:03,360 Speaker 1: when the radio came out, some people worried that reading 147 00:08:03,400 --> 00:08:07,120 Speaker 1: skills would diminish. In more recent generations, it's not been 148 00:08:07,320 --> 00:08:10,640 Speaker 1: uncommon to hear concerns about TV or video games or 149 00:08:10,680 --> 00:08:14,840 Speaker 1: smartphones rotting our brains. But in the case of this study, 150 00:08:15,320 --> 00:08:19,160 Speaker 1: that might actually be happening. So, Emmanuel, thanks so much 151 00:08:19,200 --> 00:08:20,360 Speaker 1: for joining us this week. 152 00:08:20,600 --> 00:08:21,720 Speaker 3: Yeah, thank you for having me. 153 00:08:21,960 --> 00:08:25,120 Speaker 1: You wrote a story with the headline Microsoft's study finds 154 00:08:25,160 --> 00:08:29,920 Speaker 1: AI makes human cognition quote atropheed and unprepared. With the subhead, 155 00:08:30,280 --> 00:08:33,120 Speaker 1: researchers find that the more people use AI at their job, 156 00:08:33,559 --> 00:08:36,640 Speaker 1: the less criticals thinking they may use I found this 157 00:08:36,679 --> 00:08:39,640 Speaker 1: absolutely fascinating. Can you tell us a bit about the study, 158 00:08:39,640 --> 00:08:41,680 Speaker 1: who conducted it, why, what it found. 159 00:08:42,000 --> 00:08:44,240 Speaker 3: So this was a study that was conducted primarily by 160 00:08:44,280 --> 00:08:48,880 Speaker 3: researchers at Microsoft, who collaborated with some researchers at Carnegie 161 00:08:48,920 --> 00:08:51,760 Speaker 3: Mellon University. And what they did for this study is 162 00:08:51,800 --> 00:08:56,599 Speaker 3: they recruited three hundred and nineteen knowledge workers. Knowledge workers 163 00:08:56,920 --> 00:09:00,559 Speaker 3: in this case refers to most people with an office job, right, 164 00:09:00,600 --> 00:09:04,800 Speaker 3: most people who work on computers who generate reports data 165 00:09:05,600 --> 00:09:06,120 Speaker 3: things like. 166 00:09:06,120 --> 00:09:08,920 Speaker 1: This to bankers, journalist, teachers. 167 00:09:08,640 --> 00:09:12,000 Speaker 3: Yeah, anything that, like most work that is not manual labor, 168 00:09:12,040 --> 00:09:15,120 Speaker 3: you can think of as knowledge workers, right. And they 169 00:09:15,480 --> 00:09:19,240 Speaker 3: collected nine hundred and thirty six first hand examples of 170 00:09:19,280 --> 00:09:22,960 Speaker 3: how these people used generative AI in their jobs and 171 00:09:23,000 --> 00:09:26,360 Speaker 3: then asked them to complete a questionnaire where they asked 172 00:09:26,360 --> 00:09:29,720 Speaker 3: them how they used THEAI tool, how they felt about 173 00:09:29,800 --> 00:09:32,760 Speaker 3: using the AI tool, how confident they were in the 174 00:09:32,800 --> 00:09:36,520 Speaker 3: AI tool's ability to give them a reliable, good output, 175 00:09:36,640 --> 00:09:40,520 Speaker 3: and how confident they were in their own ability to 176 00:09:40,600 --> 00:09:43,400 Speaker 3: do the job with or without the AI tool. 177 00:09:43,800 --> 00:09:47,160 Speaker 1: So, in Manuel, what were the conclusions of this study 178 00:09:47,200 --> 00:09:49,040 Speaker 1: that was partly led by Microsoft. 179 00:09:49,400 --> 00:09:52,240 Speaker 3: So the bottom line here is that the researchers found 180 00:09:52,360 --> 00:09:56,400 Speaker 3: that the more these knowledge workers were confident that the 181 00:09:56,480 --> 00:10:01,080 Speaker 3: generative AI tool was doing the job well enough, the 182 00:10:01,160 --> 00:10:04,959 Speaker 3: less likely they were to use critical thinking to make 183 00:10:04,960 --> 00:10:05,880 Speaker 3: sure it's correct. 184 00:10:06,160 --> 00:10:08,760 Speaker 1: We did a story on tech Stuff a few weeks 185 00:10:08,800 --> 00:10:14,679 Speaker 1: ago about how London taxi drivers have bigger hippocampuses than 186 00:10:15,120 --> 00:10:17,679 Speaker 1: the London bus drivers because they saw that they had 187 00:10:17,679 --> 00:10:19,440 Speaker 1: to kind of make out the route as they go 188 00:10:19,480 --> 00:10:22,920 Speaker 1: along versus following a preset route. I found that super fascinating. 189 00:10:22,920 --> 00:10:26,440 Speaker 1: You're smiling. How do you see this story in relation 190 00:10:26,480 --> 00:10:27,559 Speaker 1: to that story. 