1
00:00:04,400 --> 00:00:12,360
Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Hey there,

2
00:00:12,360 --> 00:00:15,680
Speaker 1: and welcome to tech Stuff. I'm your host, jonvan Strickland.

3
00:00:15,680 --> 00:00:18,680
Speaker 1: I'm an executive producer with iHeart Podcasts and How the

4
00:00:18,760 --> 00:00:23,440
Speaker 1: tech Are. Yeah, welcome to my penultimate episode of hosting

5
00:00:23,480 --> 00:00:26,960
Speaker 1: tech Stuff. I mean, you know, second to last normal

6
00:00:27,120 --> 00:00:30,760
Speaker 1: tech Stuff. Anyway, I might occasionally host a special episode

7
00:00:30,800 --> 00:00:33,400
Speaker 1: that drops into the feed now and then I'll still

8
00:00:33,400 --> 00:00:36,760
Speaker 1: be around at iHeart Podcasts. I'll just be executive producer

9
00:00:36,840 --> 00:00:39,720
Speaker 1: rising for the most part. But anyway, we're going to

10
00:00:39,760 --> 00:00:42,800
Speaker 1: continue what we started in our last episode and talk

11
00:00:42,840 --> 00:00:46,000
Speaker 1: about some of the biggest tech stories that have unfolded

12
00:00:46,040 --> 00:00:49,160
Speaker 1: since tech Stuff first launched in mid two thousand and eight.

13
00:00:49,520 --> 00:00:52,080
Speaker 1: And in our last episode, I went through the second

14
00:00:52,080 --> 00:00:54,720
Speaker 1: half of two thousand and eight up through twenty fourteen.

15
00:00:55,000 --> 00:00:57,840
Speaker 1: Today we're gonna go twenty fifteen up to present day.

16
00:00:58,480 --> 00:01:02,800
Speaker 1: So that's more in years and I wrote more. So

17
00:01:03,000 --> 00:01:05,320
Speaker 1: this is going to be one of those long episodes.

18
00:01:05,480 --> 00:01:08,600
Speaker 1: But hey, I'm hanging up my boots, so you won't

19
00:01:08,640 --> 00:01:10,360
Speaker 1: be having to put up with me for too much longer.

20
00:01:10,400 --> 00:01:12,680
Speaker 1: So let's just go on this journey together, shall we.

21
00:01:13,160 --> 00:01:16,279
Speaker 1: So we're up to twenty fifteen. That's when Apple first

22
00:01:16,319 --> 00:01:19,959
Speaker 1: introduced the Apple Watch, and it would take a bit

23
00:01:20,000 --> 00:01:24,160
Speaker 1: of time, no pun intended to get momentum on this

24
00:01:24,280 --> 00:01:27,480
Speaker 1: product compared to other Apple products. But these days it

25
00:01:27,600 --> 00:01:30,240
Speaker 1: is the most popular brand of smartwatch here in the

26
00:01:30,360 --> 00:01:33,160
Speaker 1: United States and it holds like a thirty percent of

27
00:01:33,200 --> 00:01:36,800
Speaker 1: the market share globally. Wearables and smart watches had been

28
00:01:36,840 --> 00:01:39,400
Speaker 1: around for a while. It's not like Apple invented them.

29
00:01:39,720 --> 00:01:43,199
Speaker 1: And though Apple created a popular smart watch, I would

30
00:01:43,240 --> 00:01:46,800
Speaker 1: say that it didn't have the same sort of Cinderella

31
00:01:47,000 --> 00:01:51,000
Speaker 1: energy as the original iPod, or the iPhone or even

32
00:01:51,040 --> 00:01:54,920
Speaker 1: the iPad, at least not initially. So just as a reminder,

33
00:01:55,080 --> 00:01:58,200
Speaker 1: Apple didn't invent the smartphone, it didn't invent the MP

34
00:01:58,280 --> 00:02:00,960
Speaker 1: three player, it didn't invent the tablet computer. But it

35
00:02:01,000 --> 00:02:04,200
Speaker 1: did succeed where everyone else had not in that it

36
00:02:04,280 --> 00:02:08,200
Speaker 1: was able to create products that appealed to a wide

37
00:02:08,440 --> 00:02:12,880
Speaker 1: audience and not just like a narrow tech geek niche.

38
00:02:13,160 --> 00:02:16,720
Speaker 1: So there was every reason to expect that they would

39
00:02:16,720 --> 00:02:18,519
Speaker 1: do the same with smart watches. It would just take

40
00:02:18,560 --> 00:02:22,079
Speaker 1: a little bit longer for those to really become popular,

41
00:02:22,120 --> 00:02:25,400
Speaker 1: And I honestly don't know how popular they are in

42
00:02:25,400 --> 00:02:28,960
Speaker 1: comparison to Apple's other product lines. But another innovation that

43
00:02:29,040 --> 00:02:33,480
Speaker 1: was introduced in twenty fifteen was Tesla's autopilot feature. That's

44
00:02:33,560 --> 00:02:37,760
Speaker 1: the not really self driving mode that has made headlines

45
00:02:37,880 --> 00:02:41,960
Speaker 1: many times, often for tragic or unfortunate reasons. This year,

46
00:02:42,120 --> 00:02:45,240
Speaker 1: Miles Klee of Rolling Stone Magazine wrote a piece titled,

47
00:02:45,400 --> 00:02:49,520
Speaker 1: Tesla has highest rate of deadly accidents among car brands,

48
00:02:49,600 --> 00:02:54,000
Speaker 1: study fines. That's not great news. Meanwhile, President elect Trump's

49
00:02:54,040 --> 00:02:57,919
Speaker 1: transition team has recently recommended that the new regime in

50
00:02:57,960 --> 00:03:01,720
Speaker 1: the US repeal a rule that otherwise requires car companies

51
00:03:01,720 --> 00:03:05,720
Speaker 1: that have self driving technologies incorporated into their vehicles to

52
00:03:05,840 --> 00:03:09,560
Speaker 1: report on car crashes. So yeah, now the team is saying, hey,

53
00:03:09,840 --> 00:03:11,959
Speaker 1: we should get rid of that rule. And you know,

54
00:03:12,120 --> 00:03:14,280
Speaker 1: Elon Musk also happens to be heading up one of

55
00:03:14,320 --> 00:03:18,840
Speaker 1: the government departments in the new administration. So I suspect

56
00:03:18,880 --> 00:03:21,840
Speaker 1: this is just the beginning of a lot of these

57
00:03:22,000 --> 00:03:26,359
Speaker 1: kinds of arguments that are are designed to remove restrictions

58
00:03:26,360 --> 00:03:30,680
Speaker 1: and regulations and accountability to allow businesses to operate with

59
00:03:31,160 --> 00:03:34,120
Speaker 1: little to know oversight. In other news, Google did a

60
00:03:34,120 --> 00:03:38,880
Speaker 1: big old corporate restructuring by creating a company called Alphabet,

61
00:03:39,080 --> 00:03:42,160
Speaker 1: which acts as a holding company for Google and its

62
00:03:42,240 --> 00:03:46,960
Speaker 1: various spinoffs like YouTube and Android. In space news, Jeff

63
00:03:47,000 --> 00:03:52,320
Speaker 1: Bezos's private space company, Blue Origin successfully launched and landed

64
00:03:52,440 --> 00:03:56,600
Speaker 1: a reusable rocket called New Shepherd. SpaceX at that point

65
00:03:56,800 --> 00:03:59,320
Speaker 1: had not yet managed to do the same thing. They

66
00:03:59,360 --> 00:04:03,240
Speaker 1: had launched some spacecraft, but the landing had not really,

67
00:04:04,000 --> 00:04:06,200
Speaker 1: they hadn't stuck the landing. I guess it's safe to

68
00:04:06,240 --> 00:04:09,440
Speaker 1: say like sometimes their landings could be a little exclusive.

69
00:04:09,880 --> 00:04:13,360
Speaker 1: But obviously SpaceX would catch up to Blue Origin and

70
00:04:13,400 --> 00:04:16,480
Speaker 1: then like leave Blue Origin in the dust as far

71
00:04:16,560 --> 00:04:19,520
Speaker 1: as private space industry stuff is concerned. I mean, you're

72
00:04:19,520 --> 00:04:21,960
Speaker 1: going to see way more stories about SpaceX than Blue

73
00:04:21,960 --> 00:04:25,000
Speaker 1: Origin typically. But the big story that I want to

74
00:04:25,080 --> 00:04:30,160
Speaker 1: highlight from twenty fifteen came to be known as diesel Gate,

75
00:04:30,560 --> 00:04:34,240
Speaker 1: and it involved the car company Volkswagen. So in September

76
00:04:34,640 --> 00:04:38,920
Speaker 1: of twenty fifteen, the United States Environmental Protection Agency or EPA,

77
00:04:39,279 --> 00:04:45,120
Speaker 1: accused Volkswagen of violating the US Clean Air Act through trickery. Essentially,

78
00:04:45,279 --> 00:04:48,480
Speaker 1: the issue was that Volkswagen was using software, you know,

79
00:04:48,600 --> 00:04:52,640
Speaker 1: software technology in order to fool emissions testing equipment regarding

80
00:04:52,720 --> 00:04:56,800
Speaker 1: its diesel engine vehicles and the emissions that they put out.

81
00:04:57,200 --> 00:04:59,440
Speaker 1: So here's kind of how it worked from a very

82
00:04:59,520 --> 00:05:04,680
Speaker 1: high level. When a Volkswagen diesel vehicle detected that it

83
00:05:04,720 --> 00:05:08,760
Speaker 1: was being put into testing mode, it would artificially limit

84
00:05:08,800 --> 00:05:11,640
Speaker 1: the output of the engine so as not to exceed

85
00:05:11,800 --> 00:05:15,720
Speaker 1: emissions standards. Now it would look like the engine was

86
00:05:15,839 --> 00:05:20,120
Speaker 1: operating at normal capacity, but in fact it was purposefully

87
00:05:20,160 --> 00:05:23,560
Speaker 1: holding itself back. Once it detected it was out of

88
00:05:23,720 --> 00:05:27,440
Speaker 1: testing mode, the engine would switch back into normal operations,

89
00:05:27,520 --> 00:05:30,000
Speaker 1: at which point the vehicle would be emitting more than

90
00:05:30,080 --> 00:05:34,120
Speaker 1: restrictions would legally allow. So the investigation had actually been

91
00:05:34,160 --> 00:05:36,120
Speaker 1: going on for a couple of years at this point.

92
00:05:36,279 --> 00:05:39,680
Speaker 1: It started with some researchers at West Virginia's University and

93
00:05:39,800 --> 00:05:42,119
Speaker 1: they were finding some pretty odd readings with a couple

94
00:05:42,120 --> 00:05:45,880
Speaker 1: of Volkswagen diesel vehicles. Now they were using testing equipment

95
00:05:45,920 --> 00:05:49,080
Speaker 1: that allowed for on road tests. That meant the vehicle

96
00:05:49,160 --> 00:05:52,279
Speaker 1: was actually in use on roads at the time it

97
00:05:52,320 --> 00:05:55,440
Speaker 1: was traveling. Most emissions tests are done with a vehicle

98
00:05:55,480 --> 00:05:58,039
Speaker 1: that's stationary, it's not actually on the road, So the

99
00:05:58,080 --> 00:06:02,000
Speaker 1: researchers were seeing unusually high emissions ratings on the two

100
00:06:02,080 --> 00:06:05,680
Speaker 1: Volkswagen vehicles, and that merited further investigation because you know,

101
00:06:05,720 --> 00:06:09,920
Speaker 1: when they were routinely tested in normal emissions testing facilities,

102
00:06:10,200 --> 00:06:13,080
Speaker 1: they were under the limit, So something was going on here.

103
00:06:13,480 --> 00:06:15,960
Speaker 1: Their findings found their way to regulators at both the

104
00:06:16,000 --> 00:06:19,640
Speaker 1: California state level and the US federal level, and ultimately

105
00:06:19,720 --> 00:06:24,160
Speaker 1: led to the EPA accusing Volkswagen of deception. Other countries

106
00:06:24,200 --> 00:06:28,159
Speaker 1: subsequently investigated the matter and found similar results, and Volkswagen's

107
00:06:28,200 --> 00:06:33,559
Speaker 1: stock prices plummeted. The CEO resigned, and the company apologized for,

108
00:06:34,040 --> 00:06:38,359
Speaker 1: you know, essentially defrauding everybody. The gambit turned out to

109
00:06:38,400 --> 00:06:41,920
Speaker 1: be a costly one for Volkswagen. Estimates put the total

110
00:06:42,000 --> 00:06:45,919
Speaker 1: cost at nearly thirty five billion with a B dollars.

111
00:06:46,279 --> 00:06:49,360
Speaker 1: That's a hefty amount of fines and legal expenses and

112
00:06:49,400 --> 00:06:52,120
Speaker 1: replacement costs and that kind of thing. But beyond that,

113
00:06:52,520 --> 00:06:56,600
Speaker 1: there were some considerable non financial costs. In fact, they

114
00:06:56,600 --> 00:07:00,919
Speaker 1: were incalculable. For example, you can't really to fy the

115
00:07:00,960 --> 00:07:03,600
Speaker 1: amount of damage that was just done to the environment

116
00:07:04,040 --> 00:07:07,800
Speaker 1: or people's health, right like. That certainly was a factor,

117
00:07:07,880 --> 00:07:09,960
Speaker 1: but we don't really have a way of putting a

118
00:07:10,160 --> 00:07:13,000
Speaker 1: number on it. All in all, Diesel gait was an

119
00:07:13,000 --> 00:07:16,960
Speaker 1: example of corporate subterfuge and greed. And here's a fun

120
00:07:16,960 --> 00:07:20,120
Speaker 1: thing to think about. It's possible that organizations like the

121
00:07:20,160 --> 00:07:22,960
Speaker 1: EPA here in the United States are going to face

122
00:07:23,000 --> 00:07:26,640
Speaker 1: some pretty drastic cuts in funding or possibly even dissolution,

123
00:07:27,080 --> 00:07:30,119
Speaker 1: setting the stage for companies to pull similar stunts without

124
00:07:30,160 --> 00:07:32,440
Speaker 1: having to worry so much about being called to answer

125
00:07:32,480 --> 00:07:35,240
Speaker 1: for transgressions, you know, the way that Volkswagen did back

126
00:07:35,280 --> 00:07:39,000
Speaker 1: in twenty fifteen. If there are no regulatory agencies out

127
00:07:39,000 --> 00:07:41,600
Speaker 1: there to oversee it, then it's almost like you're not

128
00:07:41,680 --> 00:07:46,520
Speaker 1: doing it at all. Moving swiftly onwards to twenty sixteen, Okay,

129
00:07:46,920 --> 00:07:51,480
Speaker 1: twenty sixteen was a huge year for tech news. In

130
00:07:51,480 --> 00:07:54,080
Speaker 1: most of these years, when I was looking back, I'd

131
00:07:54,080 --> 00:07:56,040
Speaker 1: be like, oh, there's like two or three stories that

132
00:07:56,080 --> 00:07:59,280
Speaker 1: are contenders. But twenty sixteen just it was like the

133
00:07:59,400 --> 00:08:02,080
Speaker 1: year that just kept on giving. It was an election year,

134
00:08:02,240 --> 00:08:04,360
Speaker 1: and that was part of it here in the United States.

135
00:08:04,680 --> 00:08:09,680
Speaker 1: But in twenty sixteen, we got an early smart watch

136
00:08:10,040 --> 00:08:14,160
Speaker 1: that had been boosted through crowdfunding and boasted an e

137
00:08:14,360 --> 00:08:17,720
Speaker 1: ink display that maximized battery life. It kicked the bucket.

