1 00:00:04,440 --> 00:00:12,400 Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Hey there, 2 00:00:12,400 --> 00:00:15,880 Speaker 1: and welcome to tech Stuff. I'm your host Jonathan Strickland. 3 00:00:15,920 --> 00:00:18,959 Speaker 1: I'm an executive producer with iHeart Podcasts and How the 4 00:00:19,000 --> 00:00:21,239 Speaker 1: tech Are Yet, it's time for us to cover the 5 00:00:21,280 --> 00:00:24,480 Speaker 1: tech news for the week ending on March twenty ninth, 6 00:00:24,600 --> 00:00:29,520 Speaker 1: twenty twenty four, and our first story is that FTX 7 00:00:29,560 --> 00:00:36,800 Speaker 1: cryptocurrency exchange and Alameda Research founder Sam Bankman freed AKASBF 8 00:00:36,960 --> 00:00:40,920 Speaker 1: faced sentencing for his multitude of crimes relating to playing 9 00:00:41,000 --> 00:00:44,320 Speaker 1: fast and loose with a whole lot of people's money. 10 00:00:45,040 --> 00:00:48,160 Speaker 1: SBF had sought a light sentence of just a few years, 11 00:00:48,200 --> 00:00:52,400 Speaker 1: while prosecutors were hoping for a forty or fifty year sentence. 12 00:00:52,720 --> 00:00:55,760 Speaker 1: He could have faced a maximum of one hundred and 13 00:00:55,840 --> 00:00:59,600 Speaker 1: ten years in the jailhouse, but Judge Lewis Kaplan has 14 00:00:59,640 --> 00:01:03,200 Speaker 1: decided on a twenty five year sentence, not as heavy 15 00:01:03,320 --> 00:01:06,200 Speaker 1: as what prosecutors wanted, but way the heck more than 16 00:01:06,240 --> 00:01:09,399 Speaker 1: what SBF was hoping for. The judge said that the 17 00:01:09,440 --> 00:01:14,679 Speaker 1: defense had put forth a pretty lousy effort, plus SBF 18 00:01:14,720 --> 00:01:19,120 Speaker 1: had engaged in witness tampering, which affected the judge's decision. 19 00:01:19,680 --> 00:01:21,680 Speaker 1: Now of course, this isn't the end of it all. 20 00:01:21,959 --> 00:01:26,880 Speaker 1: SBF does plan to appeal both the decision and the sentence, though, 21 00:01:26,959 --> 00:01:29,920 Speaker 1: considering the issues of witness tampering and such, I think 22 00:01:29,920 --> 00:01:34,120 Speaker 1: he probably faces an uphill battle on that one. He 23 00:01:34,160 --> 00:01:37,520 Speaker 1: has done himself very few favors. In the wake of 24 00:01:37,560 --> 00:01:42,399 Speaker 1: his empire collapsing. An English data company called Savanta conducted 25 00:01:42,400 --> 00:01:47,120 Speaker 1: a poll judging American's opinions about banning TikTok, and the 26 00:01:47,240 --> 00:01:51,400 Speaker 1: results are unsurprising. So according to savantah only around twenty 27 00:01:51,440 --> 00:01:54,360 Speaker 1: eight percent of all respondents favored a ban on TikTok, 28 00:01:54,680 --> 00:01:58,880 Speaker 1: and one of the questions they included asked if respondent's 29 00:01:59,200 --> 00:02:02,920 Speaker 1: friends would try to find ways to continue to access 30 00:02:02,960 --> 00:02:05,440 Speaker 1: TikTok even if it were to be banned in the 31 00:02:05,560 --> 00:02:10,720 Speaker 1: United States. That particular question got a sixty percent affirmative response. 