191 00:10:27,679 --> 00:10:31,240 Speaker 3: I share the personal anecdote in the story that I 192 00:10:31,280 --> 00:10:34,920 Speaker 3: think many people can relate to. So, when I first 193 00:10:34,960 --> 00:10:37,600 Speaker 3: moved out to college in San Francisco, it was a 194 00:10:37,600 --> 00:10:41,400 Speaker 3: little bit before the iPhone became very popular and everyone 195 00:10:41,520 --> 00:10:44,439 Speaker 3: had access to Google Maps on their phone, and I 196 00:10:44,480 --> 00:10:47,840 Speaker 3: would leave my house with a little mini map of 197 00:10:47,880 --> 00:10:51,040 Speaker 3: the city and all the bus routes, and doing that 198 00:10:51,120 --> 00:10:53,120 Speaker 3: for a year, you learn the city, and then you 199 00:10:53,240 --> 00:10:55,480 Speaker 3: just have an intuitive understanding of where you are and 200 00:10:55,480 --> 00:10:57,120 Speaker 3: relations just to the rest of the city, the rest 201 00:10:57,120 --> 00:10:58,760 Speaker 3: of the bus lines, and you know how to get 202 00:10:58,800 --> 00:11:01,840 Speaker 3: places without looking at your phone, which wasn't available to 203 00:11:01,840 --> 00:11:03,839 Speaker 3: me at the time. And ever since then, every time 204 00:11:03,880 --> 00:11:06,679 Speaker 3: I moved to a new city, I never learned the 205 00:11:07,400 --> 00:11:10,840 Speaker 3: city again in the same way because I relied on 206 00:11:10,880 --> 00:11:14,440 Speaker 3: the phone. Same thing with I'm old enough to remember 207 00:11:14,800 --> 00:11:18,079 Speaker 3: remembering my friend's phone numbers and my family's phone numbers. 208 00:11:18,280 --> 00:11:20,600 Speaker 3: Still remember some of those numbers, but ever since I 209 00:11:20,640 --> 00:11:23,080 Speaker 3: had a contact list on my phone, like, never remember 210 00:11:23,120 --> 00:11:24,480 Speaker 3: another phone number again. 211 00:11:25,160 --> 00:11:26,719 Speaker 1: This is one of the things I think about. Why 212 00:11:26,720 --> 00:11:31,920 Speaker 1: I'm grateful that I'm an elder millennial is because My 213 00:11:32,120 --> 00:11:36,480 Speaker 1: education was basically, you know, reading tons of things, memorizing 214 00:11:36,559 --> 00:11:39,720 Speaker 1: facts and then regurgitating them, probably in a way which 215 00:11:39,840 --> 00:11:43,199 Speaker 1: was below the standard of any LM today, well below 216 00:11:43,240 --> 00:11:45,920 Speaker 1: the standard. But the process of learning to do that 217 00:11:45,960 --> 00:11:48,200 Speaker 1: made me who I am today, and it's I find 218 00:11:48,240 --> 00:11:51,840 Speaker 1: it quite concerning to think that so many people don't 219 00:11:51,880 --> 00:11:54,480 Speaker 1: get the experience of having to train their own brain. 220 00:11:54,960 --> 00:11:59,160 Speaker 3: Yeah, it's a really interesting question that I think is 221 00:11:59,240 --> 00:12:03,360 Speaker 3: maybe bigger than the study, which is what do we 222 00:12:03,480 --> 00:12:09,000 Speaker 3: feel comfortable outsourcing to technology? Which parts of our brain 223 00:12:09,440 --> 00:12:12,600 Speaker 3: do we feel comfortable outsourcing in this way? So do 224 00:12:13,000 --> 00:12:15,959 Speaker 3: I need that muscle in my brain to remember ten 225 00:12:16,040 --> 00:12:18,720 Speaker 3: phone numbers? I don't know. Maybe that gray matter is 226 00:12:18,880 --> 00:12:21,360 Speaker 3: better used for another task. I feel fine about that, 227 00:12:21,880 --> 00:12:23,559 Speaker 3: but I feel less fine about the fact that I'm 228 00:12:23,600 --> 00:12:26,520 Speaker 3: not as good as I used to be at navigating 229 00:12:26,880 --> 00:12:30,400 Speaker 3: cities and so on. Obviously, to go back to the 230 00:12:30,440 --> 00:12:34,240 Speaker 3: fact that this comes from Microsoft, Microsoft is the biggest 231 00:12:34,280 --> 00:12:37,400 Speaker 3: investor in open AI. It's a giant tech company. Like 232 00:12:37,480 --> 00:12:40,920 Speaker 3: all the other giant tech companies, it is at this 233 00:12:40,960 --> 00:12:46,680 Speaker 3: point heavily invested in generative AI tools being widely adopted 234 00:12:47,480 --> 00:12:50,120 Speaker 3: and used by people in their personal lives and their 235 00:12:50,120 --> 00:12:51,640 Speaker 3: professional lives and so on. 236 00:12:51,960 --> 00:12:56,000 Speaker 1: So why are they funding and publishing research that drives 237 00:12:56,000 --> 