138
00:08:18,040 --> 00:08:21,960
Speaker 1: In twenty sixteen. This was the Pebble smart Watch, which

139
00:08:22,080 --> 00:08:27,400
Speaker 1: was an ambitious project, independent project. Ultimately it was not

140
00:08:27,520 --> 00:08:30,360
Speaker 1: able to succeed as a business. That was a bummer

141
00:08:30,400 --> 00:08:33,520
Speaker 1: for me personally. I had actually backed it after the

142
00:08:33,640 --> 00:08:37,640
Speaker 1: kickstarting campaign was already over it had already successfully completed

143
00:08:37,640 --> 00:08:39,920
Speaker 1: the Kickstart campaign. In fact, it was one of the

144
00:08:39,960 --> 00:08:44,640
Speaker 1: most successful campaigns at that time, and I had held

145
00:08:44,720 --> 00:08:47,200
Speaker 1: back on backing it. But then I went to Cees

146
00:08:47,320 --> 00:08:50,280
Speaker 1: and I saw a presentation about the Pebble smart Watch,

147
00:08:50,520 --> 00:08:52,679
Speaker 1: and that convinced me to put in a pre order,

148
00:08:52,960 --> 00:08:55,400
Speaker 1: and even though I got in too late, I ended

149
00:08:55,480 --> 00:08:58,680
Speaker 1: up getting a Kickstarter edition watch. It's one of the

150
00:08:58,720 --> 00:09:03,120
Speaker 1: red ones. I'm guessing that the people who were backing

151
00:09:03,160 --> 00:09:05,760
Speaker 1: it didn't order as many of the red ones, although

152
00:09:05,800 --> 00:09:07,719
Speaker 1: I thought the red one was really striking, which is

153
00:09:07,760 --> 00:09:09,959
Speaker 1: why I got it, so I guess that's why I

154
00:09:09,960 --> 00:09:12,679
Speaker 1: got a Kickstarter version. Anyway, I thought it was nifty,

155
00:09:13,000 --> 00:09:16,920
Speaker 1: but Pebble ultimately didn't have staying power and it folded

156
00:09:17,280 --> 00:09:21,120
Speaker 1: in twenty sixteen. Other notable tech stories from twenty sixteen

157
00:09:21,160 --> 00:09:25,520
Speaker 1: included the tragic reveal that a Tesla vehicle operating in

158
00:09:25,559 --> 00:09:29,040
Speaker 1: autopilot mode was involved in a fatal car accident that

159
00:09:29,080 --> 00:09:31,760
Speaker 1: would sadly not be the last time we would hear

160
00:09:32,040 --> 00:09:36,559
Speaker 1: something like that happening. The Hyperloop One company was plagued

161
00:09:36,600 --> 00:09:41,000
Speaker 1: by a crazy power struggle and various legal battles, which

162
00:09:41,040 --> 00:09:44,760
Speaker 1: meant we were not getting any closer to the science

163
00:09:44,840 --> 00:09:47,800
Speaker 1: fiction vision that Elon Musk had laid out a couple

164
00:09:47,800 --> 00:09:50,800
Speaker 1: of years earlier. If you don't remember what hyperloop is,

165
00:09:51,160 --> 00:09:54,600
Speaker 1: the concept is it's a transportation system meant to go

166
00:09:54,720 --> 00:09:57,520
Speaker 1: between like cities there may be a couple hundred or

167
00:09:57,600 --> 00:09:59,800
Speaker 1: few hundred miles away from each other, and it can

168
00:10:00,000 --> 00:10:05,920
Speaker 1: cysts in its original form as a tunnel that has

169
00:10:06,000 --> 00:10:08,400
Speaker 1: had most or all of the air pumped out of it,

170
00:10:08,480 --> 00:10:11,040
Speaker 1: so you have very very very low air pressure inside

171
00:10:11,080 --> 00:10:14,800
Speaker 1: the tunnel. You've got a magnet system or air bearings,

172
00:10:14,880 --> 00:10:17,400
Speaker 1: one of the two, or maybe both. It allows a

173
00:10:17,480 --> 00:10:21,680
Speaker 1: train to essentially hover above the floor of the tunnel

174
00:10:22,000 --> 00:10:25,560
Speaker 1: and travel with very little friction, so very little resistance,

175
00:10:25,880 --> 00:10:29,839
Speaker 1: and it allows the train to reach incredible top speeds

176
00:10:29,880 --> 00:10:33,720
Speaker 1: and thus make the trip in pretty short order while

177
00:10:33,760 --> 00:10:37,240
Speaker 1: being more efficient and less costly than air travel, less

178
00:10:37,320 --> 00:10:45,000
Speaker 1: environmentally pollutive, I guess than air travel as well, but Unfortunately,

179
00:10:45,480 --> 00:10:49,240
Speaker 1: hyper Loop one, because of this big battle, ended up

180
00:10:49,280 --> 00:10:53,559
Speaker 1: kind of getting again not to use a pun, but derailed.

181
00:10:53,920 --> 00:10:58,199
Speaker 1: Hyper Loop one would stick around, but would severely cut

182
00:10:58,320 --> 00:11:01,480
Speaker 1: back on its vision as far as what it's setting

183
00:11:01,520 --> 00:11:05,360
Speaker 1: out to do. Another big story that happened in twenty

184
00:11:05,440 --> 00:11:10,640
Speaker 1: sixteen is the explosive and that is intended controversy around

185
00:11:10,640 --> 00:11:14,800
Speaker 1: the Samsung Galaxy seven Note smartphones. So there were multiple

186
00:11:14,840 --> 00:11:18,800
Speaker 1: reports that detailed how these smartphones would spontaneously burst into

187
00:11:18,800 --> 00:11:22,520
Speaker 1: fire and explode. It led to lots of restrictions on

188
00:11:22,559 --> 00:11:25,520
Speaker 1: the phones. There were airlines that said, you cannot board

189
00:11:25,520 --> 00:11:27,280
Speaker 1: a plane if you're carrying one of these. You have

190
00:11:27,320 --> 00:11:29,640
Speaker 1: to leave it here. You can't take it on board

191
00:11:30,200 --> 00:11:35,319
Speaker 1: because these things had proven to be unstable. And it

192
00:11:35,400 --> 00:11:40,040
Speaker 1: turned out that the battery manufacturing process, which was actually

193
00:11:40,080 --> 00:11:42,520
Speaker 1: being carried out with two different companies and had two

194
00:11:42,559 --> 00:11:45,560
Speaker 1: separate issues, but they resulted in the same problem in

195
00:11:45,600 --> 00:11:48,920
Speaker 1: that they created short circuits inside the battery that would

196
00:11:49,000 --> 00:11:52,240
Speaker 1: lead to thermal runaway, meaning the battery would heat up

197
00:11:52,240 --> 00:11:54,360
Speaker 1: and heat up and heat up until it ultimately failed

198
00:11:54,679 --> 00:11:58,000
Speaker 1: and would explode. This was huge news, obviously, and it

199
00:11:58,080 --> 00:12:01,480
Speaker 1: cost Samsung millions of dollars in recalls and lawsuits. It

200
00:12:01,920 --> 00:12:05,319
Speaker 1: was a big story in twenty sixteen. There's so much more. Though,

201
00:12:05,440 --> 00:12:07,760
Speaker 1: Yahoo admitted that it had been the victim of a

202
00:12:07,800 --> 00:12:11,559
Speaker 1: massive data breach that year. That breach affected hundreds of

203
00:12:11,600 --> 00:12:14,520
Speaker 1: millions of people. We would find out later it essentially

204
00:12:14,559 --> 00:12:18,240
Speaker 1: affected anyone who had a Yahoo account at that time, Like,

205
00:12:18,320 --> 00:12:20,520
Speaker 1: even if you hadn't used it in a while, you

206
00:12:20,600 --> 00:12:25,200
Speaker 1: were affected. Also another cyber warfare kind of story, hackers

207
00:12:25,240 --> 00:12:28,400
Speaker 1: brought down a domain name server host in the fall

208
00:12:28,559 --> 00:12:32,840
Speaker 1: of that year, which in turn affected numerous major websites

209
00:12:32,880 --> 00:12:36,920
Speaker 1: and services like Netflix and Amazon, so they were unavailable

210
00:12:37,000 --> 00:12:40,360
Speaker 1: on again and off again in October twenty first, twenty sixteen. Like,

211
00:12:40,400 --> 00:12:43,120
Speaker 1: that was just a bad day if you were one

212
00:12:43,160 --> 00:12:45,360
Speaker 1: of those companies. That's just the tip of the iceberg.

213
00:12:45,400 --> 00:12:48,120
Speaker 1: A ton of stuff happened in twenty sixteen and tech,

214
00:12:48,160 --> 00:12:51,400
Speaker 1: but none of that is what I wanted to focus

215
00:12:51,480 --> 00:12:55,800
Speaker 1: on in this episode. Instead, for me, the story that

216
00:12:55,960 --> 00:12:59,800
Speaker 1: caught my attention most in twenty sixteen was kind of

217
00:12:59,840 --> 00:13:04,640
Speaker 1: the official beginning of the end for the biotech company, Thearrahnose.

218
00:13:04,920 --> 00:13:07,120
Speaker 1: It was literally the end of Therahnose that year. But

219
00:13:07,280 --> 00:13:10,200
Speaker 1: then the beginning of the end for the freedom of

220
00:13:10,240 --> 00:13:13,600
Speaker 1: the people who are leading Therahnose. So this was a

221
00:13:13,640 --> 00:13:16,839
Speaker 1: company that was founded by Elizabeth Holmes, and it was

222
00:13:16,880 --> 00:13:21,120
Speaker 1: a company that aimed to democratize medical testing. The idea

223
00:13:21,160 --> 00:13:23,800
Speaker 1: being that you would end up with a device the

224
00:13:23,840 --> 00:13:26,960
Speaker 1: size of a desktop printer that would allow the average

225
00:13:26,960 --> 00:13:29,280
Speaker 1: person the chance to screen themselves for more than one

226
00:13:29,400 --> 00:13:33,200
Speaker 1: hundred different diseases, all while just parting with the tiniest

227
00:13:33,320 --> 00:13:37,840
Speaker 1: micro drop of blood. Because Elizabeth Holmes said she was

228
00:13:38,040 --> 00:13:40,080
Speaker 1: scared of needles, and that was one of the reasons

229
00:13:40,120 --> 00:13:43,760
Speaker 1: she didn't, you know, she would kind of avoid medical checkups.

230
00:13:43,760 --> 00:13:45,400
Speaker 1: But she thought, said, well, if you could do it

231
00:13:45,480 --> 00:13:48,160
Speaker 1: where you could only need the smallest drop of blood

232
00:13:48,360 --> 00:13:51,400
Speaker 1: and you could run, you know, potentially hundreds of different

233
00:13:51,480 --> 00:13:53,920
Speaker 1: or more than one hundred different medical tests on that

234
00:13:53,960 --> 00:13:56,240
Speaker 1: little drop of blood, on a device that could sit

235
00:13:56,320 --> 00:13:58,520
Speaker 1: on top of your desk and find out you know,

236
00:13:59,040 --> 00:14:03,600
Speaker 1: what you need to look out for and what you know,

237
00:14:03,640 --> 00:14:05,640
Speaker 1: what your health was, and maybe if there were any

238
00:14:05,679 --> 00:14:07,839
Speaker 1: lifestyle changes you needed to make in order to live

239
00:14:07,880 --> 00:14:11,120
Speaker 1: a healthier life. It would open up options for people

240
00:14:11,120 --> 00:14:14,319
Speaker 1: that otherwise never would exist. That was the dream. When

241
00:14:14,360 --> 00:14:17,960
Speaker 1: we come back, I'll talk about the problem that dream

242
00:14:18,320 --> 00:14:20,400
Speaker 1: had when it came to meeting up with a thing

243
00:14:20,440 --> 00:14:22,760
Speaker 1: I like to call reality. But first, let's take a

244
00:14:22,840 --> 00:14:35,080
Speaker 1: quick break to thank our sponsors. Okay, we're back. So

245
00:14:35,320 --> 00:14:38,960
Speaker 1: I left off talking about twenty sixteen and Fairhos. So

246
00:14:39,800 --> 00:14:42,280
Speaker 1: here's an issue. This company's story was kind of the

247
00:14:42,280 --> 00:14:45,200
Speaker 1: stuff of Silicon Valley fairy tales, Like it was the

248
00:14:45,240 --> 00:14:48,760
Speaker 1: sort of thing that investors really get excited about. So

249
00:14:48,880 --> 00:14:52,880
Speaker 1: Holmes had been attending Stanford University before she dropped out

250
00:14:52,920 --> 00:14:57,320
Speaker 1: to create her company. She courted various investors, including really famous,

251
00:14:57,400 --> 00:15:01,240
Speaker 1: high profile ones like Oracle found under Larry Ellison was

252
00:15:01,240 --> 00:15:06,600
Speaker 1: one of them. The media conglomerate owner Rupert Murdoch was another.

253
00:15:06,920 --> 00:15:09,760
Speaker 1: And so she had raised hundreds of millions of dollars

254
00:15:09,880 --> 00:15:12,840
Speaker 1: to make her dream of becoming an iconic tech leader

255
00:15:12,880 --> 00:15:14,720
Speaker 1: come true. And I want to be clear about that. Like,

256
00:15:15,040 --> 00:15:18,440
Speaker 1: the technology was something that she thought would be really cool.

257
00:15:18,760 --> 00:15:23,160
Speaker 1: She seemed to be pretty passionate about it. But my interpretation,

258
00:15:23,360 --> 00:15:25,720
Speaker 1: which I admit is fully biased and I could be

259
00:15:26,080 --> 00:15:29,280
Speaker 1: entirely wrong, was more that she was interested in being

260
00:15:29,640 --> 00:15:34,040
Speaker 1: an icon than really, you know, having a belief and

261
00:15:34,120 --> 00:15:37,760
Speaker 1: knowledge about this technology. She wanted to be another Steve Jobs,

262
00:15:38,000 --> 00:15:40,360
Speaker 1: you know, be on the cover of magazines and be

263
00:15:40,640 --> 00:15:44,440
Speaker 1: talked about as like this visionary leader. The trouble was

264
00:15:44,760 --> 00:15:47,840
Speaker 1: her idea proved to be much less attainable than she

265
00:15:48,000 --> 00:15:51,760
Speaker 1: initially thought. In late twenty fifteen, Wall Street Journal's John

266
00:15:51,920 --> 00:15:56,000
Speaker 1: Kerrieru whose name I always butcher anyway, he broke the

267
00:15:56,080 --> 00:15:59,560
Speaker 1: story that Darrenos the company was actually built. On top

268
00:15:59,640 --> 00:16:04,359
Speaker 1: of this, that the company's supposed demonstrations of its technology

269
00:16:04,520 --> 00:16:09,160
Speaker 1: were in actuality frauds. They were relying heavily on existing

270
00:16:09,200 --> 00:16:13,360
Speaker 1: blood analysis technologies from other companies, so actual machines made

271
00:16:13,440 --> 00:16:17,440
Speaker 1: by other companies that did standard blood tests. They were

272
00:16:17,560 --> 00:16:21,080
Speaker 1: using those, but then claiming that the results came from

273
00:16:21,120 --> 00:16:25,880
Speaker 1: their own technology, and that they would take tiny blood

274
00:16:25,920 --> 00:16:29,080
Speaker 1: samples larger than a micro drop but still you know,

275
00:16:29,120 --> 00:16:32,920
Speaker 1: pretty small. Then they would dilute those samples and then

276
00:16:33,200 --> 00:16:37,520
Speaker 1: run tests on them using these other companies, you know, machines.

277
00:16:37,920 --> 00:16:41,400
Speaker 1: Then they would pass off whatever the results were as

278
00:16:41,520 --> 00:16:44,480
Speaker 1: their own work, while desperately trying to get their technology

279
00:16:44,520 --> 00:16:47,880
Speaker 1: to operate properly. The story didn't end there either. There

280
00:16:48,000 --> 00:16:51,160
Speaker 1: got worse, Like it unfolded that there had been advisors

281
00:16:51,160 --> 00:16:54,680
Speaker 1: who had told Holmes early on that her idea was

282
00:16:54,760 --> 00:16:59,080
Speaker 1: unrealistic from a technological and biological perspective, that just because

283
00:16:59,160 --> 00:17:01,160
Speaker 1: she wanted it to work doesn't mean it would work.

284
00:17:01,520 --> 00:17:03,760
Speaker 1: So essentially it turned out to be kind of an

285
00:17:03,760 --> 00:17:07,959
Speaker 1: Emperor has No Clothes situation, and the company would totally

286
00:17:08,040 --> 00:17:11,960
Speaker 1: unravel throughout twenty sixteen. In the process, Holmes, who had

287
00:17:11,960 --> 00:17:16,240
Speaker 1: developed a reputation for extravagance and peculiar habits, found her

288
00:17:16,280 --> 00:17:19,480
Speaker 1: net worth plunge to zero. She went from being one

289
00:17:19,520 --> 00:17:23,199
Speaker 1: of the wealthiest women on the planet to having no

290
00:17:23,600 --> 00:17:26,960
Speaker 1: net worth at all. The SEC opened an investigation into

291
00:17:27,000 --> 00:17:30,120
Speaker 1: the company. Holmes and her business partner and on again

292
00:17:30,200 --> 00:17:34,560
Speaker 1: off again romantic partner, Ramesh Malwani, would ultimately stand trial

293
00:17:34,600 --> 00:17:38,320
Speaker 1: for multiple criminal charges of fraud and related crimes. Holmes

294
00:17:38,359 --> 00:17:41,520
Speaker 1: then would be found guilty and sentenced to eleven years

295
00:17:41,560 --> 00:17:45,320
Speaker 1: in prison, although that did get reduced a couple of times. Currently,

296
00:17:45,359 --> 00:17:48,639
Speaker 1: I believe she will be eligible for release in the

297
00:17:48,640 --> 00:17:52,120
Speaker 1: summer of twenty thirty two. Balwani would get a similar

298
00:17:52,160 --> 00:17:55,240
Speaker 1: sentence of nearly thirteen years for his part in the

299
00:17:55,280 --> 00:17:58,639
Speaker 1: deception and the reason I wanted to highlight therahos in

300
00:17:58,720 --> 00:18:01,520
Speaker 1: this episode is because I think it really shines a

301
00:18:01,640 --> 00:18:05,040
Speaker 1: light on some real issues in tech in general. So

302
00:18:05,520 --> 00:18:10,000
Speaker 1: Issue one, companies overhype the capabilities of their technologies just

303
00:18:10,520 --> 00:18:13,080
Speaker 1: par for the course. Here. Some folks refer to this

304
00:18:13,119 --> 00:18:17,560
Speaker 1: as marketing. I call it lying. Issue two. Their nose

305
00:18:17,640 --> 00:18:20,360
Speaker 1: really strips bear the dangers of the fake it till

306
00:18:20,400 --> 00:18:23,200
Speaker 1: you make it philosophy that you know, folks will assume

307
00:18:23,560 --> 00:18:27,320
Speaker 1: that if they can imagine it, that it's actually possible,

308
00:18:27,560 --> 00:18:31,120
Speaker 1: and they'll build a business idea or an actual business

309
00:18:31,160 --> 00:18:35,000
Speaker 1: around this concept before finding out if it really is

310
00:18:35,440 --> 00:18:38,840
Speaker 1: possible or not, and by then it's too late. So

311
00:18:38,880 --> 00:18:41,400
Speaker 1: you sell yourself on this idea, and then you scramble

312
00:18:41,480 --> 00:18:43,520
Speaker 1: to try and make the idea actually work in the

313
00:18:43,560 --> 00:18:46,800
Speaker 1: real world. It is not a good approach. I mean,

314
00:18:46,840 --> 00:18:50,440
Speaker 1: when it works, it's great. It just very rarely works.

315
00:18:50,760 --> 00:18:53,640
Speaker 1: Issue three, and this is relating to the last one.

316
00:18:53,880 --> 00:18:57,119
Speaker 1: It reminds us that while technology is incredible and it

317
00:18:57,280 --> 00:18:59,960
Speaker 1: can let us do stuff that previous generations would have

318
00:19:00,080 --> 00:19:04,520
Speaker 1: thought was impossible, technology is not without limitations. It's not

319
00:19:04,640 --> 00:19:06,920
Speaker 1: some sort of magic get out of jail free card

320
00:19:07,000 --> 00:19:12,520
Speaker 1: for all situations. Issue for the trappings of the tech

321
00:19:12,960 --> 00:19:17,959
Speaker 1: icon identity are really tempting, you know, from wealth or

322
00:19:18,520 --> 00:19:22,040
Speaker 1: influence to the public perception that you're someone who's just

323
00:19:22,119 --> 00:19:25,960
Speaker 1: really innovative and savvy. There's a lot to envy there,

324
00:19:26,359 --> 00:19:28,679
Speaker 1: so you can understand why someone would want to be

325
00:19:28,760 --> 00:19:33,240
Speaker 1: in that category. Issue five. Investors can sometimes propel a

326
00:19:33,280 --> 00:19:36,879
Speaker 1: bad idea into the world despite the fact it's a

327
00:19:36,920 --> 00:19:40,680
Speaker 1: bad idea, and that wouldn't be an issue except that

328
00:19:40,960 --> 00:19:44,280
Speaker 1: in the course of things, other people who are innocent

329
00:19:44,400 --> 00:19:46,800
Speaker 1: can end up suffering as a result of this. So

330
00:19:46,920 --> 00:19:51,240
Speaker 1: with the Theradouse case, that included patients who were relying

331
00:19:51,400 --> 00:19:55,040
Speaker 1: on what ended up being unreliable medical results. So they

332
00:19:55,040 --> 00:19:58,120
Speaker 1: are like actual people, sick people who were you know,

333
00:19:58,320 --> 00:20:02,119
Speaker 1: making use of this supposed tech and there's a chance

334
00:20:02,200 --> 00:20:04,119
Speaker 1: that some of the stuff they got back was not

335
00:20:04,280 --> 00:20:08,439
Speaker 1: at all reliable, which that's life endangering. That's terrible. So

336
00:20:08,720 --> 00:20:10,960
Speaker 1: I think Fraans is going to be a case study

337
00:20:11,200 --> 00:20:13,639
Speaker 1: for many, many years. It's a lesson on what not

338
00:20:13,920 --> 00:20:16,240
Speaker 1: to do and how not to do it, and I

339
00:20:16,280 --> 00:20:19,360
Speaker 1: hope a lot of people take that to heart and they,

340
00:20:19,480 --> 00:20:23,240
Speaker 1: you know, they learn from those mistakes. Let's move on

341
00:20:23,800 --> 00:20:27,200
Speaker 1: to twenty seventeen. Holy cow, this is taking forever. It's

342
00:20:27,200 --> 00:20:30,760
Speaker 1: almost as long as living through those years in real time. Anyway,

343
00:20:30,920 --> 00:20:35,400
Speaker 1: twenty seventeen, mega venture capital fund company soft Bank invested

344
00:20:35,560 --> 00:20:38,480
Speaker 1: hundreds of millions of dollars into a company that was

345
00:20:38,760 --> 00:20:41,760
Speaker 1: portrayed as if it were a tech company, but it

346
00:20:41,800 --> 00:20:45,639
Speaker 1: really wasn't. In reality, all it was doing was little

347
00:20:45,640 --> 00:20:49,560
Speaker 1: more than subleasing office space to clients, and that company

348
00:20:49,840 --> 00:20:53,200
Speaker 1: was we Work. Now, over the following years, soft Bank

349
00:20:53,240 --> 00:20:56,200
Speaker 1: would increase this investment to the point where it's spent

350
00:20:56,320 --> 00:21:01,200
Speaker 1: around sixteen billion with a B dollars on work, not

351
00:21:01,280 --> 00:21:04,600
Speaker 1: just in investments, but you know, overall. We Work meanwhile,

352
00:21:04,920 --> 00:21:08,800
Speaker 1: would prep itself for an initial public offering or IPO

353
00:21:09,119 --> 00:21:14,320
Speaker 1: in twenty nineteen. Unfortunately, that public offering imploded before it

354
00:21:14,320 --> 00:21:18,679
Speaker 1: could happen. It was kind of an absurd situation, like

355
00:21:18,720 --> 00:21:23,280
Speaker 1: there were corporate documents that just read like new age,

356
00:21:23,640 --> 00:21:29,320
Speaker 1: you know, ridiculous meaningless babble. Anyway, we Work did not

357
00:21:29,560 --> 00:21:33,080
Speaker 1: go public, and it then started to go into a

358
00:21:33,080 --> 00:21:36,840
Speaker 1: period of decline and actually went into bankruptcy protection in

359
00:21:36,880 --> 00:21:39,920
Speaker 1: twenty twenty three. And it didn't go away, It didn't

360
00:21:39,920 --> 00:21:42,680
Speaker 1: go out of business. It did go into bankruptcy, but

361
00:21:42,720 --> 00:21:45,280
Speaker 1: it's still around. Some view we work as sort of

362
00:21:45,320 --> 00:21:50,000
Speaker 1: a cautionary tale about the startup ecosystem, and as crazy

363
00:21:50,000 --> 00:21:53,000
Speaker 1: as that story is, it's still not the one I

364
00:21:53,080 --> 00:21:56,920
Speaker 1: picked to focus on this episode. Other things that happened

365
00:21:56,920 --> 00:21:59,320
Speaker 1: in twenty seventeen. There were a lot more data breaches

366
00:21:59,359 --> 00:22:02,680
Speaker 1: that year, including equa Fax, which is a credit reporting

367
00:22:02,720 --> 00:22:06,360
Speaker 1: company that was huge like bad bad news. I mean,

368
00:22:06,400 --> 00:22:10,600
Speaker 1: you expect an institution like a credit reporting company to

369
00:22:10,640 --> 00:22:15,880
Speaker 1: have really strong security measures because that's incredibly sensitive information

370
00:22:15,920 --> 00:22:18,400
Speaker 1: that affects millions of people, Like we're all lumped into

371
00:22:18,440 --> 00:22:21,640
Speaker 1: that system here in the United States. You know, everyone's

372
00:22:21,680 --> 00:22:24,560
Speaker 1: got their credit rating and everything, so having that information

373
00:22:24,680 --> 00:22:29,400
Speaker 1: get breached very damaging. The Yahoo breach I mentioned earlier

374
00:22:30,000 --> 00:22:34,720
Speaker 1: would end up becoming more clear in twenty seventeen. That's

375
00:22:34,760 --> 00:22:38,119
Speaker 1: when we learned that essentially everybody was whoever had a

376
00:22:38,200 --> 00:22:40,800
Speaker 1: Yahoo account was affected by it. And it also had

377
00:22:40,840 --> 00:22:45,359
Speaker 1: a huge impact on an acquisition because Verizon was buying Yahoo,

378
00:22:46,040 --> 00:22:49,680
Speaker 1: and it ended up affecting that acquisition deal by knocking

379
00:22:49,720 --> 00:22:52,760
Speaker 1: off around three hundred and fifty million dollars off Yahoo's

380
00:22:52,840 --> 00:22:57,080
Speaker 1: sales price. So yikes. Facebook's mark Zuckerberg was called to

381
00:22:57,080 --> 00:22:59,760
Speaker 1: face questioning from Congress, and not for the last time,

382
00:23:00,280 --> 00:23:04,200
Speaker 1: after Facebook had served up political ads that turned out

383
00:23:04,240 --> 00:23:07,720
Speaker 1: to have been paid for by Russian groups, and these

384
00:23:07,720 --> 00:23:10,199
Speaker 1: were political ads for the United States, so they were

385
00:23:10,240 --> 00:23:13,320
Speaker 1: intended to sway Americans who were voting in the twenty

386
00:23:13,320 --> 00:23:16,480
Speaker 1: sixteen presidential elections, and this started up a conversation in

387
00:23:16,520 --> 00:23:20,600
Speaker 1: America about how to protect against foreign interference in American elections,

388
00:23:20,800 --> 00:23:25,159
Speaker 1: something that hadn't really taken like the headlines by storm

389
00:23:25,200 --> 00:23:28,520
Speaker 1: in previous years, but this time it really was. Fortunately,

390
00:23:28,800 --> 00:23:31,400
Speaker 1: we have all of that sorted out today and there's

391
00:23:31,440 --> 00:23:36,440
Speaker 1: no more problem. That was me being sarcastic. Cryptocurrency boomed

392
00:23:36,440 --> 00:23:39,560
Speaker 1: in twenty seventeen and Ethereum became more of a known

393
00:23:39,680 --> 00:23:44,000
Speaker 1: quantity and introduced the possibility of other blockchain based cryptocurrencies

394
00:23:44,000 --> 00:23:47,639
Speaker 1: based off the Ethereum blockchain. At this point, Ethereum was

395
00:23:47,680 --> 00:23:51,760
Speaker 1: a proof of work cryptocurrency, just like Bitcoin is, but

396
00:23:51,960 --> 00:23:54,320
Speaker 1: would later turn to be a proof of stake, but

397
00:23:54,359 --> 00:23:57,280
Speaker 1: that would take a few years. Arguably this was really

398
00:23:57,320 --> 00:23:59,960
Speaker 1: when proof of work currencies began, making it very hard

399
00:24:00,280 --> 00:24:03,840
Speaker 1: for non crypto customers to get hold of graphics processing

400
00:24:03,920 --> 00:24:08,160
Speaker 1: units or GPUs, particularly in the video game space. GPUs

401
00:24:08,200 --> 00:24:11,639
Speaker 1: are important. Crypto miners were relying on these cards because

402
00:24:11,680 --> 00:24:15,280
Speaker 1: they have great parallel processing capabilities and that helps when

403
00:24:15,280 --> 00:24:19,880
Speaker 1: you're mining cryptocurrency and a proof of work system. So yeah,

404
00:24:19,960 --> 00:24:22,280
Speaker 1: this was the beginning of a tough time. If you

405
00:24:22,320 --> 00:24:24,320
Speaker 1: were a gamer and you wanted to get hold of

406
00:24:24,359 --> 00:24:27,399
Speaker 1: the latest in GPUs, it was just as hard to

407
00:24:27,400 --> 00:24:29,439
Speaker 1: get hold of them for a few years. But the

408
00:24:29,440 --> 00:24:31,760
Speaker 1: big story I really wanted to talk about is the

409
00:24:32,119 --> 00:24:35,720
Speaker 1: no good, very bad year that Uber had in twenty seventeen. Now,

410
00:24:35,760 --> 00:24:38,240
Speaker 1: to be clear, a lot of the stuff that happened

411
00:24:38,440 --> 00:24:41,040
Speaker 1: to Uber in twenty seventeen had actually been happening for

412
00:24:41,080 --> 00:24:43,240
Speaker 1: a while. It's just that it all came to a

413
00:24:43,359 --> 00:24:46,919
Speaker 1: head in twenty seventeen. So starting off, we've got some

414
00:24:46,960 --> 00:24:51,239
Speaker 1: political events that landed Uber in a critical place. They

415
00:24:51,440 --> 00:24:54,320
Speaker 1: got a lot of criticism for this. So in January

416
00:24:54,320 --> 00:24:58,080
Speaker 1: twenty seventeen, Donald Trump passed some executive actions that placed

417
00:24:58,119 --> 00:25:02,120
Speaker 1: hefty restrictions on foreign nation being allowed into the United States,

418
00:25:02,359 --> 00:25:05,919
Speaker 1: and largely these restrictions affected people who were traveling from

419
00:25:06,000 --> 00:25:10,240
Speaker 1: countries that have a large Muslim population. So people got

420
00:25:10,320 --> 00:25:14,600
Speaker 1: very upset They were enraged that Trump was apparently engaging

421
00:25:14,840 --> 00:25:18,159
Speaker 1: in profiling because it's not like these other countries that

422
00:25:18,240 --> 00:25:21,200
Speaker 1: don't have large Muslim populations were affected. So yeah, it

423
00:25:21,560 --> 00:25:25,040
Speaker 1: was it was hard to justify. But in response to

424
00:25:25,359 --> 00:25:29,120
Speaker 1: his move, taxi cab drivers in New York City refused

425
00:25:29,160 --> 00:25:32,920
Speaker 1: to work in protest of these travel restrictions, and they

426
00:25:32,920 --> 00:25:37,520
Speaker 1: did this the day following when Trump signed those executive actions,

427
00:25:37,760 --> 00:25:43,080
Speaker 1: and Uber became news because it lifted surge pricing in

428
00:25:43,200 --> 00:25:45,760
Speaker 1: New York in an apparent attempt to take advantage of

429
00:25:45,800 --> 00:25:50,280
Speaker 1: a lack of competition due to that taxi driver work stoppage,

430
00:25:50,520 --> 00:25:52,320
Speaker 1: and it was seen as a move to undermine a

431
00:25:52,359 --> 00:25:56,479
Speaker 1: political protest, and in response, a hashtag called delete Uber

432
00:25:56,600 --> 00:25:59,400
Speaker 1: began to circulate. As it would turn out, there would

433
00:25:59,440 --> 00:26:01,560
Speaker 1: be lots of other reasons folks would want to delete

434
00:26:01,680 --> 00:26:04,320
Speaker 1: Uber as the year went on that didn't involve travel

435
00:26:04,359 --> 00:26:07,600
Speaker 1: restrictions at all. So next up. In February twenty seventeen,

436
00:26:07,880 --> 00:26:12,360
Speaker 1: a former Uber engineer named Susan Fowler came forward posting