00:12:58,840 Speaker 1: a whole news cycle about how AI might be bad 238 00:12:58,880 --> 00:12:59,240 Speaker 1: for us? 239 00:12:59,600 --> 00:13:01,560 Speaker 3: Yeah, the same reason that a lot of these AI 240 00:13:01,640 --> 00:13:08,280 Speaker 3: companies have these self reflective studies about the negative side 241 00:13:08,280 --> 00:13:11,560 Speaker 3: effects of AI tools. Right. So, I think last year 242 00:13:11,559 --> 00:13:14,319 Speaker 3: I wrote a story about Google doing a study about 243 00:13:14,520 --> 00:13:18,959 Speaker 3: how AI tools make it much easier to produce disinformation 244 00:13:19,040 --> 00:13:21,800 Speaker 3: and spread it online. But the purpose of that study 245 00:13:21,920 --> 00:13:24,320 Speaker 3: was for Google to say, we developed these tools, we 246 00:13:24,360 --> 00:13:28,160 Speaker 3: recognize this is a problem. Here's our idea for how 247 00:13:28,160 --> 00:13:30,720 Speaker 3: we can mitigate that problem to put you at ease 248 00:13:30,920 --> 00:13:34,360 Speaker 3: so you can feel more comfortable about buying our AI tools. 249 00:13:34,679 --> 00:13:38,360 Speaker 3: And similarly, Microsoft here is recognizing that this is a 250 00:13:38,400 --> 00:13:41,520 Speaker 3: problem and offering a solution so they can move forward 251 00:13:41,559 --> 00:13:43,960 Speaker 3: and developing and deploying these AI tools. 252 00:13:44,360 --> 00:13:46,199 Speaker 1: And how can it be mitigated? I mean, did any 253 00:13:46,200 --> 00:13:48,560 Speaker 1: of the users or the subjects that the study talk 254 00:13:48,640 --> 00:13:51,400 Speaker 1: about how AI is beneficial to them in their work 255 00:13:51,520 --> 00:13:54,360 Speaker 1: or what's the what's the optimistic takeaway here? 256 00:13:54,960 --> 00:13:57,880 Speaker 3: So in general, they note that there is a shift 257 00:13:58,280 --> 00:14:03,800 Speaker 3: between what they call people doing task execution to oversight, 258 00:14:04,559 --> 00:14:08,839 Speaker 3: and when you shift into that oversight role. That's when 259 00:14:09,000 --> 00:14:11,240 Speaker 3: things can get lax and you use less critical thinking 260 00:14:11,280 --> 00:14:14,920 Speaker 3: and mistakes can slip through, and that's bad. And what 261 00:14:14,960 --> 00:14:18,320 Speaker 3: they suggest is developing AI tools with this problem in 262 00:14:18,400 --> 00:14:21,720 Speaker 3: mind and sort of constantly encouraging the person who is 263 00:14:21,800 --> 00:14:25,000 Speaker 3: using the AI tool to use critical thinking to examine 264 00:14:25,040 --> 00:14:28,880 Speaker 3: the output. Like they say, they could offer guided critiques 265 00:14:29,040 --> 00:14:32,240 Speaker 3: for you know, how to approach an output from AI. 266 00:14:32,360 --> 00:14:34,480 Speaker 3: Something that I thought was really interesting and we've seen 267 00:14:34,640 --> 00:14:37,400 Speaker 3: maybe your listeners have heard about deep Seek, which was 268 00:14:37,440 --> 00:14:41,400 Speaker 3: this popular LLLM from China, and one of the things 269 00:14:41,440 --> 00:14:44,480 Speaker 3: that it does, and I think increasingly we're going to 270 00:14:44,560 --> 00:14:47,320 Speaker 3: see other AI tools do, is that you presented with 271 00:14:47,360 --> 00:14:50,320 Speaker 3: the problem and it doesn't just spit out the final answer. 272 00:14:50,680 --> 00:14:53,520 Speaker 3: It kind of shows you its quote unquote thought process. 273 00:14:53,840 --> 00:14:56,560 Speaker 1: Right, it's just so called reasoning models, right, yes. 274 00:14:56,320 --> 00:14:59,120 Speaker 3: Right, So it's showing you how it's getting to the conclusion. 275 00:14:59,120 --> 00:15:01,640 Speaker 3: It's presenting you at the and that's another thing that 276 00:15:01,680 --> 00:15:05,960 Speaker 3: the researchers here say might help make people continue to 277 00:15:06,240 --> 00:15:08,880 Speaker 3: think critically about the output by showing how the AI 278 00:15:09,400 --> 00:15:11,400 Speaker 3: is arriving at that conclusion. And when you look at that, 279 00:15:11,440 --> 00:15:13,840 Speaker 3: maybe you'll you'll be able to note, oh wait, this 280 00:15:13,920 --> 00:15:16,280 Speaker 3: is a bad process and this is a bad conclusion. 