437
00:26:12,359 --> 00:26:16,000
Speaker 1: a blog post that made public accusations regarding gender discrimination

438
00:26:16,359 --> 00:26:20,879
Speaker 1: and sexual harassment within Uber, and she detailed a devastatingly

439
00:26:21,119 --> 00:26:25,480
Speaker 1: toxic work culture that was particularly harmful toward women within

440
00:26:25,520 --> 00:26:29,040
Speaker 1: the company. Fowler's move would inspire other women within the

441
00:26:29,080 --> 00:26:32,000
Speaker 1: tech industry to come forward with their own stories, and

442
00:26:32,080 --> 00:26:35,600
Speaker 1: when paired with the blossoming Me Too movement in late

443
00:26:35,720 --> 00:26:38,800
Speaker 1: twenty seventeen, this would build into a more unified call

444
00:26:38,880 --> 00:26:41,440
Speaker 1: for change and accountability. And I wish I could say

445
00:26:41,440 --> 00:26:44,800
Speaker 1: that we got there, that things were all better now,

446
00:26:44,840 --> 00:26:47,920
Speaker 1: But over the following years we would get more stories

447
00:26:48,160 --> 00:26:51,840
Speaker 1: about companies and toxic work cultures and sexual harassment that

448
00:26:51,880 --> 00:26:55,560
Speaker 1: had been codified into company culture. So we got a

449
00:26:55,560 --> 00:26:58,280
Speaker 1: long way to go. But also in February twenty seventeen,

450
00:26:58,400 --> 00:27:02,600
Speaker 1: a video went viral showing then Uber CEO Travis Kalanik,

451
00:27:02,760 --> 00:27:05,240
Speaker 1: who co founded the company, getting into an argument with

452
00:27:05,320 --> 00:27:08,199
Speaker 1: an Uber driver, and the driver protested to Kalanick that

453
00:27:08,440 --> 00:27:11,520
Speaker 1: the company was putting drivers in a squeeze because it

454
00:27:11,560 --> 00:27:13,880
Speaker 1: was raising standards for stuff like, you know, the types

455
00:27:13,920 --> 00:27:17,639
Speaker 1: of cars that could be used on various Uber services,

456
00:27:17,760 --> 00:27:21,960
Speaker 1: while also dropping prices for customers, and drivers were having

457
00:27:22,040 --> 00:27:25,080
Speaker 1: to pay more and they were getting paid less. Kalan

458
00:27:25,160 --> 00:27:28,560
Speaker 1: Nick got really argumentative, but the whole exchange was captured

459
00:27:28,600 --> 00:27:31,520
Speaker 1: on a dash cam. The driver later uploaded it to YouTube.

460
00:27:31,800 --> 00:27:35,240
Speaker 1: I found quite a bit of popularity. Kalanik later issued

461
00:27:35,280 --> 00:27:38,840
Speaker 1: an apology in an internal email in Uber. He sensed

462
00:27:38,880 --> 00:27:42,880
Speaker 1: that perhaps he didn't come off looking really sympathetic because

463
00:27:42,880 --> 00:27:45,639
Speaker 1: he was sitting in the back of an Uber black

464
00:27:45,800 --> 00:27:50,560
Speaker 1: vehicle between two women sitting next to him and arguing

465
00:27:50,600 --> 00:27:53,280
Speaker 1: with his driver about the fact that the driver needed

466
00:27:53,280 --> 00:27:56,400
Speaker 1: to take responsibility for his own financial situation. That Uber

467
00:27:56,560 --> 00:27:59,920
Speaker 1: was blameless in this particular batter, You know, considering how

468
00:28:00,040 --> 00:28:03,359
Speaker 1: Uber was also trying to dodge the responsibility for accusations

469
00:28:03,400 --> 00:28:06,040
Speaker 1: brought against the company from Vowler. The fact that he

470
00:28:06,160 --> 00:28:08,600
Speaker 1: was arguing that, hey, you need to take responsibility for

471
00:28:08,640 --> 00:28:11,800
Speaker 1: your life while simultaneously the company was trying to dodge

472
00:28:11,800 --> 00:28:14,520
Speaker 1: responsibility struck a lot of people as being perhaps on

473
00:28:14,600 --> 00:28:17,360
Speaker 1: the touch ironic side, and we're not done with their year.

474
00:28:17,560 --> 00:28:20,119
Speaker 1: That's just the beginning of the year. In March, the

475
00:28:20,160 --> 00:28:23,119
Speaker 1: New York Times published an article revealing that Uber was

476
00:28:23,119 --> 00:28:26,000
Speaker 1: making use of a tool called gray Ball. Now. The

477
00:28:26,000 --> 00:28:29,240
Speaker 1: purpose of this tool was to fool local authorities in

478
00:28:29,320 --> 00:28:33,440
Speaker 1: regions where Uber's operations were unsanctioned. In other words, gray

479
00:28:33,480 --> 00:28:36,040
Speaker 1: Ball was meant to trick police so that Uber drivers

480
00:28:36,040 --> 00:28:39,480
Speaker 1: could operate in places where they weren't technically allowed. The

481
00:28:39,520 --> 00:28:43,400
Speaker 1: gray Ball tool would identify law enforcement that was attempting

482
00:28:43,440 --> 00:28:46,360
Speaker 1: to hail a ride. So let's say that police are

483
00:28:46,400 --> 00:28:49,120
Speaker 1: trying to prove that Uber is operating in a place

484
00:28:49,120 --> 00:28:51,880
Speaker 1: where they're not supposed to, so they're using their phone

485
00:28:51,880 --> 00:28:54,120
Speaker 1: to call a ride. Well, grey Ball was meant to

486
00:28:54,200 --> 00:28:57,480
Speaker 1: identify when it was a police officer, and then the

487
00:28:57,520 --> 00:29:01,240
Speaker 1: police officer would see cars on the app, but no

488
00:29:01,360 --> 00:29:04,320
Speaker 1: car would ever come to actually pick them up, so

489
00:29:04,960 --> 00:29:06,760
Speaker 1: you know, there was no way for them to use

490
00:29:06,800 --> 00:29:10,520
Speaker 1: that as evidence that Uber was picking up or dropping

491
00:29:10,560 --> 00:29:15,080
Speaker 1: off in that particular region. So Uber spokespeople claimed that

492
00:29:15,160 --> 00:29:17,280
Speaker 1: the tool was actually meant to protect drivers. It was

493
00:29:17,320 --> 00:29:20,080
Speaker 1: meant to prevent them from picking up a fair from

494
00:29:20,120 --> 00:29:24,600
Speaker 1: somebody who was abusing Uber's systems in other words, and

495
00:29:25,080 --> 00:29:27,800
Speaker 1: said that you know, the drivers are independent contractors and

496
00:29:27,840 --> 00:29:30,840
Speaker 1: that they should also not be persecuted or They also

497
00:29:31,120 --> 00:29:34,560
Speaker 1: suggested that law enforcement was somehow in collusion with taxi

498
00:29:34,600 --> 00:29:38,200
Speaker 1: companies to muscle ride hailing services out of various regions.

499
00:29:38,640 --> 00:29:40,360
Speaker 1: I think it is good to point out that even

500
00:29:40,360 --> 00:29:43,600
Speaker 1: if that were true, it doesn't mean that Uber gets

501
00:29:44,200 --> 00:29:46,800
Speaker 1: a free pass to break the law, Like the law

502
00:29:46,880 --> 00:29:49,320
Speaker 1: is the law, and a law can be unjust, but

503
00:29:49,400 --> 00:29:53,280
Speaker 1: breaking an unjust law still comes with consequences. And I

504
00:29:53,320 --> 00:29:56,600
Speaker 1: know it's an easy pat answer to say that the

505
00:29:56,640 --> 00:29:59,320
Speaker 1: solution is to change the law, because you know, I

506
00:29:59,360 --> 00:30:01,840
Speaker 1: know our system here in the United States. I know,

507
00:30:02,240 --> 00:30:06,160
Speaker 1: at best it's byzantine and complicated, and at worst it's

508
00:30:06,240 --> 00:30:09,440
Speaker 1: broken and biased. But you know, it's still a law.

509
00:30:09,960 --> 00:30:12,160
Speaker 1: That's the problem. Like you can't just choose which laws

510
00:30:12,200 --> 00:30:14,520
Speaker 1: to obey and which ones not to obey. Now, that

511
00:30:14,600 --> 00:30:17,600
Speaker 1: still just gets us into to March of twenty seventeen,

512
00:30:17,600 --> 00:30:19,600
Speaker 1: and Uber's very bad, no good year still has a

513
00:30:19,640 --> 00:30:22,040
Speaker 1: long way to go. In May, the company had to

514
00:30:22,040 --> 00:30:25,200
Speaker 1: come clean about how it was paying drivers. So, in

515
00:30:25,240 --> 00:30:27,840
Speaker 1: an example that g Nome Shiber used in the New

516
00:30:27,920 --> 00:30:32,200
Speaker 1: York Times, if a fare cost twenty dollars and taxes

517
00:30:32,280 --> 00:30:35,760
Speaker 1: on that fare were two dollars, Uber was only supposed

518
00:30:35,800 --> 00:30:39,720
Speaker 1: to take its commission, which is twenty five percent off

519
00:30:39,800 --> 00:30:44,080
Speaker 1: the eighteen dollars that were left over so after taxes. However,

520
00:30:44,160 --> 00:30:47,680
Speaker 1: it was discovered that Uber appeared to instead be taking

521
00:30:47,760 --> 00:30:52,120
Speaker 1: its twenty five percent share before taxes, so in the

522
00:30:52,520 --> 00:30:55,520
Speaker 1: example that Schiber gave, it would take twenty five percent

523
00:30:55,520 --> 00:30:58,240
Speaker 1: of twenty bucks, which is five dollars, instead of twenty

524
00:30:58,520 --> 00:31:01,520
Speaker 1: five percent of eighteen bucks, which is four dollars fifty cents,

525
00:31:01,800 --> 00:31:04,160
Speaker 1: and it meant that drivers were taking home less money

526
00:31:04,200 --> 00:31:07,560
Speaker 1: than what they were actually owed. An advocacy group argued

527
00:31:07,560 --> 00:31:10,680
Speaker 1: that what Uber was actually doing was passing the cost

528
00:31:10,800 --> 00:31:14,280
Speaker 1: of state taxes onto drivers, particularly in the state of

529
00:31:14,320 --> 00:31:18,280
Speaker 1: New York, and Uber was not assuming the responsibility of

530
00:31:18,360 --> 00:31:21,960
Speaker 1: paying taxes as a company, and this was a tough

531
00:31:22,080 --> 00:31:24,880
Speaker 1: argument against Uber, and Uber executives said that the company

532
00:31:24,920 --> 00:31:27,360
Speaker 1: was dedicated to paying all drivers the money that they

533
00:31:27,360 --> 00:31:30,280
Speaker 1: were owed due to this practice, that they were sorry

534
00:31:30,320 --> 00:31:32,760
Speaker 1: for it, and you know, et cetera, et cetera. So

535
00:31:32,880 --> 00:31:35,240
Speaker 1: this ended up costing the company as well, gave it

536
00:31:35,280 --> 00:31:38,520
Speaker 1: another black eye, which was unfortunate because at this point

537
00:31:38,680 --> 00:31:41,680
Speaker 1: Uber had more black eyes than eyes. At the halfway

538
00:31:41,720 --> 00:31:44,480
Speaker 1: mark of the year in June, and internal investigation in

539
00:31:44,600 --> 00:31:48,280
Speaker 1: Uber led to twenty people in the company, some of

540
00:31:48,280 --> 00:31:52,880
Speaker 1: them in leadership positions, being fired for inappropriate behavior. And

541
00:31:52,960 --> 00:31:57,720
Speaker 1: by inappropriate behavior, I'm mostly referring to sexual harassment. In addition,

542
00:31:57,760 --> 00:32:00,960
Speaker 1: the board of directors pressured Travis Kalanick to resign, which

543
00:32:00,960 --> 00:32:03,240
Speaker 1: he did, though he still had many close friends on

544
00:32:03,280 --> 00:32:05,400
Speaker 1: the board of directors, so that was a bit of

545
00:32:05,400 --> 00:32:09,320
Speaker 1: a struggle too, and we're not done so. In Singapore,

546
00:32:09,920 --> 00:32:13,880
Speaker 1: it was discovered that Uber had been renting dangerous vehicles

547
00:32:13,880 --> 00:32:17,840
Speaker 1: to drivers. These were vehicles that had known safety issues, namely,

548
00:32:18,160 --> 00:32:20,720
Speaker 1: once in a while they would catch on fire, and

549
00:32:21,120 --> 00:32:24,680
Speaker 1: Uber apparently knew this and still use those vehicles to

550
00:32:24,760 --> 00:32:27,360
Speaker 1: rent to drivers in Singapore to drive for the company,

551
00:32:27,400 --> 00:32:31,400
Speaker 1: which seems in my mind a tiny bit negligent. Uber

552
00:32:31,480 --> 00:32:34,120
Speaker 1: also lost its license to operate within the City of

553
00:32:34,160 --> 00:32:37,959
Speaker 1: London for failure to adhere to certain corporate responsibility requirements,

554
00:32:38,120 --> 00:32:40,000
Speaker 1: and near the end of the year, the company had

555
00:32:40,000 --> 00:32:42,200
Speaker 1: to come clean about a data breach that had happened

556
00:32:42,240 --> 00:32:45,720
Speaker 1: in twenty sixteen and allowed hackers to access information about

557
00:32:45,800 --> 00:32:48,680
Speaker 1: drivers and customers alike. Oh and also that Uber had

558
00:32:48,720 --> 00:32:51,360
Speaker 1: paid a ransom to keep the whole thing quiet. And

559
00:32:51,400 --> 00:32:54,680
Speaker 1: as I always point out, paying ransoms is bad because

560
00:32:54,720 --> 00:32:57,960
Speaker 1: it reinforces to the attackers that ransoms work, and it

561
00:32:58,040 --> 00:33:00,320
Speaker 1: just means we get more of them. So it's better

562
00:33:00,400 --> 00:33:02,840
Speaker 1: not to do that anyway. Yeah, not a good year

563
00:33:02,880 --> 00:33:04,880
Speaker 1: for Uber, especially since you know, they paid to keep

564
00:33:04,920 --> 00:33:07,040
Speaker 1: that thing quiet and it came out in the open anyway.

565
00:33:07,080 --> 00:33:10,360
Speaker 1: So twenty seventeen rough year. All Right, we got a

566
00:33:10,360 --> 00:33:13,400
Speaker 1: whole lot to get through. So let's take another quick

567
00:33:13,400 --> 00:33:15,080
Speaker 1: break and when we come back, we'll pick up with

568
00:33:15,160 --> 00:33:28,040
Speaker 1: twenty eighteen. We're back. So in twenty eighteen here in

569
00:33:28,080 --> 00:33:30,400
Speaker 1: the United States, we were coming to grips with how

570
00:33:30,440 --> 00:33:34,640
Speaker 1: to navigate city streets while all these folks on rental

571
00:33:34,680 --> 00:33:38,520
Speaker 1: electric scooters were whizzing around on sidewalks and roads, and

572
00:33:38,720 --> 00:33:40,920
Speaker 1: it was a mess. It was a mess because you know,

573
00:33:40,960 --> 00:33:43,560
Speaker 1: you had all these people just getting in the way

574
00:33:43,600 --> 00:33:46,080
Speaker 1: of pedestrians, traffic and stuff. It was a mess because

575
00:33:46,120 --> 00:33:50,200
Speaker 1: you would have folks abandoning their electric scooter wherever they wanted,

576
00:33:50,240 --> 00:33:52,800
Speaker 1: including like in the middle of the road sometimes or

577
00:33:52,840 --> 00:33:55,480
Speaker 1: some people would get upset about the electric scooters and

578
00:33:55,520 --> 00:33:57,280
Speaker 1: would round up as many as they could and like

579
00:33:57,400 --> 00:33:59,840
Speaker 1: throw them in a pond or whatever. It was just

580
00:33:59,840 --> 00:34:02,360
Speaker 1: a a huge mess and an example of a tech

581
00:34:02,440 --> 00:34:06,960
Speaker 1: oriented business model that hit the streets before local laws

582
00:34:06,960 --> 00:34:10,160
Speaker 1: and regulations could really weigh in on how this should

583
00:34:10,360 --> 00:34:15,680
Speaker 1: interact with the existing infrastructure. So example here in Atlanta. Technically,

584
00:34:15,880 --> 00:34:18,200
Speaker 1: the law is that if you're driving one of these things,

585
00:34:18,239 --> 00:34:20,200
Speaker 1: you're supposed to be on the road. You're not supposed

586
00:34:20,200 --> 00:34:22,480
Speaker 1: to be on a sidewalk. And technically I believe you're

587
00:34:22,480 --> 00:34:25,279
Speaker 1: supposed to be wearing a helmet, though nobody does, and

588
00:34:25,320 --> 00:34:28,040
Speaker 1: as far as I've seen, no one really enforces that either,

589
00:34:28,239 --> 00:34:30,760
Speaker 1: But that's what the rule is supposed to be anyway.

590
00:34:30,800 --> 00:34:33,520
Speaker 1: Twenty eighteen was also the year when five G wireless

591
00:34:33,520 --> 00:34:37,520
Speaker 1: technologies first began to take headlines by storm. And it

592
00:34:37,600 --> 00:34:40,359
Speaker 1: turns out five G is a little more complicated than

593
00:34:40,400 --> 00:34:43,160
Speaker 1: just being one G better than four G. There are

594
00:34:43,200 --> 00:34:46,200
Speaker 1: different flavors, if you will, of five G. So on

595
00:34:46,200 --> 00:34:49,080
Speaker 1: one end, you've got like the ultra high frequency five

596
00:34:49,160 --> 00:34:51,600
Speaker 1: G bands. These are the ones that are great for

597
00:34:51,760 --> 00:34:54,760
Speaker 1: super high throughput, so you're able to make huge data

598
00:34:54,800 --> 00:34:58,879
Speaker 1: transfers in very short amounts of time. But it has

599
00:34:58,920 --> 00:35:03,200
Speaker 1: a limited transmission range, and also the frequencies aren't really

600
00:35:03,200 --> 00:35:06,279
Speaker 1: good at penetrating solid matter, so if you happen to be,

601
00:35:06,400 --> 00:35:09,719
Speaker 1: say on the other side of a wall as a transmitter,

602
00:35:09,920 --> 00:35:13,160
Speaker 1: you might not actually be able to make use of

603
00:35:13,200 --> 00:35:17,920
Speaker 1: that five G transmitter. Lower frequency five G bands allow

604
00:35:17,960 --> 00:35:23,000
Speaker 1: for longer distance communications, and they penetrate through stuff like walls,

605
00:35:23,080 --> 00:35:25,799
Speaker 1: but they also have lower data throughput values, so you

606
00:35:25,840 --> 00:35:30,239
Speaker 1: don't have the quote unquote speed of the other version

607
00:35:30,280 --> 00:35:33,120
Speaker 1: of five G. But communicating all of this to customers

608
00:35:33,200 --> 00:35:36,520
Speaker 1: is hard, Like how do you say, Hey, under certain circumstances,

609
00:35:36,560 --> 00:35:38,839
Speaker 1: you're going to get this kind of experience, but under

610
00:35:38,880 --> 00:35:42,240
Speaker 1: every other circumstance it's going to be like this, that's muddy.

611
00:35:42,560 --> 00:35:45,000
Speaker 1: So a lot of the marketing that I saw really

612
00:35:45,040 --> 00:35:50,320
Speaker 1: focused on the ideal hypothetical situation you could find yourself

613
00:35:50,360 --> 00:35:53,960
Speaker 1: in where you have this incredible throughput with these ultrai

614
00:35:54,040 --> 00:35:58,880
Speaker 1: frequency five G transmitters and ignored everything else, and that

615
00:35:59,000 --> 00:36:02,640
Speaker 1: just set people up for disappointment unless they just happened

616
00:36:02,640 --> 00:36:04,920
Speaker 1: to be in an area where they could take advantage

617
00:36:05,120 --> 00:36:08,520
Speaker 1: of those ultra high frequency transmitter stations, in which case

618
00:36:08,560 --> 00:36:10,919
Speaker 1: they had a great experience as long as a wall

619
00:36:10,920 --> 00:36:13,879
Speaker 1: didn't come between them and the tower. Rumors first began

620
00:36:14,000 --> 00:36:16,840
Speaker 1: to circulate in this year that Apple had been working

621
00:36:16,880 --> 00:36:19,600
Speaker 1: on a mixed reality headset, with the original goal of

622
00:36:19,640 --> 00:36:22,320
Speaker 1: having it come to market by twenty twenty. Of course,

623
00:36:22,880 --> 00:36:26,120
Speaker 1: we would realize later that the Apple Vision pro would

624
00:36:26,160 --> 00:36:29,760
Speaker 1: not debut until early twenty twenty four, and the somewhat

625
00:36:29,800 --> 00:36:33,360
Speaker 1: tep in response to that headset has put the future

626
00:36:33,400 --> 00:36:35,640
Speaker 1: of the product line into question. I have read a

627
00:36:35,640 --> 00:36:38,920
Speaker 1: lot of analysts that have said that Apple is likely

628
00:36:39,400 --> 00:36:43,120
Speaker 1: going to release a lower cost, stripped down version of

629
00:36:43,200 --> 00:36:47,560
Speaker 1: the Vision pro that they haven't fully abandoned the tech

630
00:36:47,640 --> 00:36:50,680
Speaker 1: product line. But you know, we'll just have to see

631
00:36:50,719 --> 00:36:52,680
Speaker 1: what happens, and by the time you hear this, maybe

632
00:36:52,719 --> 00:36:56,040
Speaker 1: we'll already have seen it. That's also twenty eighteen was

633
00:36:56,040 --> 00:36:58,520
Speaker 1: the year when Elon Musk would get into trouble with SEC,

634
00:36:58,680 --> 00:37:01,560
Speaker 1: not for the last time. He tweeted out that he

635
00:37:01,719 --> 00:37:04,719
Speaker 1: intended to take Tesla private for four hundred and twenty

636
00:37:04,760 --> 00:37:09,160
Speaker 1: dollars a share and that some presumably some unnamed investors

637
00:37:09,200 --> 00:37:11,640
Speaker 1: would front him the funds to do it. But turns

638
00:37:11,680 --> 00:37:14,640
Speaker 1: out that was what we call a lie, and the

639
00:37:14,680 --> 00:37:18,000
Speaker 1: SEC wasn't so big on the whole. Hey, it was

640
00:37:18,120 --> 00:37:22,280
Speaker 1: just a weed joke. Defense Musk, as CEO of Tesla,

641
00:37:22,680 --> 00:37:27,839
Speaker 1: has a responsibility to Tesla's shareholders, and the SEC said, hey,

642
00:37:27,880 --> 00:37:31,200
Speaker 1: you can't go around defrauding everyone, even if it's a joke,

643
00:37:31,400 --> 00:37:33,839
Speaker 1: because you're the CEO. You can't say that you're going

644
00:37:33,920 --> 00:37:36,480
Speaker 1: to take the company private if you don't actually have

645
00:37:36,560 --> 00:37:39,320
Speaker 1: plans to do that. Musk would go on to build

646
00:37:39,360 --> 00:37:43,400
Speaker 1: a very contentious relationship with the SEC, and I'm very

647
00:37:43,480 --> 00:37:45,239
Speaker 1: curious to see how that plays out over the next

648
00:37:45,280 --> 00:37:49,200
Speaker 1: few years. Okay, here's the story I actually wanted to

649
00:37:49,320 --> 00:37:53,600
Speaker 1: focus on for twenty eighteen. That was the year when Facebook,

650
00:37:54,200 --> 00:37:58,360
Speaker 1: specifically Mark Zuckerberg, was called before Congress again, this time

651
00:37:58,840 --> 00:38:03,000
Speaker 1: to talk about the Cambridge Analytica scandal, which had been

652
00:38:03,080 --> 00:38:05,360
Speaker 1: broiling around the tech world for a couple of years

653
00:38:05,719 --> 00:38:09,240
Speaker 1: but really only became public knowledge and a big story

654
00:38:09,360 --> 00:38:11,719
Speaker 1: in twenty eighteen. So in case you don't remember what

655
00:38:11,800 --> 00:38:17,040
Speaker 1: this was all about, Cambridge Analytica was a political consulting

656
00:38:17,200 --> 00:38:21,400
Speaker 1: firm that worked on various political campaigns, including Trump's first

657
00:38:21,719 --> 00:38:24,440
Speaker 1: candidacy for president back in twenty sixteen here in the

658
00:38:24,560 --> 00:38:28,200
Speaker 1: United States. And it turned out that Cambridge Analytica worked

659
00:38:28,239 --> 00:38:31,879
Speaker 1: with a researcher, a data scientist, who had designed an

660
00:38:31,920 --> 00:38:36,440
Speaker 1: app essentially a personality test slash survey, and this app

661
00:38:36,520 --> 00:38:40,400
Speaker 1: would scrape data off of Facebook without the knowledge or

662
00:38:40,440 --> 00:38:43,279
Speaker 1: consent of most of the people who were affected by it.

663
00:38:43,640 --> 00:38:46,640
Speaker 1: So here's how it kind of worked. So essentially, some

664
00:38:46,880 --> 00:38:51,760
Speaker 1: users were invited to take this little personality test slash survey,

665
00:38:52,040 --> 00:38:55,040
Speaker 1: and in return they would receive a small amount of money,

666
00:38:55,160 --> 00:38:57,800
Speaker 1: so they were being paid to do it. The survey

667
00:38:58,160 --> 00:39:02,120
Speaker 1: involved installing this app extension in Facebook that let the

668
00:39:02,200 --> 00:39:06,120
Speaker 1: developer look at all the profiles connected to the users

669
00:39:06,160 --> 00:39:10,200
Speaker 1: who were taking the test as if the developer were

670
00:39:10,719 --> 00:39:14,080
Speaker 1: was that that user. So let me use an example

671
00:39:14,160 --> 00:39:17,640
Speaker 1: to explain this. Let's say that I'm on Facebook and

672
00:39:17,680 --> 00:39:21,120
Speaker 1: I'm friends with you, and I've decided to take this survey,

673
00:39:21,400 --> 00:39:24,560
Speaker 1: and let's say that you had set your Facebook profiles

674
00:39:24,640 --> 00:39:27,200
Speaker 1: so that only your friends could see it, like the

675
00:39:27,200 --> 00:39:30,279
Speaker 1: public cannot see what you post, only your friends can

676
00:39:30,320 --> 00:39:33,960
Speaker 1: see that. Well, because of the loophole that existed in

677
00:39:34,040 --> 00:39:38,120
Speaker 1: Facebook's app environment at the time when this data scientist

678
00:39:38,200 --> 00:39:41,040
Speaker 1: was doing this research, the data scientists could view your

679
00:39:41,080 --> 00:39:44,200
Speaker 1: profile as if the data scientist were me, like it

680
00:39:44,239 --> 00:39:46,279
Speaker 1: would be like if it were in my place, and

681
00:39:46,320 --> 00:39:48,799
Speaker 1: they would just see everything you had posted because I'm

682
00:39:48,840 --> 00:39:52,000
Speaker 1: your friend, and now the data scientist is posing against

683
00:39:52,040 --> 00:39:54,760
Speaker 1: me and can see everything that you've posted. And thus

684
00:39:54,960 --> 00:39:57,520
Speaker 1: they were able to get hold of tons of information

685
00:39:57,840 --> 00:40:01,319
Speaker 1: that involved people who had never consented to having their

686
00:40:01,360 --> 00:40:05,520
Speaker 1: information collected or used. Now, Cambridge Analytica was aiming to

687
00:40:05,680 --> 00:40:11,400
Speaker 1: take that information and create various targeted campaigns in various regions,

688
00:40:11,440 --> 00:40:15,520
Speaker 1: including doing stuff like discouraging people from voting. You know,

689
00:40:15,719 --> 00:40:18,000
Speaker 1: if it turned out that there was a region that

690
00:40:18,120 --> 00:40:21,000
Speaker 1: was not likely to support one of the candidates that

691
00:40:21,160 --> 00:40:24,560
Speaker 1: had hired Cambridge Analytica, they would try to discourage people

692
00:40:24,560 --> 00:40:28,480
Speaker 1: from voting at all to help boost their clients' numbers. So,

693
00:40:28,880 --> 00:40:31,040
Speaker 1: in the Trump example, if it turns out like there's

694
00:40:31,040 --> 00:40:34,240
Speaker 1: a county in a state that's not likely to support Trump,

695
00:40:34,480 --> 00:40:37,320
Speaker 1: but maybe Trump could carry the state. If that county

696
00:40:37,440 --> 00:40:40,480
Speaker 1: just didn't participate in the election, they would target that

697
00:40:40,520 --> 00:40:43,840
Speaker 1: county that kind of thing. Now, their effectiveness in actually

698
00:40:44,560 --> 00:40:47,319
Speaker 1: accomplishing this goal was a matter of debate. I saw

699
00:40:47,320 --> 00:40:49,360
Speaker 1: a lot of people kind of dismissed, saying like, yeah,

700
00:40:49,400 --> 00:40:53,160
Speaker 1: you know, what they did was unethical and potentially illegal,

701
00:40:53,480 --> 00:40:56,640
Speaker 1: but they also were not really good at it. But still,

702
00:40:56,800 --> 00:40:59,520
Speaker 1: the data collection thing was huge, Like it violated the

703
00:40:59,560 --> 00:41:04,160
Speaker 1: privacy of millions of people. So Facebook also faced a

704
00:41:04,160 --> 00:41:07,960
Speaker 1: ton of criticism because it facilitated this. It allowed it

705
00:41:08,000 --> 00:41:11,480
Speaker 1: to happen through having this lax policy, which, to be

706
00:41:11,560 --> 00:41:14,920
Speaker 1: fair to Facebook, the company had already addressed. They had

707
00:41:14,960 --> 00:41:19,080
Speaker 1: already closed that loophole before anyone had come to the

708
00:41:19,080 --> 00:41:21,720
Speaker 1: public with Cambridge Analytica. Like, I don't even think anyone

709
00:41:21,719 --> 00:41:25,240
Speaker 1: at Facebook necessarily knew about Cambridge Analytica at that point.

710
00:41:25,560 --> 00:41:28,920
Speaker 1: They just had been made aware of this loophole and

711
00:41:28,960 --> 00:41:33,080
Speaker 1: they had fixed it. Also unrelated to that, but the

712
00:41:33,080 --> 00:41:36,840
Speaker 1: company's chief operating officer at the time, Cheryl Sandberg, landed

713
00:41:36,840 --> 00:41:39,359
Speaker 1: in the hot seat because apparently she had hired a

714
00:41:39,440 --> 00:41:43,160
Speaker 1: PR firm to investigate people who are criticizing Facebook, which

715
00:41:43,160 --> 00:41:46,399
Speaker 1: seems like an intimidation tactic to me, right, Like, sure

716
00:41:46,440 --> 00:41:49,400
Speaker 1: would be a shame if you said something bad about

717
00:41:49,400 --> 00:41:52,960
Speaker 1: Facebook and then I don't know, people found out you

718
00:41:53,000 --> 00:41:56,000
Speaker 1: were having an affair on your spouse or something like that.

719
00:41:56,160 --> 00:42:01,120
Speaker 1: As very skullduggery and cloak and daggery not attract. Okay,

720
00:42:01,560 --> 00:42:04,400
Speaker 1: we are now up to twenty nineteen. We still have

721
00:42:04,440 --> 00:42:06,160
Speaker 1: several years to go, but don't worry. Like once I

722
00:42:06,200 --> 00:42:10,200
Speaker 1: hit twenty twenty, things go real fast. However, because of

723
00:42:10,200 --> 00:42:12,040
Speaker 1: the length of this episode, we do need to take

724
00:42:12,080 --> 00:42:14,480
Speaker 1: another quick break to thank our sponsors. We'll be back

725
00:42:14,560 --> 00:42:16,839
Speaker 1: to wrap all this up. Stick with me because I'm

726
00:42:16,840 --> 00:42:27,879
Speaker 1: not going to be doing this much longer. All right,

727
00:42:27,920 --> 00:42:31,440
Speaker 1: we're back, and we're up to twenty nineteen. So Tesla

728
00:42:31,640 --> 00:42:35,960
Speaker 1: introduced the cyber truck that year. It was a sometimes

729
00:42:36,320 --> 00:42:41,800
Speaker 1: a fairly embarrassing unveiling. It included a moment when windows

730
00:42:41,800 --> 00:42:45,840
Speaker 1: that were supposed to be reinforced and difficult to shatter shattered.