281 00:15:16,800 --> 00:15:18,600 Speaker 1: Well, it brings me back to my school days and 282 00:15:18,640 --> 00:15:23,960 Speaker 1: that very frustrating phrase show your workings. Yeah, do you 283 00:15:24,000 --> 00:15:25,720 Speaker 1: have any tips? I mean, it's somebody who's you know, 284 00:15:25,840 --> 00:15:28,520 Speaker 1: a journalist who covers this, but also as a person 285 00:15:28,560 --> 00:15:30,760 Speaker 1: in the world who spends more time thinking about this 286 00:15:30,880 --> 00:15:34,480 Speaker 1: than the average person, about how we should as regular 287 00:15:34,520 --> 00:15:37,560 Speaker 1: people think about finding the right balance and using AI 288 00:15:37,640 --> 00:15:40,520 Speaker 1: tools to help us versus kind of corroding our own 289 00:15:40,600 --> 00:15:42,800 Speaker 1: abilities on the past. 290 00:15:43,480 --> 00:15:47,080 Speaker 3: Ooh, that's a big question, I would say. I think 291 00:15:47,080 --> 00:15:49,280 Speaker 3: this is easy to forget. We're kind of early in 292 00:15:49,320 --> 00:15:52,640 Speaker 3: the game here about the widespread adoption of these tools, 293 00:15:52,880 --> 00:15:54,880 Speaker 3: and from what I see, a lot of our stories 294 00:15:54,920 --> 00:15:58,400 Speaker 3: are basically about the outputs being bad and the outputs 295 00:15:58,480 --> 00:16:03,440 Speaker 3: being wrong and ways that are not immediately obvious. So 296 00:16:04,200 --> 00:16:08,960 Speaker 3: today I would approach any output from any AI with 297 00:16:09,240 --> 00:16:12,560 Speaker 3: a lot of skepticism. Can it be useful? For sure? 298 00:16:13,880 --> 00:16:16,320 Speaker 3: Is it making people more efficient at work? For sure? 299 00:16:16,840 --> 00:16:20,680 Speaker 3: But like open aya has a new research tool, I 300 00:16:20,720 --> 00:16:23,960 Speaker 3: would never just trust the output blindly. I would have 301 00:16:24,000 --> 00:16:26,440 Speaker 3: to double check. Maybe it's a good jumping off point, right, 302 00:16:27,160 --> 00:16:30,320 Speaker 3: I remember in school they told us it's like Wikipedia 303 00:16:30,440 --> 00:16:32,280 Speaker 3: might be a good thing to look at, but it's 304 00:16:32,320 --> 00:16:34,440 Speaker 3: a jumping off point. It's not the end of your 305 00:16:34,480 --> 00:16:38,600 Speaker 3: research or something similar to that. As for in the future, 306 00:16:38,640 --> 00:16:42,040 Speaker 3: when these tools maybe get more competent, I think then 307 00:16:42,080 --> 00:16:47,280 Speaker 3: it's a question of personal choice. I enjoy the process 308 00:16:47,320 --> 00:16:50,240 Speaker 3: of writing. I'm going to continue to do it the 309 00:16:50,280 --> 00:16:52,720 Speaker 3: way that I've done it because that's what I like 310 00:16:52,800 --> 00:16:57,560 Speaker 3: to do, even if chat Gupte makes it instantaneous. 311 00:16:58,160 --> 00:17:00,120 Speaker 1: And Matanuel, thank you so much for joining us today. 312 00:17:00,720 --> 00:17:01,400 Speaker 3: Oh thank you. 313 00:17:04,160 --> 00:17:07,800 Speaker 1: Coming up a look at Elon Musk's history of government contracts. 314 00:17:08,200 --> 00:17:18,320 Speaker 1: Stay with us. If you're even mildly tuned into the news, 315 00:17:18,800 --> 00:17:21,760 Speaker 1: you can't escape the mention of the Department of Government 316 00:17:21,800 --> 00:17:27,520 Speaker 1: Efficiency aka DOGE. DOGE is also a cryptocurrency issued by Musk, 317 00:17:28,080 --> 00:17:30,840 Speaker 1: but it's not actually a department of government like Defense 318 00:17:30,960 --> 00:17:35,120 Speaker 1: or Treasury. It's an entity within the United States Digital Service, 319 00:17:35,440 --> 00:17:39,000 Speaker 1: which has now been renamed by Executive order to United 320 00:17:39,040 --> 00:17:42,400 Speaker 1: States DOGE Service, which means the head of the US 321 00:17:42,520 --> 00:17:45,520 Speaker 1: DOGE Service, Elon Musk, does not need to be vetted 322 00:17:45,640 --> 00:17:48,720 Speaker 1: or approved by Congress, which meant Elon had nothing to 323 00:17:48,720 --> 00:17:51,439 Speaker 1: stop him from getting straight to work, and so he 324 00:17:51,800 --> 00:17:55,320 Speaker 1: and Doge have been very, very, very busy. In the 325 00:17:55,320 --> 00:17:59,520 Speaker 1: twenty six days since President Trump's inauguration, Doge has been 326 00:17:59,640 --> 00:18:02,840 Speaker 1: tearing through the federal government officially in the name of 327 00:18:02,880 --> 00:18:07,960 Speaker 1: reform and efficiency. Government staffers across agencies are being asked 328 00:18:08,000 --> 00:18:11,199 Speaker 1: to resign, programs are being put on hold, Budgets are 329 00:18:11,200 --> 00:18:15,040 Speaker 1: getting slashed, sometimes with the assistance of AI, and this 330 00:18:15,200 --> 00:18:17,399 Speaker 1: is of course nothing new for the man at the 331 00:18:17,440 --> 00:18:21,080 Speaker 1: center of it all, Elon Musk. In fact, it mirrors 332 00:18:21,119 --> 00:18:25,360 Speaker 1: his managerial style at other companies slash first, fix later, 333 00:18:26,080 --> 00:18:28,080 Speaker 1: and he's shown time and again that he'd rather cut 334 00:18:28,160 --> 00:18:30,479 Speaker 1: too much and add back than be too gentle at 335 00:18:30,520 --> 00:18:34,080 Speaker 1: the first pass. As of this taping, those claims have 336 00:18:34,119 --> 00:18:37,720 Speaker 1: identified and cut over one billion dollars in government spending 337 00:18:38,240 --> 00:18:41,920 Speaker 1: the goal two trillion dollars, which means that while it's 338 00:18:41,920 --> 00:18:44,919 Speaker 1: already been a chaotic few weeks, there's no sign of 339 00:18:44,960 --> 00:18:49,160 Speaker 1: anything slowing down. This got me interested in how Musk 340 00:18:49,280 --> 00:18:53,320 Speaker 1: and his fellow Silicon Valley titans first intersected with government 341 00:18:53,359 --> 00:18:59,320 Speaker 1: spending and what their relationship to government efficiency really is, Because, 342 00:18:59,359 --> 00:19:02,440 Speaker 1: of course, efficiency is in the eye of the beholder. 343 00:19:02,960 --> 00:19:05,560 Speaker 1: To prepare for this segment, I had an interesting conversation 344 00:19:05,640 --> 00:19:08,959 Speaker 1: with David Eves, who's a professor of Digital Government at 345 00:19:09,080 --> 00:19:12,480 Speaker 1: University College London, and he said to me, quote, what's 346 00:19:12,480 --> 00:19:15,399 Speaker 1: disappointing is the lack of imagination for a bunch of 347 00:19:15,440 --> 00:19:18,919 Speaker 1: people who benefited from various forms of industrial policy to 348 00:19:18,960 --> 00:19:24,120 Speaker 1: have this simplistic shrink government is always attitude Tesla SpaceX. 349 00:19:24,240 --> 00:19:28,720 Speaker 1: These organizations exist because people chose to invest. Like they saw, 350 00:19:28,760 --> 00:19:31,479 Speaker 1: they had a vision and they chose to invest. Now 351 00:19:31,520 --> 00:19:34,480 Speaker 1: we'll have more of David's take later, but first let's 352 00:19:34,560 --> 00:19:38,520 Speaker 1: establish and baseline points. There is, indeed lots of spending 353 00:19:38,560 --> 00:19:41,800 Speaker 1: from Washington. The national debt is at about thirty two 354 00:19:41,960 --> 00:19:45,760 Speaker 1: trillion dollars right now, and surely, as in any complex entity, 355 00:19:46,200 --> 00:19:49,200 Speaker 1: whether a government or a massive corporation or a university, 356 00:19:49,800 --> 00:19:54,080 Speaker 1: not every dollar is getting much bang for its well buck. Still, 357 00:19:54,480 --> 00:19:57,280 Speaker 1: as much as there is interesting cutting costs and eliminating 358 00:19:57,359 --> 00:20:00,359 Speaker 1: debt from leaders in Congress, whether calling for cuts in 359 00:20:00,400 --> 00:20:03,679 Speaker 1: defense or the National Endowment for the Arts, many elected 360 00:20:03,720 --> 00:20:07,400 Speaker 1: officials and civil servants are strongly opposed to. This particular 361 00:20:07,480 --> 00:20:10,600 Speaker 1: style of cost cutting is very Silicon Valley very moved 362 00:20:10,600 --> 00:20:14,000 Speaker 1: fast and break things again. It's a tactic that seems 363 00:20:14,000 --> 00:20:16,800 Speaker 1: to have worked for the richest man alive in his 364 00:20:16,880 --> 00:20:20,199 Speaker 1: work in the private sector, specifically in Silicon Valley. But 365 00:20:20,280 --> 00:20:23,000 Speaker 1: what I find so interesting is the fact that Elon 366 00:20:23,119 --> 00:20:26,520 Speaker 1: Musk and his companies have actually benefited more than a 367 00:20:26,560 --> 00:20:30,760 Speaker 1: little from government spending. Some might even call it inefficiency. 