731
00:42:46,320 --> 00:42:49,240
Speaker 1: That was a whoopsie. But it would actually take several

732
00:42:49,320 --> 00:42:51,960
Speaker 1: more years before any cyber trucks would be ready to

733
00:42:52,000 --> 00:42:54,640
Speaker 1: ship to customers, so you could argue this was one

734
00:42:54,680 --> 00:42:58,120
Speaker 1: of those events that happened way too early. I also

735
00:42:58,160 --> 00:43:00,279
Speaker 1: think that's going to be the case for some other

736
00:43:00,320 --> 00:43:04,440
Speaker 1: Tesla products, like their robots. Like a fully fledged robot

737
00:43:04,520 --> 00:43:08,080
Speaker 1: capable of doing what their robots appeared to do at

738
00:43:08,080 --> 00:43:10,560
Speaker 1: a recent Tesla event, I imagine is going to take

739
00:43:10,560 --> 00:43:13,200
Speaker 1: a few more years. And also, I'm sure no one

740
00:43:13,400 --> 00:43:17,000
Speaker 1: anticipated someone blowing up a cyber truck outside of Trump's

741
00:43:17,000 --> 00:43:20,719
Speaker 1: Casino in Las Vegas, Nevada. So as I record this episode,

742
00:43:20,760 --> 00:43:23,200
Speaker 1: we still don't have all the details about the Las

743
00:43:23,320 --> 00:43:26,400
Speaker 1: Vegas incident. We know that there was a fatality, the

744
00:43:26,440 --> 00:43:29,400
Speaker 1: person who was in the car and seven injuries, and

745
00:43:29,440 --> 00:43:31,320
Speaker 1: it appears to have been an attack. I mean, the

746
00:43:31,800 --> 00:43:34,560
Speaker 1: news articles I've read have suggested that the car was

747
00:43:34,640 --> 00:43:38,360
Speaker 1: laiden down with potentially a mixture of fireworks and fuel,

748
00:43:38,680 --> 00:43:42,160
Speaker 1: maybe other explosive devices, and so it appears that it

749
00:43:42,200 --> 00:43:47,560
Speaker 1: was purposefully exploded outside of Trump's casino in Nevada. But

750
00:43:47,600 --> 00:43:49,799
Speaker 1: we don't know anything more than that at the time

751
00:43:49,840 --> 00:43:52,239
Speaker 1: that I'm recording this. So if more details have come

752
00:43:52,239 --> 00:43:54,840
Speaker 1: out by the time you hear this, what a tragic

753
00:43:55,040 --> 00:43:58,080
Speaker 1: and baffling sort of thing. Like I never get these

754
00:43:58,719 --> 00:44:05,000
Speaker 1: acts of public violence. It's absolutely alien to me. Twenty

755
00:44:05,120 --> 00:44:07,799
Speaker 1: nineteen is also the year that Disney Plus launched, which

756
00:44:07,800 --> 00:44:10,240
Speaker 1: I don't think merrit's a whole lot of conversation, except

757
00:44:10,239 --> 00:44:12,239
Speaker 1: to say I think of it as almost like a

758
00:44:12,280 --> 00:44:15,120
Speaker 1: tipping point where the number of streaming services started to

759
00:44:15,160 --> 00:44:18,560
Speaker 1: get out of control. I mean, we already had Hulu

760
00:44:18,960 --> 00:44:23,640
Speaker 1: and Netflix and Amazon Prime Video and HBO Max and

761
00:44:23,760 --> 00:44:26,920
Speaker 1: Disney Plus joining the ranks would make things more crowded,

762
00:44:26,960 --> 00:44:29,160
Speaker 1: and we were just getting started. I mean, Apple TV

763
00:44:29,320 --> 00:44:32,520
Speaker 1: Plus also debuted in twenty nineteen that was more of

764
00:44:32,600 --> 00:44:36,560
Speaker 1: a prestige television angle. That Apple was going for but

765
00:44:36,680 --> 00:44:41,640
Speaker 1: still adding even more options there. And today we live

766
00:44:41,640 --> 00:44:44,360
Speaker 1: in a world where there's so many different streaming services

767
00:44:44,360 --> 00:44:47,359
Speaker 1: that we're starting to see them make bundles because they

768
00:44:47,400 --> 00:44:50,600
Speaker 1: recognize that they're too many and that if they all

769
00:44:50,719 --> 00:44:53,400
Speaker 1: try and stand on their own, they're not going to

770
00:44:53,400 --> 00:44:55,920
Speaker 1: get a big slice of the pie because customers have

771
00:44:55,960 --> 00:44:58,359
Speaker 1: a limit to how much they're willing to subscribe to,

772
00:44:58,560 --> 00:45:01,879
Speaker 1: or at least most do. We're getting bundles and consolidation

773
00:45:02,560 --> 00:45:04,799
Speaker 1: because of that, because there's just so many of these

774
00:45:04,840 --> 00:45:08,120
Speaker 1: gosh darn streaming services out there, and most of them

775
00:45:08,160 --> 00:45:10,920
Speaker 1: aren't even nearly as good as dropout TV. And I

776
00:45:10,960 --> 00:45:13,000
Speaker 1: don't know anyone on dropout TV. I just know that

777
00:45:13,080 --> 00:45:15,880
Speaker 1: dropout TV is really good, all right, Speaking of a

778
00:45:15,880 --> 00:45:19,279
Speaker 1: flood of media. It's funny because in twenty nineteen we

779
00:45:19,320 --> 00:45:23,959
Speaker 1: saw what we thought was a big booming period in podcasting.

780
00:45:24,200 --> 00:45:27,760
Speaker 1: And I say it's funny because the next year would

781
00:45:27,800 --> 00:45:30,640
Speaker 1: put twenty nineteen to shame. But yeah, twenty nineteen was

782
00:45:31,000 --> 00:45:33,160
Speaker 1: an important year, Like we saw a lot of people

783
00:45:33,239 --> 00:45:37,840
Speaker 1: getting into podcasting, including high profile personalities like cod and

784
00:45:37,880 --> 00:45:41,560
Speaker 1: O'Brien starting his podcast that year. Meanwhile, there were folks

785
00:45:41,560 --> 00:45:43,480
Speaker 1: like yours truly, who had been going strong for more

786
00:45:43,480 --> 00:45:45,720
Speaker 1: than a decade to that point. And all I'm saying

787
00:45:45,800 --> 00:45:48,400
Speaker 1: is I was podcasting before it was cool now. I

788
00:45:48,440 --> 00:45:50,799
Speaker 1: think the story of twenty nineteen that I want to

789
00:45:50,800 --> 00:45:54,640
Speaker 1: focus on is that the various US regulatory agencies and

790
00:45:54,680 --> 00:45:57,440
Speaker 1: the Department of Justice here in the United States began

791
00:45:57,520 --> 00:46:02,759
Speaker 1: to consider anti trust investigations into big tech companies, primarily

792
00:46:03,120 --> 00:46:07,479
Speaker 1: members of the Big Five. And those are Facebook, which

793
00:46:07,520 --> 00:46:12,799
Speaker 1: is now Meta Alphabet, which is you know, Google's parent company, Apple, Amazon,

794
00:46:13,200 --> 00:46:16,160
Speaker 1: and Microsoft. And those stories are still playing out today,

795
00:46:16,200 --> 00:46:19,239
Speaker 1: with Google in particular looking ahead to a ruling from

796
00:46:19,280 --> 00:46:21,920
Speaker 1: a judge that could determine the future of Google Chrome,

797
00:46:22,040 --> 00:46:24,920
Speaker 1: among other things. Twenty nineteen was also the year that

798
00:46:25,040 --> 00:46:31,080
Speaker 1: TikTok exploded in popularity, so it already launched, but before

799
00:46:31,160 --> 00:46:34,799
Speaker 1: twenty nineteen, it was largely just this obscure app that

800
00:46:34,920 --> 00:46:38,040
Speaker 1: teens were using to lip sync to various music tracks

801
00:46:38,239 --> 00:46:40,719
Speaker 1: and create, you know, fun little videos that way. But

802
00:46:40,760 --> 00:46:43,719
Speaker 1: in twenty nineteen it became a fully fledged short form

803
00:46:43,840 --> 00:46:48,080
Speaker 1: video social network, and it also quickly became a concern

804
00:46:48,360 --> 00:46:52,000
Speaker 1: among people who were worried about stuff like privacy and security,

805
00:46:52,200 --> 00:46:55,879
Speaker 1: and obviously that escalated in later years and ultimately would

806
00:46:55,960 --> 00:46:59,319
Speaker 1: lead to the United States government banning the app in

807
00:46:59,400 --> 00:47:03,360
Speaker 1: twenty twenty before unless it separates from its Chinese parent company,

808
00:47:03,400 --> 00:47:06,319
Speaker 1: Byte Dance. However, I should add that the Trump administration

809
00:47:06,560 --> 00:47:10,279
Speaker 1: has asked the Supreme Court to delay the deadline for this.

810
00:47:10,440 --> 00:47:12,960
Speaker 1: It was set for January nineteenth, which is the day

811
00:47:13,000 --> 00:47:16,840
Speaker 1: before Trump takes office. He's now asking the Supreme Court

812
00:47:16,880 --> 00:47:21,960
Speaker 1: to delay that deadline, and presumably Trump would then attempt

813
00:47:21,960 --> 00:47:26,640
Speaker 1: to change the thing like it's already been signed into law.

814
00:47:26,760 --> 00:47:29,040
Speaker 1: So I don't actually know what the process is at

815
00:47:29,040 --> 00:47:31,600
Speaker 1: this point. I mean, he's the president of the United States,

816
00:47:31,640 --> 00:47:35,240
Speaker 1: but like, I don't think he just has the power

817
00:47:35,280 --> 00:47:40,080
Speaker 1: to undo something for a previous president had signed into law.

818
00:47:40,400 --> 00:47:43,400
Speaker 1: But I'm no constitutional expert, so what do I know?

819
00:47:43,640 --> 00:47:45,759
Speaker 1: All Right, So, now we're up to twenty twenty, and

820
00:47:46,280 --> 00:47:48,880
Speaker 1: nothing happened that year, At least I'd like to pretend

821
00:47:49,280 --> 00:47:53,040
Speaker 1: nothing happened in twenty twenty. The COVID nineteen pandemic obviously

822
00:47:53,080 --> 00:47:56,920
Speaker 1: made an enormous impact on everything in the world, including

823
00:47:56,960 --> 00:48:00,840
Speaker 1: the tech sector. It pushed some companies to the spotlight,

824
00:48:00,960 --> 00:48:05,000
Speaker 1: so tools like Zoom and Slack became crucial to businesses

825
00:48:05,200 --> 00:48:07,800
Speaker 1: that needed their teams to be able to work together

826
00:48:07,920 --> 00:48:10,719
Speaker 1: even when they weren't allowed to like go to the office.

827
00:48:11,000 --> 00:48:14,400
Speaker 1: And as I mentioned a moment ago, podcasts had another

828
00:48:14,560 --> 00:48:17,759
Speaker 1: explosive moment of growth in twenty twenty. Everyone and their

829
00:48:17,800 --> 00:48:20,560
Speaker 1: Dog launched a show that year. Most of those shows

830
00:48:20,680 --> 00:48:23,760
Speaker 1: ended within three episodes, but some of them stuck around.

831
00:48:24,040 --> 00:48:27,480
Speaker 1: Some tech companies or companies connected to tech, found themselves

832
00:48:27,760 --> 00:48:30,640
Speaker 1: in deep trouble in twenty twenty. You know, like we work,

833
00:48:30,920 --> 00:48:33,480
Speaker 1: we work not technically being a tech company, but treated

834
00:48:33,560 --> 00:48:36,759
Speaker 1: that way. It was already having issues before the pandemic,

835
00:48:37,040 --> 00:48:39,279
Speaker 1: but the pandemic meant that customers had no need for

836
00:48:39,360 --> 00:48:41,800
Speaker 1: office space in the meantime, and we work with sitting

837
00:48:41,800 --> 00:48:45,000
Speaker 1: on all these leases that it needed to you know,

838
00:48:45,320 --> 00:48:48,200
Speaker 1: pay off, So that put we work in a really

839
00:48:48,200 --> 00:48:51,879
Speaker 1: tough position, set it up for the path to bankruptcy

840
00:48:51,920 --> 00:48:55,080
Speaker 1: in twenty twenty three. Now, one huge story connected to

841
00:48:55,120 --> 00:48:58,319
Speaker 1: the pandemic was how the Internet facilitated the spread of

842
00:48:58,480 --> 00:49:02,360
Speaker 1: misinformation that year, twenty twenty election year in the United States.

843
00:49:02,600 --> 00:49:05,400
Speaker 1: We always see an uptick in misinformation on those years,

844
00:49:05,600 --> 00:49:08,400
Speaker 1: and boy howdy, did we see it in twenty twenty, Like,

845
00:49:08,520 --> 00:49:13,000
Speaker 1: remember stories like how five G wireless technologies were supposedly

846
00:49:13,000 --> 00:49:16,440
Speaker 1: carrying the coronavirus in some inexplicable way, Like there were

847
00:49:16,480 --> 00:49:19,479
Speaker 1: actual people who believe that. I don't understand how that's

848
00:49:19,560 --> 00:49:22,520
Speaker 1: the case. I guess if you aren't aware of how

849
00:49:22,640 --> 00:49:26,879
Speaker 1: wireless technologies work and the way or lack of way

850
00:49:26,920 --> 00:49:32,200
Speaker 1: that they interact with organisms, I suppose you could be convinced.

851
00:49:32,239 --> 00:49:35,800
Speaker 1: But like it just takes the smallest amount of education

852
00:49:35,960 --> 00:49:39,879
Speaker 1: to learn that just isn't a viable possibility. I still

853
00:49:39,920 --> 00:49:42,880
Speaker 1: can't wrap my head around that one. It's been four years, y'all.

854
00:49:43,160 --> 00:49:45,640
Speaker 1: And that's just one example of one of the weird

855
00:49:45,719 --> 00:49:49,200
Speaker 1: fringe theories that went around related to tech and the coronavirus.

856
00:49:49,200 --> 00:49:52,160
Speaker 1: There's so many. And because it was an election year,

857
00:49:52,200 --> 00:49:54,560
Speaker 1: and while twenty sixteen gave us a taste of how

858
00:49:54,600 --> 00:49:58,520
Speaker 1: misinformation and propaganda campaigns could work, largely funded from foreign

859
00:49:58,840 --> 00:50:02,279
Speaker 1: sources and primarily from Russia, honestly, twenty sixteen felt like

860
00:50:02,320 --> 00:50:06,240
Speaker 1: an informal rehearsal compared to what happened in twenty twenty plus.