368 00:20:31,400 --> 00:20:33,880 Speaker 1: According to The New York Times, Musk had around one 369 00:20:33,960 --> 00:20:39,000 Speaker 1: hundred contracts through seventeen different federal agencies last year alone, 370 00:20:39,040 --> 00:20:42,640 Speaker 1: and within the last decade, musks companies, SpaceX and Tesla 371 00:20:43,000 --> 00:20:47,359 Speaker 1: have received at least eighteen billion dollars in government contracts. Now, 372 00:20:47,520 --> 00:20:50,080 Speaker 1: tens of billions of dollars might sound like a pittance 373 00:20:50,119 --> 00:20:53,600 Speaker 1: next to Elon's estimated net worth, which fluctuates, but last 374 00:20:53,600 --> 00:20:56,760 Speaker 1: time I checked was around three hundred and eighty billion dollars. 375 00:20:57,400 --> 00:20:59,920 Speaker 1: But those government contracts have driven up the share price 376 00:21:00,080 --> 00:21:04,679 Speaker 1: his companies and personally enriched him, and the timing and 377 00:21:04,760 --> 00:21:07,359 Speaker 1: the nature of the government funding given to SpaceX and 378 00:21:07,400 --> 00:21:12,119 Speaker 1: Tesla has at times been crucial to their success. Let's 379 00:21:12,240 --> 00:21:16,119 Speaker 1: start with Tesla. Back in twenty ten, the company was 380 00:21:16,280 --> 00:21:18,280 Speaker 1: just starting out and had only sold a couple of 381 00:21:18,320 --> 00:21:22,520 Speaker 1: thousand cars two years before. They'd nearly gone bankrupt, but 382 00:21:22,680 --> 00:21:26,040 Speaker 1: then the Department of Energy stepped in and gave Tesla 383 00:21:26,119 --> 00:21:29,200 Speaker 1: a four hundred and sixty five million dollar loan, which 384 00:21:29,200 --> 00:21:32,520 Speaker 1: the company used to develop a new car model. Just 385 00:21:32,600 --> 00:21:35,399 Speaker 1: three years after this loan, in twenty fourteen, with the 386 00:21:35,400 --> 00:21:38,240 Speaker 1: model s on the market, the carmaker had sold just 387 00:21:38,280 --> 00:21:42,000 Speaker 1: over thirty one thousand cars. With that success, they were 388 00:21:42,040 --> 00:21:43,920 Speaker 1: able to pay back their loan to the Department of 389 00:21:44,040 --> 00:21:48,040 Speaker 1: Energy early. But some have pointed out that a private 390 00:21:48,080 --> 00:21:51,520 Speaker 1: sector investor would have demanded more than just a repayment 391 00:21:51,560 --> 00:21:54,000 Speaker 1: of the loan. They would have demanded a stake in 392 00:21:54,080 --> 00:21:58,040 Speaker 1: Tesla itself, which today will be worth many, many multiples 393 00:21:58,040 --> 00:22:01,160 Speaker 1: of what it was then. So why doesn't the taxpayer 394 00:22:01,160 --> 00:22:04,280 Speaker 1: see that benefit when they're taking the risk. There's this 395 00:22:04,400 --> 00:22:08,639 Speaker 1: concept in investing called the efficient frontier, which basically tells 396 00:22:08,720 --> 00:22:11,960 Speaker 1: investors how much risk is acceptable in proportion to a 397 00:22:11,960 --> 00:22:17,400 Speaker 1: potential reward. Per my favorite source investor Pedia quote, portfolios 398 00:22:17,400 --> 00:22:21,360 Speaker 1: that lie below the efficient frontier are suboptimal because they 399 00:22:21,359 --> 00:22:23,960 Speaker 1: do not provide enough return for the level of risk. 400 00:22:24,480 --> 00:22:28,080 Speaker 1: So if you're a US taxpayer, it kind of sucks 401 00:22:28,119 --> 00:22:29,879 Speaker 1: that you take on all the risk of bailing out 402 00:22:29,880 --> 00:22:33,240 Speaker 1: a company but get barely any return if it survives. 403 00:22:33,720 --> 00:22:37,679 Speaker 1: Remember the Auto bailout anyway. Since then, Tesla has been 404 00:22:37,720 --> 00:22:40,640 Speaker 1: awarded contracts for things like building solar panels to the government, 405 00:22:40,960 --> 00:22:44,800 Speaker 1: providing tactical vehicles for US embassies, money for things the 406 00:22:44,840 --> 00:22:48,879 Speaker 1: government deemed necessary. Then there's also the ev tax credit, 407 00:22:49,240 --> 00:22:52,360 Speaker 1: which existed during the early years of Tesla until twenty nineteen, 408 00:22:52,880 --> 00:22:57,080 Speaker 1: and then was reinstated during the Biden administration seventy five 409 00:22:57,240 --> 00:23:01,240 Speaker 1: hundred dollars to Americans who purchased an electric vehicle. No doubt, 410 00:23:01,240 --> 00:23:04,320 Speaker 1: this encouraged sales and allowed Tesla to set a slightly 411 00:23:04,400 --> 