861
00:50:06,520 --> 00:50:09,200
Speaker 1: We were starting to see how emerging technologies like deep

862
00:50:09,239 --> 00:50:12,760
Speaker 1: fakes could potentially play a part in misinformation campaigns down

863
00:50:12,800 --> 00:50:15,399
Speaker 1: the road. It was in twenty twenty when Trump as

864
00:50:15,440 --> 00:50:20,000
Speaker 1: president first attempted to ban TikTok through an executive order. Interestingly,

865
00:50:20,120 --> 00:50:23,160
Speaker 1: now that TikTok is actually facing a legal ban here

866
00:50:23,200 --> 00:50:25,279
Speaker 1: in the United States, not just an executive order but

867
00:50:25,400 --> 00:50:28,959
Speaker 1: a law that's been passed, Trump now opposes this move,

868
00:50:29,360 --> 00:50:32,000
Speaker 1: perhaps because one of his big financial supporters during the

869
00:50:32,040 --> 00:50:35,040
Speaker 1: twenty twenty four campaign also happened to be a big

870
00:50:35,040 --> 00:50:38,200
Speaker 1: investor in byte Dance, which is TikTok's parent company. But

871
00:50:38,280 --> 00:50:42,160
Speaker 1: who's to say, okay. There's also a move to take

872
00:50:42,239 --> 00:50:44,800
Speaker 1: the protections out of section two thirty of the nineteen

873
00:50:44,880 --> 00:50:49,239
Speaker 1: ninety six Communications Decency Act in twenty twenty. That's the

874
00:50:49,920 --> 00:50:53,760
Speaker 1: measure that protects platforms online platforms from being held legally

875
00:50:53,880 --> 00:50:57,640
Speaker 1: liable for content that was posted by users. So if

876
00:50:57,680 --> 00:51:01,520
Speaker 1: a user posts something that's illegal, the platform is not

877
00:51:01,840 --> 00:51:07,319
Speaker 1: held responsible for that, particularly if the platform responds appropriately

878
00:51:07,480 --> 00:51:10,960
Speaker 1: by removing the content and restricting the person from being

879
00:51:11,000 --> 00:51:13,640
Speaker 1: able to do that again. I suspect that we're going

880
00:51:13,719 --> 00:51:16,000
Speaker 1: to see a much harder push to strip Section two

881
00:51:16,080 --> 00:51:19,960
Speaker 1: thirty protections moving forward. In twenty twenty five. I would

882
00:51:20,000 --> 00:51:23,080
Speaker 1: be amazed if section two thirty remains a thing by

883
00:51:23,120 --> 00:51:26,279
Speaker 1: the end of Trump's presidency. We'll see, all right. So

884
00:51:26,320 --> 00:51:29,200
Speaker 1: twenty twenty is also the year that Quibi debuted. Do

885
00:51:29,239 --> 00:51:32,840
Speaker 1: you remember Quibi? Quibi debuted, it sputtered, and it died,

886
00:51:33,000 --> 00:51:34,879
Speaker 1: all in the span of a year. If you don't

887
00:51:34,920 --> 00:51:38,319
Speaker 1: remember Quibi. It was a short form video platform that

888
00:51:38,360 --> 00:51:41,560
Speaker 1: poured a whole lot of money into very highly produced,

889
00:51:41,880 --> 00:51:45,400
Speaker 1: professional video projects. So this wasn't user generated. This was

890
00:51:45,480 --> 00:51:49,280
Speaker 1: meant to be like Netflix or Amazon Prime or whatever,

891
00:51:49,360 --> 00:51:52,480
Speaker 1: except for short form video. It was made with an

892
00:51:52,560 --> 00:51:55,720
Speaker 1: audience watching on mobile devices in mind. In fact, every

893
00:51:55,760 --> 00:51:58,640
Speaker 1: production was made so that you could view it portrait

894
00:51:58,760 --> 00:52:02,239
Speaker 1: or landscape, to the point where like directors had to

895
00:52:02,280 --> 00:52:06,279
Speaker 1: frame their shots so that it would work in either orientation.

896
00:52:06,600 --> 00:52:08,360
Speaker 1: But the project did not work out so well in

897
00:52:08,400 --> 00:52:10,880
Speaker 1: a world where everybody was stuck at home because of

898
00:52:10,920 --> 00:52:13,880
Speaker 1: the coronavirus and they would just watch stuff on larger

899
00:52:13,920 --> 00:52:17,799
Speaker 1: screens instead of their phones. Most analysts that I've read

900
00:52:17,920 --> 00:52:20,360
Speaker 1: up on seemed to agree that even if the pandemic

901
00:52:20,360 --> 00:52:22,800
Speaker 1: hadn't been a thing, Quibi's success was a long shot

902
00:52:22,840 --> 00:52:26,480
Speaker 1: at best. And that you couldn't say that the coronavirus

903
00:52:26,600 --> 00:52:29,600
Speaker 1: made Quibi fail, but sure to help. I think the

904
00:52:29,640 --> 00:52:32,480
Speaker 1: story to reflect on in twenty twenty is the solar

905
00:52:32,600 --> 00:52:35,040
Speaker 1: Winds hack, which opened their eyes to the dangers of

906
00:52:35,080 --> 00:52:38,600
Speaker 1: supply chain attacks. Now that's a complicated story, but i'll

907
00:52:38,600 --> 00:52:41,880
Speaker 1: sum it up. A company called solar Winds creates software

908
00:52:41,920 --> 00:52:46,279
Speaker 1: for enterprise clients, so that is other businesses. Hackers were

909
00:52:46,320 --> 00:52:50,160
Speaker 1: able to hide some malware into a solar Winds update

910
00:52:50,440 --> 00:52:54,080
Speaker 1: to software called Orion. So rather than focus on the

911
00:52:54,200 --> 00:52:57,640
Speaker 1: end targets such as you know, specific agencies in the

912
00:52:57,760 --> 00:53:01,840
Speaker 1: US government, the hackers implant the malware in a trusted

913
00:53:01,880 --> 00:53:05,960
Speaker 1: partner's software package, which then got pushed out to the

914
00:53:06,000 --> 00:53:09,960
Speaker 1: customers all around the world and infected those customers. So

915
00:53:10,040 --> 00:53:13,839
Speaker 1: this was a huge and very effective cyber attack. While

916
00:53:13,880 --> 00:53:17,640
Speaker 1: thousands of organizations were impacted, it appeared as though the

917
00:53:17,640 --> 00:53:21,560
Speaker 1: attackers had a few specific targets in mind. It's just

918
00:53:21,880 --> 00:53:25,440
Speaker 1: their attack was super effective across all of solar Winds

919
00:53:25,480 --> 00:53:29,160
Speaker 1: customers using the Orion platforms. So the reason I included

920
00:53:29,239 --> 00:53:32,239
Speaker 1: in this episode isn't because of how devastating the attack was,

921
00:53:32,280 --> 00:53:35,600
Speaker 1: although it was quite devastating, but rather it illustrates how

922
00:53:35,640 --> 00:53:38,880
Speaker 1: effective a supply chain attack can be. So, if you

923
00:53:39,080 --> 00:53:42,920
Speaker 1: have identified that your target has really strong defenses and

924
00:53:42,960 --> 00:53:45,400
Speaker 1: you're not likely to be able to penetrate their systems,

925
00:53:45,880 --> 00:53:49,840
Speaker 1: maybe you target one of their trusted partners so you

926
00:53:49,840 --> 00:53:53,680
Speaker 1: can find a way in. It's scary stuff. In twenty

927
00:53:53,800 --> 00:53:57,960
Speaker 1: twenty one, we had a few interesting things happen. Antivirus

928
00:53:58,000 --> 00:54:02,600
Speaker 1: software developer and totally bunked's personality, John McAfee passed away.

929
00:54:03,000 --> 00:54:05,919
Speaker 1: I know it's ghost to speak ill of the dead,

930
00:54:05,960 --> 00:54:10,520
Speaker 1: but this guy really was wild. So in a normal world,

931
00:54:10,880 --> 00:54:13,360
Speaker 1: McAfee would best be known as a guy who created

932
00:54:13,480 --> 00:54:16,319
Speaker 1: McAfee antivirus. But we don't live in a normal world.

933
00:54:16,400 --> 00:54:18,000
Speaker 1: We live in this one, and this is the world

934
00:54:18,040 --> 00:54:21,080
Speaker 1: where McAfee was perhaps best known for living a kind

935
00:54:21,120 --> 00:54:24,720
Speaker 1: of sex, drugs, and rock and roll lifestyle, which comes

936
00:54:24,760 --> 00:54:27,359
Speaker 1: complete with him living in a different country and at

937
00:54:27,400 --> 00:54:32,480
Speaker 1: one point being under suspicion for a committing murder on

938
00:54:32,480 --> 00:54:34,960
Speaker 1: one of his neighbors, something that may or may not

939
00:54:35,000 --> 00:54:38,000
Speaker 1: have involved him, we don't know. He was wanted in

940
00:54:38,040 --> 00:54:41,240
Speaker 1: the United States for tax evasion, and he was arrested

941
00:54:41,280 --> 00:54:45,680
Speaker 1: while in Barcelona Spain, and then he was found dead

942
00:54:45,840 --> 00:54:48,520
Speaker 1: in his prison cell not long after. The Spanish courts

943
00:54:48,520 --> 00:54:52,720
Speaker 1: that approved his extradition to the United States absolutely wild,

944
00:54:53,160 --> 00:54:56,479
Speaker 1: and the implication is that he did take his own

945
00:54:56,480 --> 00:54:59,640
Speaker 1: life while he was in jail rather than be extradited

946
00:54:59,680 --> 00:55:03,720
Speaker 1: to the un to stand trial. Microsoft launched Windows eleven

947
00:55:03,760 --> 00:55:06,560
Speaker 1: and twenty twenty one, and it has generally received a

948
00:55:06,600 --> 00:55:11,520
Speaker 1: more positive reception. There are some notable exceptions that would

949
00:55:11,840 --> 00:55:15,680
Speaker 1: unfold later on. Microsoft had skipped over the number nine.

950
00:55:16,120 --> 00:55:19,000
Speaker 1: It went from Windows eight to Windows ten. Windows ten

951
00:55:19,080 --> 00:55:21,279
Speaker 1: got a fair reception. Windows eleven seemed to be an

952
00:55:21,280 --> 00:55:25,280
Speaker 1: improvement in many ways, but some recent developments have critics

953
00:55:25,360 --> 00:55:29,600
Speaker 1: upset at Microsoft, namely the recall or recall feature, which

954
00:55:29,640 --> 00:55:32,680
Speaker 1: takes snapshots of user activities on their computers, so like,

955
00:55:32,880 --> 00:55:36,600
Speaker 1: you can search through these snapshots to find times when

956
00:55:36,640 --> 00:55:39,120
Speaker 1: you've visited certain sites or use certain apps or whatever.

957
00:55:39,320 --> 00:55:42,839
Speaker 1: Advocates have argued that this represents a privacy and security vulnerability,

958
00:55:43,040 --> 00:55:46,640
Speaker 1: and Microsoft did tweak it quite a bit in response

959
00:55:46,680 --> 00:55:49,799
Speaker 1: to those criticisms. But it's opt in, so at least

960
00:55:49,800 --> 00:55:52,480
Speaker 1: you're not like using it by default. But I guess

961
00:55:52,600 --> 00:55:54,759
Speaker 1: the big story that year was really a pair of

962
00:55:54,800 --> 00:55:57,600
Speaker 1: news items that arguably had some connection to one another,

963
00:55:57,680 --> 00:56:00,920
Speaker 1: and both of them involved Facebook. So in the first story,

964
00:56:00,960 --> 00:56:04,319
Speaker 1: a whistleblower named Francis Hogan, who had previously worked as

965
00:56:04,320 --> 00:56:07,319
Speaker 1: a data scientist for Facebook, came forward with the accusations

966
00:56:07,320 --> 00:56:10,759
Speaker 1: and documentation showing that Facebook's policies did a lot of

967
00:56:10,800 --> 00:56:14,080
Speaker 1: bad stuff, like putting children at risk, for example, and

968
00:56:14,120 --> 00:56:17,680
Speaker 1: that Facebook executives further know about this, and the company

969
00:56:17,719 --> 00:56:20,120
Speaker 1: had worked hard to hide it from the public while

970
00:56:20,160 --> 00:56:26,600
Speaker 1: still pursuing these variously harmful or potentially harmful policies, and

971
00:56:26,640 --> 00:56:28,719
Speaker 1: that really what was all about was the company was

972
00:56:28,760 --> 00:56:32,080
Speaker 1: seeking profit over anything else, even in the wake of

973
00:56:32,120 --> 00:56:35,200
Speaker 1: potentially hurting not just children, which is bad enough all

974
00:56:35,239 --> 00:56:39,800
Speaker 1: by itself, but also hurting things like institutions like democracy.

975
00:56:40,120 --> 00:56:43,760
Speaker 1: And Haugen's testimony to Congress was brutal. It is wild

976
00:56:43,840 --> 00:56:46,040
Speaker 1: to me that we have largely forgotten the things she

977
00:56:46,160 --> 00:56:49,200
Speaker 1: brought forward back in twenty twenty one, because it was

978
00:56:49,280 --> 00:56:53,200
Speaker 1: extensive and I thought pretty damning, But you know, we've

979
00:56:53,280 --> 00:56:56,120
Speaker 1: largely moved on since then. Now. Not long after her

980
00:56:56,200 --> 00:57:00,440
Speaker 1: accusations became headlines, Facebook added to the chaos by announcing

981
00:57:00,440 --> 00:57:03,640
Speaker 1: the company was changing its name to Meta Now. Ostensibly

982
00:57:03,719 --> 00:57:06,160
Speaker 1: this was because Facebook would be investing heavily in the

983
00:57:06,360 --> 00:57:10,120
Speaker 1: metaverse concept, which is a vague vision of the future

984
00:57:10,120 --> 00:57:13,880
Speaker 1: of online interactivity that typically involves some form of mixed

985
00:57:13,920 --> 00:57:17,400
Speaker 1: reality component. Critics argued that it was an attempt for

986
00:57:17,440 --> 00:57:20,520
Speaker 1: the company to distance itself from the very real accusations

987
00:57:20,560 --> 00:57:24,000
Speaker 1: flying from the testimony and documentation that had come out

988
00:57:24,000 --> 00:57:26,520
Speaker 1: earlier in the year. However, here we are in twenty

989
00:57:26,600 --> 00:57:29,120
Speaker 1: twenty four and Meta is still very much a thing.

990
00:57:29,280 --> 00:57:32,080
Speaker 1: Investors sometimes get a little miffed at Mark Zuckerberg for

991
00:57:32,120 --> 00:57:34,720
Speaker 1: continuing to pour so much money and R and D

992
00:57:34,960 --> 00:57:38,800
Speaker 1: into mixed reality AI and the metaverse concepts, but other

993
00:57:38,880 --> 00:57:43,680
Speaker 1: than that, the Meta ship continues to sail fairly smoothly

994
00:57:43,760 --> 00:57:46,680
Speaker 1: all things considered. All Right, now, we're up to twenty

995
00:57:46,720 --> 00:57:49,720
Speaker 1: twenty two. Man, let's power through it saw a lot

996
00:57:49,760 --> 00:57:52,960
Speaker 1: of stuff happen in twenty twenty two. Non fungible tokens

997
00:57:53,000 --> 00:57:57,200
Speaker 1: aka NFTs had become mainstream headlines in twenty twenty one.

998
00:57:57,360 --> 00:57:59,280
Speaker 1: They'd been around a little longer than that, but twenty

999
00:57:59,320 --> 00:58:02,480
Speaker 1: twenty one was when the mainstream public really became aware

1000
00:58:02,520 --> 00:58:06,000
Speaker 1: of them, and that's when we saw a speculative market

1001
00:58:06,040 --> 00:58:09,720
Speaker 1: form around these blockchain based digital markers, and I think

1002
00:58:09,720 --> 00:58:12,680
Speaker 1: a lot of the speculation was rooted deeply in ignorance

1003
00:58:12,760 --> 00:58:16,200
Speaker 1: as to what NFTs actually are, or maybe more accurately,

1004
00:58:16,240 --> 00:58:19,240
Speaker 1: what they are not. Anyway, it was early in twenty

1005
00:58:19,280 --> 00:58:22,600
Speaker 1: twenty two when that speculative market collapsed in on itself,

1006
00:58:23,040 --> 00:58:26,240
Speaker 1: and NFTs as a whole lost nearly all of their

1007
00:58:26,640 --> 00:58:30,520
Speaker 1: value in the process. So what had been the gold

1008
00:58:30,560 --> 00:58:33,520
Speaker 1: mine of twenty twenty one became kind of a punchline

1009
00:58:33,960 --> 00:58:36,880
Speaker 1: in twenty twenty two, and suddenly some people found themselves

1010
00:58:36,880 --> 00:58:40,600
Speaker 1: in possession of a digital marker that was worthless after

1011
00:58:40,640 --> 00:58:44,160
Speaker 1: they had spent perhaps thousands or tens of thousands of

1012
00:58:44,200 --> 00:58:47,320
Speaker 1: dollars on them. One big story that spanned nearly all

1013
00:58:47,360 --> 00:58:51,160
Speaker 1: of twenty twenty two was Elon Musk's quest to purchase Twitter.

1014
00:58:51,640 --> 00:58:54,440
Speaker 1: His intentions first became clear in the spring of twenty

1015
00:58:54,480 --> 00:58:57,200
Speaker 1: twenty two, but not long after he announced his plan

1016
00:58:57,320 --> 00:59:00,520
Speaker 1: to buy it, he began to backpedal, and he tried

1017
00:59:00,520 --> 00:59:02,200
Speaker 1: to get out of the deal, but doing so would

1018
00:59:02,200 --> 00:59:04,880
Speaker 1: have cost him an awful lot of money because he

1019
00:59:04,920 --> 00:59:08,560
Speaker 1: had already entered into the negotiations in good faith and

1020
00:59:08,640 --> 00:59:12,720
Speaker 1: backing out just wasn't an option, at least not without consequences.