00:23:07,280 Speaker 1: higher price than the market may have allowed before the 412 00:23:07,280 --> 00:23:12,919 Speaker 1: tax credit. As for SpaceX, well, Musk's rocket manufacturer has 413 00:23:12,960 --> 00:23:16,840 Speaker 1: gotten a lot of discretionary government funding, in particular from 414 00:23:16,920 --> 00:23:20,760 Speaker 1: NASA and the Department of Defense. Musk's contracts with those 415 00:23:20,800 --> 00:23:23,879 Speaker 1: agencies total up to about fifteen billion dollars in the 416 00:23:23,960 --> 00:23:27,879 Speaker 1: last decade, for things like designing space landing systems and 417 00:23:28,119 --> 00:23:31,320 Speaker 1: government use of Starlink, which is a satellite network that 418 00:23:31,400 --> 00:23:35,880 Speaker 1: helps people in remote areas connect to the Internet. David Eves, 419 00:23:35,960 --> 00:23:38,959 Speaker 1: the professor, pointed out to me that a major turning 420 00:23:39,000 --> 00:23:42,600 Speaker 1: point for SpaceX was the Russian incursion into Ukraine in 421 00:23:42,680 --> 00:23:46,800 Speaker 1: twenty fourteen. Up until that point, the US had relied 422 00:23:46,880 --> 00:23:49,760 Speaker 1: on Russian made rocket engines to launch stuff into space, 423 00:23:50,400 --> 00:23:53,840 Speaker 1: but after the conflict in Ukraine, the US government deemed 424 00:23:53,840 --> 00:23:57,520 Speaker 1: this a threat to national security and created the National 425 00:23:57,520 --> 00:24:02,080 Speaker 1: Security Space Launch Program, which gave contracts to US companies 426 00:24:02,119 --> 00:24:06,520 Speaker 1: to develop rocket technology. SpaceX won one of these contracts. 427 00:24:06,880 --> 00:24:10,120 Speaker 1: As David pointed out, this was not a government decision 428 00:24:10,200 --> 00:24:14,159 Speaker 1: to maximize efficiency. The Russian rockets were cheap and plentiful, 429 00:24:14,600 --> 00:24:19,160 Speaker 1: but it arguably benefited the United States and certainly benefited SpaceX. 430 00:24:20,080 --> 00:24:22,720 Speaker 1: More money came must sway during the war in Ukraine 431 00:24:22,840 --> 00:24:26,359 Speaker 1: through Starlink, the satellite network. By the fall of twenty 432 00:24:26,400 --> 00:24:30,760 Speaker 1: twenty two, Ukraine had received more than twenty thousand Starlink terminals, 433 00:24:31,040 --> 00:24:35,240 Speaker 1: which aided in restoring military and civilian communications in the country. 434 00:24:36,080 --> 00:24:39,200 Speaker 1: Some of these terminals were donated by Starlink, but some 435 00:24:39,240 --> 00:24:44,760 Speaker 1: were purchased by USAID with government funds. That's USA, the 436 00:24:44,800 --> 00:24:49,239 Speaker 1: agency whose operations have essentially been halted by DOGE. In 437 00:24:49,240 --> 00:24:52,960 Speaker 1: twenty twenty one, SpaceX was awarded a multi billion dollar 438 00:24:53,040 --> 00:24:56,320 Speaker 1: contract to help land the first woman and first person 439 00:24:56,359 --> 00:24:59,959 Speaker 1: of color on the Moon as part of NASA's Artemis programme. 440 00:25:00,920 --> 00:25:05,120 Speaker 1: While Mask's DOGE is cutting DEI efforts across federal agencies, 441 00:25:05,400 --> 00:25:08,680 Speaker 1: SpaceX is still building a spacecraft for long term exploration 442 00:25:08,760 --> 00:25:12,720 Speaker 1: of the Moon, and for SpaceX, the contracts keep on rolling. 443 00:25:13,400 --> 00:25:15,800 Speaker 1: Last year, the company was selected to build a vehicle 444 00:25:15,800 --> 00:25:18,919 Speaker 1: that would take the International Space Station safely out of 445 00:25:19,000 --> 00:25:23,560 Speaker 1: orbit in twenty thirty due to its outdated technology and capabilities. 446 00:25:24,560 --> 00:25:27,280 Speaker 1: But in many ways, this is a story far bigger 447 00:25:27,320 --> 00:25:31,720 Speaker 1: than just Elon. As David Eves told me, quote, one 448 00:25:31,840 --> 00:25:36,080 Speaker 1: cannot underestimate the importance of the US federal government in 449 00:25:36,119 --> 00:25:40,000 Speaker 1: the creation of Silicon Valley. For example, you wouldn't have 450 00:25:40,000 --> 00:25:43,680 Speaker 1: the smartphone without the touchscreen, which was developed by researchers 451 00:25:43,720 --> 00:25:46,800 Speaker 1: at the University of Delaware with funding from the CIA 452 00:25:46,960 --> 00:25:50,560 Speaker 1: and the National Science Foundation, not to mention the countless 453 00:25:50,560 --> 00:25:54,359 Speaker 1: innovations through DARPER, the Research and Development Agency of the 454 00:25:54,400 --> 00:25:57,680 Speaker 1: Department of Defense. I mean, I think we all know this, 455 00:25:58,040 --> 00:26:01,160 Speaker 1: but we wouldn't even have the Internet without DARPER, which 456 00:26:01,200 --> 00:26:06,160 Speaker 1: funded the development of upernet, the Advanced Research Projects Agency network. 