1021
00:59:13,080 --> 00:59:15,920
Speaker 1: At one point, the United States court system got involved,

1022
00:59:16,040 --> 00:59:19,840
Speaker 1: and Musk capitulated and agreed to buy Twitter for a

1023
00:59:19,840 --> 00:59:24,040
Speaker 1: whopping forty four billion dollars before he was forced to

1024
00:59:24,080 --> 00:59:29,600
Speaker 1: do so, so he did it by choice technically. One

1025
00:59:29,640 --> 00:59:32,720
Speaker 1: of the investors in Twitter, a company called Fidelity, would

1026
00:59:32,840 --> 00:59:36,480
Speaker 1: later estimate in twenty twenty four that Twitter's value had

1027
00:59:36,760 --> 00:59:39,520
Speaker 1: gone all the way down to nine point four billion dollars.

1028
00:59:39,600 --> 00:59:41,840
Speaker 1: So he spent forty four billion and now it's worth

1029
00:59:41,960 --> 00:59:44,200
Speaker 1: nine point four billion. That is a heck of a

1030
00:59:44,240 --> 00:59:47,840
Speaker 1: decline in just two years. Russia's invasion of Ukraine would

1031
00:59:47,880 --> 00:59:51,080
Speaker 1: also have a huge impact on technology. For one thing,

1032
00:59:51,080 --> 00:59:53,720
Speaker 1: the US would start to restrict business dealings with Russian

1033
00:59:53,760 --> 00:59:57,560
Speaker 1: based companies that included tech companies like Kaspersky, which is

1034
00:59:57,600 --> 01:00:01,280
Speaker 1: a cybersecurity firm, and many a American based tech companies

1035
01:00:01,320 --> 01:00:05,000
Speaker 1: would pull out of operations within Russia. So you had

1036
01:00:05,160 --> 01:00:07,960
Speaker 1: companies just picking up stakes and moving out of Russia,

1037
01:00:08,000 --> 01:00:12,360
Speaker 1: sometimes even suspending customer access to platforms within Russia in

1038
01:00:12,400 --> 01:00:15,040
Speaker 1: the process. YouTube has been in hot water with the

1039
01:00:15,120 --> 01:00:19,439
Speaker 1: Russian government on and off again since this, because they

1040
01:00:19,560 --> 01:00:24,080
Speaker 1: have done things like block state backed misinformation channels because

1041
01:00:24,120 --> 01:00:28,439
Speaker 1: it's misinformation and Russia's government doesn't like that very much. Also,

1042
01:00:28,600 --> 01:00:31,320
Speaker 1: as the tech sector began to settle after the chaos

1043
01:00:31,320 --> 01:00:33,680
Speaker 1: that had followed the COVID outbreak, we started seeing the

1044
01:00:33,720 --> 01:00:37,440
Speaker 1: first of many rounds of layoffs. Companies that had grown

1045
01:00:37,560 --> 01:00:40,960
Speaker 1: very quickly to respond to changing conditions during the pandemic

1046
01:00:40,960 --> 01:00:44,120
Speaker 1: found themselves ready to slim down again once things were

1047
01:00:44,160 --> 01:00:47,320
Speaker 1: starting to settle, and we still see that happening today.

1048
01:00:47,800 --> 01:00:50,720
Speaker 1: There have been numerous cuts in the sector and it's

1049
01:00:50,760 --> 01:00:54,400
Speaker 1: really demoralizing and hard to see those headlines month after month.

1050
01:00:54,920 --> 01:00:59,200
Speaker 1: Also in twenty twenty two, we got open AI's chat GPT.

1051
01:00:59,560 --> 01:01:02,880
Speaker 1: Arguably that should be my big story, because generative AI

1052
01:01:03,040 --> 01:01:05,640
Speaker 1: would continue to be an enormous topic in the following years.

1053
01:01:05,920 --> 01:01:07,680
Speaker 1: But I think the big story I want to focus

1054
01:01:07,760 --> 01:01:11,000
Speaker 1: on from twenty twenty two is the implosion of FTX,

1055
01:01:11,080 --> 01:01:14,760
Speaker 1: the cryptocurrency exchange. So as a reminder, the face of

1056
01:01:14,800 --> 01:01:18,280
Speaker 1: this company, the co founder Sam Bankman Freed or SBF,

1057
01:01:18,680 --> 01:01:21,320
Speaker 1: would be accused of running a kind of fraud in

1058
01:01:21,320 --> 01:01:25,640
Speaker 1: which he was taking customer deposits in FTX and then

1059
01:01:25,760 --> 01:01:28,640
Speaker 1: using those to cover investments that were being made by

1060
01:01:28,680 --> 01:01:32,600
Speaker 1: a sister company called Alameda Research. So Alameda was kind

1061
01:01:32,600 --> 01:01:35,120
Speaker 1: of a hedge fund company that would invest in businesses

1062
01:01:35,160 --> 01:01:38,440
Speaker 1: related to the crypto space, but to cover investments that

1063
01:01:38,480 --> 01:01:42,520
Speaker 1: were made there. SBF and team were siphoning money away

1064
01:01:42,520 --> 01:01:44,760
Speaker 1: from FTX in the hopes that no one would really

1065
01:01:44,800 --> 01:01:46,720
Speaker 1: notice and that maybe in the long run they would

1066
01:01:46,720 --> 01:01:49,200
Speaker 1: be able to cover all their bases. But an expose

1067
01:01:49,440 --> 01:01:52,840
Speaker 1: in late twenty twenty two led to investigations, which in

1068
01:01:52,880 --> 01:01:55,800
Speaker 1: turn led to SBF getting the boot from the company

1069
01:01:55,840 --> 01:01:59,400
Speaker 1: and ultimately to his arrest. He has since been sentenced

1070
01:01:59,400 --> 01:02:02,040
Speaker 1: to twenty five years in prison and ordered to pay

1071
01:02:02,160 --> 01:02:06,160
Speaker 1: eleven billion with a B dollars in forfeiture, which is

1072
01:02:06,200 --> 01:02:08,760
Speaker 1: something I suspect will not ever be paid off, but heck,

1073
01:02:08,800 --> 01:02:12,280
Speaker 1: who knows, maybe he'll get a pardon. This administration lot

1074
01:02:12,320 --> 01:02:15,000
Speaker 1: to see now. The collapse of FDx added to the

1075
01:02:15,040 --> 01:02:18,040
Speaker 1: instability in general in the crypto market. It led to

1076
01:02:18,120 --> 01:02:21,960
Speaker 1: other crypto exchanges and other companies falling apart as a result,

1077
01:02:22,080 --> 01:02:24,640
Speaker 1: including a couple of banks that had been instrumental in

1078
01:02:24,640 --> 01:02:27,800
Speaker 1: the crypto world, and for a short while, it looked

1079
01:02:27,840 --> 01:02:30,560
Speaker 1: like crypto in general, could end up sinking as a

1080
01:02:30,560 --> 01:02:34,120
Speaker 1: result of the FTX failure, but ultimately the industry did

1081
01:02:34,200 --> 01:02:36,760
Speaker 1: weather the storm and would prove to be as volatile

1082
01:02:36,800 --> 01:02:38,920
Speaker 1: as ever in the long run. But yeah, the failure

1083
01:02:38,960 --> 01:02:41,959
Speaker 1: of FTX was a reminder that sometimes tech isn't there

1084
01:02:42,000 --> 01:02:45,760
Speaker 1: to enable new things, but maybe just mask attempts to

1085
01:02:45,760 --> 01:02:48,440
Speaker 1: commit fraud, you know, just make it confusing enough and

1086
01:02:48,520 --> 01:02:53,760
Speaker 1: nobody asks any questions. Okay. Twenty twenty three this was

1087
01:02:53,800 --> 01:02:56,040
Speaker 1: the year that Musk changed the name of Twitter to

1088
01:02:56,400 --> 01:02:59,840
Speaker 1: X while making sweeping changes that alienated many users and

1089
01:03:00,080 --> 01:03:03,240
Speaker 1: encouraged a bunch of others. The users who were alienated

1090
01:03:03,320 --> 01:03:07,400
Speaker 1: often left and ultimately would go to some other alternative

1091
01:03:07,680 --> 01:03:12,560
Speaker 1: short form messaging or social network services. The whole pay

1092
01:03:12,640 --> 01:03:15,800
Speaker 1: for a verified check mark strategy must introduce chased off

1093
01:03:15,800 --> 01:03:18,080
Speaker 1: a ton of people who had previously received a check

1094
01:03:18,080 --> 01:03:21,200
Speaker 1: mark from being a notable personality of some sort. I

1095
01:03:21,240 --> 01:03:23,000
Speaker 1: was one of those people. I had a checkmark and

1096
01:03:23,040 --> 01:03:24,800
Speaker 1: I was on Twitter. I would argue I am not

1097
01:03:24,960 --> 01:03:27,640
Speaker 1: actually notable, but I did have a check mark. But

1098
01:03:27,800 --> 01:03:30,200
Speaker 1: you know, twenty twenty three was really a year that

1099
01:03:30,320 --> 01:03:34,400
Speaker 1: was all about AI. So Chad GBT had debuted in

1100
01:03:34,440 --> 01:03:36,960
Speaker 1: twenty twenty two, but it was twenty twenty three that

1101
01:03:37,080 --> 01:03:40,840
Speaker 1: saw artificial intelligence make headlines pretty much every single day

1102
01:03:40,840 --> 01:03:43,960
Speaker 1: of the year. Sometimes it was because of the incredible

1103
01:03:43,960 --> 01:03:48,800
Speaker 1: display of AI's capabilities, some of which really are you know, amazing.

1104
01:03:49,000 --> 01:03:53,280
Speaker 1: There were also tons of think pieces about the technology's shortcomings,

1105
01:03:53,320 --> 01:03:59,240
Speaker 1: everything from hallucinations or confabulations to the possible impact on

1106
01:03:59,480 --> 01:04:03,520
Speaker 1: creative like you know, whether or not AI is plagiarizing

1107
01:04:03,560 --> 01:04:05,840
Speaker 1: the work of actual human beings or putting them out

1108
01:04:05,840 --> 01:04:08,520
Speaker 1: of work because folks will use AI to I don't know,

1109
01:04:08,640 --> 01:04:13,400
Speaker 1: generate marketing slogans as opposed to hiring marketers or create

1110
01:04:13,480 --> 01:04:15,720
Speaker 1: artwork instead of hiring an artist, that kind of stuff.

1111
01:04:16,000 --> 01:04:19,040
Speaker 1: We got lots of warnings from critics who were worried

1112
01:04:19,080 --> 01:04:22,560
Speaker 1: about deploying a technology before it was thoroughly tested to

1113
01:04:22,560 --> 01:04:24,919
Speaker 1: make sure it was safe. You know, we just could

1114
01:04:25,000 --> 01:04:27,959
Speaker 1: not get away from AI in twenty twenty three. Whether

1115
01:04:28,000 --> 01:04:32,000
Speaker 1: it was Open AI or Google or Meta or Microsoft

1116
01:04:32,080 --> 01:04:37,120
Speaker 1: or Amazon or some other company. Artificial intelligence dominated headlines

1117
01:04:37,200 --> 01:04:39,960
Speaker 1: throughout twenty twenty three, and we asked a lot of

1118
01:04:40,040 --> 01:04:43,000
Speaker 1: questions and we only got a few answers, and none

1119
01:04:43,080 --> 01:04:46,480
Speaker 1: of them were relevant or good. So we're still working

1120
01:04:46,520 --> 01:04:50,160
Speaker 1: on that related to this. In twenty twenty three, Sam Altman,

1121
01:04:50,240 --> 01:04:52,680
Speaker 1: who was one of the co founders and the CEO

1122
01:04:52,880 --> 01:04:56,280
Speaker 1: of open Ai, got the boot from the Board of directors.

1123
01:04:56,560 --> 01:05:00,640
Speaker 1: He was fired, but then he was a to return

1124
01:05:00,680 --> 01:05:03,920
Speaker 1: to the company after enormous pressure was put on the

1125
01:05:03,920 --> 01:05:07,160
Speaker 1: Board of directors to reverse their decision. The board was

1126
01:05:07,240 --> 01:05:10,360
Speaker 1: concerned that Altman was leading open Ai in a direction

1127
01:05:10,440 --> 01:05:13,600
Speaker 1: that was in opposition to its founding principles, which were

1128
01:05:14,120 --> 01:05:19,560
Speaker 1: to develop AI in a safe and accountable and transparent way.

1129
01:05:20,000 --> 01:05:22,560
Speaker 1: So the board directors were saying, that's not what's happening,

1130
01:05:22,680 --> 01:05:24,480
Speaker 1: and we need to get rid of this guy because

1131
01:05:24,520 --> 01:05:27,360
Speaker 1: he's pushing the company to do something that's fundamentally in

1132
01:05:27,400 --> 01:05:31,120
Speaker 1: opposition to what it's supposed to be about. But the

1133
01:05:31,120 --> 01:05:34,920
Speaker 1: board's decision was reversed. The board was then kind of

1134
01:05:35,240 --> 01:05:38,000
Speaker 1: given the boot and replaced, and Sam Altman to this

1135
01:05:38,120 --> 01:05:41,520
Speaker 1: day remains the CEO of open Ai, And considering the

1136
01:05:41,560 --> 01:05:45,520
Speaker 1: stories involving open Ai since then, I think the Board

1137
01:05:45,520 --> 01:05:51,880
Speaker 1: of Directors' concerns were merited. I'll say that. And then finally,

1138
01:05:52,360 --> 01:05:54,840
Speaker 1: after all this time, we get to twenty twenty four,

1139
01:05:55,120 --> 01:05:58,000
Speaker 1: and a lot happened in twenty twenty four, but let's

1140
01:05:58,000 --> 01:06:01,080
Speaker 1: be honest, there's only one story that we really need

1141
01:06:01,080 --> 01:06:04,160
Speaker 1: to talk about. The big story in tech is that

1142
01:06:04,320 --> 01:06:08,360
Speaker 1: Jonathan Strickland, co founder and host of the popular podcast

1143
01:06:08,400 --> 01:06:11,080
Speaker 1: tech Stuff, announced he would be stepping down from the

1144
01:06:11,120 --> 01:06:16,160
Speaker 1: show in early twenty twenty five. In fact, next episode

1145
01:06:16,240 --> 01:06:20,120
Speaker 1: will be my last as host of the show officially,

1146
01:06:20,440 --> 01:06:23,160
Speaker 1: though again who knows. I may occasionally pop in with

1147
01:06:23,240 --> 01:06:27,600
Speaker 1: a special episode here or there, depending upon circumstances, but

1148
01:06:28,080 --> 01:06:30,240
Speaker 1: I should add I don't have any plans to do

1149
01:06:30,280 --> 01:06:33,760
Speaker 1: that just yet. I'm just saying it's a possibility. I'm

1150
01:06:33,800 --> 01:06:38,000
Speaker 1: not leaving it off the table. It could happen. We

1151
01:06:38,120 --> 01:06:40,200
Speaker 1: just don't have plans for it. But yeah, the curtain

1152
01:06:40,240 --> 01:06:42,640
Speaker 1: is closing in on me and I've got one episode

1153
01:06:42,680 --> 01:06:45,960
Speaker 1: left before I shuffle off backstage. And in our next episode,

1154
01:06:46,160 --> 01:06:49,000
Speaker 1: you will meet the new hosts of Tech Stuff, the

1155
01:06:49,000 --> 01:06:51,520
Speaker 1: people who will bring the show to new heights that

1156
01:06:51,600 --> 01:06:55,360
Speaker 1: I can only dream about. I'm genuinely excited to see

1157
01:06:55,360 --> 01:06:58,000
Speaker 1: where they take it and how they share their own

1158
01:06:58,120 --> 01:07:01,560
Speaker 1: view of technology. And yeah, the show will be different,

1159
01:07:01,720 --> 01:07:04,120
Speaker 1: but I have no doubts it will be even better

1160
01:07:04,560 --> 01:07:08,760
Speaker 1: than before, and now so I can say it one

1161
01:07:08,840 --> 01:07:12,959
Speaker 1: last time to y'all, I'll talk to you again really soon.

1162
01:07:19,240 --> 01:07:23,880
Speaker 1: Tech Stuff is an iHeartRadio production. For more podcasts from iHeartRadio,

1163
01:07:24,200 --> 01:07:27,919
Speaker 1: visit the iHeartRadio app, Apple Podcasts, or wherever you listen

1164
01:07:27,960 --> 01:07:32,640
Speaker 1: to your favorite shows.