457 00:26:06,200 --> 00:26:09,920 Speaker 1: Back in the sixties, and this network was declared operational 458 00:26:09,920 --> 00:26:13,080 Speaker 1: in nineteen seventy one, and soon there was an early 459 00:26:13,119 --> 00:26:17,359 Speaker 1: form of email through advances in remote login and file transfer. 460 00:26:18,240 --> 00:26:22,320 Speaker 1: We wouldn't have GPS or autonomous vehicles without Darper or 461 00:26:22,359 --> 00:26:25,720 Speaker 1: digital virtual assistance like Siri, which were originally meant to 462 00:26:25,720 --> 00:26:30,119 Speaker 1: provide assistance to soldiers in the field. Back in the sixties, 463 00:26:30,400 --> 00:26:34,600 Speaker 1: one million dollars was provided to start building Arpernet, which 464 00:26:34,680 --> 00:26:38,199 Speaker 1: with inflation, would be over nine million dollars today. The 465 00:26:38,240 --> 00:26:42,000 Speaker 1: wealth creation that money has spurred, particularly in Silicon Valley, 466 00:26:42,240 --> 00:26:47,359 Speaker 1: has been completely insane. It just bears remembering many of 467 00:26:47,400 --> 00:26:49,600 Speaker 1: the inventions that are at the heart of so much 468 00:26:49,680 --> 00:26:52,879 Speaker 1: tech we use today were funded because someone in the 469 00:26:52,920 --> 00:26:56,960 Speaker 1: federal government said, here, have some money, this is important, 470 00:26:57,480 --> 00:27:00,960 Speaker 1: figure it out. And those priorities, of course change and 471 00:27:01,000 --> 00:27:04,679 Speaker 1: evolve from administration to administration. But one of the frustrating 472 00:27:04,720 --> 00:27:08,000 Speaker 1: things about watching Doge hack away agency budgets is that 473 00:27:08,040 --> 00:27:11,960 Speaker 1: there's very little transparency surrounding the tactics or motivations of 474 00:27:12,000 --> 00:27:16,320 Speaker 1: Elon Musk and the Department of Government Efficiency. Most of all, 475 00:27:16,359 --> 00:27:20,240 Speaker 1: I'm actually craving a definition of the word efficiency. A 476 00:27:20,280 --> 00:27:24,439 Speaker 1: final thought from David quote. The government's main role is 477 00:27:24,480 --> 00:27:27,919 Speaker 1: an aggregator of capital so it can make investments. And 478 00:27:28,000 --> 00:27:30,200 Speaker 1: if the public is robbed of its confidence in the 479 00:27:30,240 --> 00:27:35,080 Speaker 1: public sector's ability to make investments that generate public good outcomes, 480 00:27:35,359 --> 00:27:37,880 Speaker 1: then we've been robbed of a critical tool to address 481 00:27:37,880 --> 00:27:40,720 Speaker 1: the key problems of our time. Food for thought. But 482 00:27:40,920 --> 00:27:43,159 Speaker 1: that's all we have time for this week on tech Stuff. 483 00:27:46,119 --> 00:27:49,520 Speaker 1: For tech Stuff, I'm os Vloshin. This episode was produced 484 00:27:49,560 --> 00:27:53,119 Speaker 1: by Eliza Dennis, Victoria de Mingez, and Lizzie Jacobs. It 485 00:27:53,240 --> 00:27:57,040 Speaker 1: was executive produced by me Kara Price and Kate Osborne 486 00:27:57,040 --> 00:28:01,720 Speaker 1: for Kaleidoscope and Katrina Norvel b iheartpodt but he'd Fraser 487 00:28:01,800 --> 00:28:04,720 Speaker 1: is our engineer and Kyle Murdoch mixed this episode and 488 00:28:04,760 --> 00:28:07,800 Speaker 1: also wrote the theme song. Join us next Wednesday for 489 00:28:07,920 --> 00:28:11,080 Speaker 1: tex Stuff The Story, when we'll share an in depth 490 00:28:11,119 --> 00:28:14,760 Speaker 1: conversation with Harney Freed, the man the world turns to 491 00:28:15,080 --> 00:28:18,399 Speaker 1: when they aren't quite sure is this image real or not? 492 00:28:19,440 --> 00:28:22,360 Speaker 1: Please rate, review, and reach out to us at tech 493 00:28:22,400 --> 00:28:25,920 Speaker 1: Stuff Podcast at gmail dot com. We love hearing from you.