32 00:02:11,080 --> 00:02:14,600 Speaker 1: I guess this was actually Savanta's way of saying, look, mate, 33 00:02:14,760 --> 00:02:17,080 Speaker 1: I'm not saying you'd break the law, right, I'm not 34 00:02:17,120 --> 00:02:20,160 Speaker 1: saying that, but your friends. Right, there's some shady characters, 35 00:02:20,200 --> 00:02:22,640 Speaker 1: any of them likely to slip around the corner, maybe 36 00:02:22,680 --> 00:02:25,760 Speaker 1: log into a little VP, maybe do a little scrolling 37 00:02:25,880 --> 00:02:30,080 Speaker 1: on TikTok. A anyway. Like I said, the results are unsurprising, 38 00:02:30,160 --> 00:02:32,920 Speaker 1: but they might very well provide the US Senate with 39 00:02:32,960 --> 00:02:35,560 Speaker 1: some food for thought, as that body now has the 40 00:02:35,600 --> 00:02:39,080 Speaker 1: responsibility to take up this issue or to you know, 41 00:02:39,200 --> 00:02:41,160 Speaker 1: not take it up. They could do that too, They 42 00:02:41,200 --> 00:02:44,280 Speaker 1: could just not consider it. I still think it's likely 43 00:02:44,360 --> 00:02:46,639 Speaker 1: that the Senate is not going to pass this bill 44 00:02:46,639 --> 00:02:49,520 Speaker 1: into lab because the perception is it would be very 45 00:02:49,520 --> 00:02:52,360 Speaker 1: harmful to their chances of getting re elected, and that 46 00:02:52,440 --> 00:02:57,480 Speaker 1: seems to be a pretty big driver in politics these days. Meanwhile, 47 00:02:57,880 --> 00:03:00,880 Speaker 1: TikTok has invested more than two million dollars in an 48 00:03:00,880 --> 00:03:03,760 Speaker 1: ad campaign in an effort to pressure the Senate to 49 00:03:03,919 --> 00:03:07,760 Speaker 1: drop the proposed ban legislation, and they've targeted the ads 50 00:03:07,760 --> 00:03:12,160 Speaker 1: and battleground states where Democrat senators could face a pretty 51 00:03:12,280 --> 00:03:15,520 Speaker 1: darn tough reelection campaign if the public were to be 52 00:03:15,600 --> 00:03:19,760 Speaker 1: totally cheesed off at them. Specifically, they are running ads 53 00:03:19,800 --> 00:03:24,800 Speaker 1: in Ohio, Wisconsin, Pennsylvania, Montana, and Nevada, And when you 54 00:03:24,880 --> 00:03:27,360 Speaker 1: know it, each one of those states has a Democrat 55 00:03:27,440 --> 00:03:30,520 Speaker 1: senator who's running for reelection this year. So I think 56 00:03:30,800 --> 00:03:34,000 Speaker 1: senators are already a little worried about the repercussions they 57 00:03:34,000 --> 00:03:36,520 Speaker 1: would face if they tried to push this bill into law. 58 00:03:36,600 --> 00:03:40,120 Speaker 1: But this sort of really drives that threat home. And 59 00:03:40,280 --> 00:03:42,320 Speaker 1: I have yet to see one of these ads, but 60 00:03:42,440 --> 00:03:45,000 Speaker 1: to me, they kind of sound like the super sad 61 00:03:45,080 --> 00:03:48,760 Speaker 1: ads you would see for the ASPCA that feature Sarah 62 00:03:48,840 --> 00:03:51,480 Speaker 1: McLaughlin singing about how the puppies are now in the 63 00:03:51,600 --> 00:03:55,000 Speaker 1: arms of an angel or something, Except these ads show 64 00:03:55,080 --> 00:03:58,040 Speaker 1: supposed TikTok users talking about how the world would really 65 00:03:58,120 --> 00:04:00,640 Speaker 1: suck if there weren't a TikTok. Also, I'm a little 66 00:04:00,680 --> 00:04:05,640 Speaker 1: extra snarky today, speaking of politics, the Biden administration announced 67 00:04:05,680 --> 00:04:08,520 Speaker 1: some new policies regarding AI and its use by the 68 00:04:08,520 --> 00:04:12,280 Speaker 1: federal government. Those policies are that federal agencies are now 69 00:04:12,320 --> 00:04:14,920 Speaker 1: tasked with ensuring that their use of AI does not 70 00:04:15,000 --> 00:04:18,080 Speaker 1: put the rights and safety of American citizens at risk, 71 00:04:18,480 --> 00:04:22,200 Speaker 1: which sounds totally reasonable, but it also sounds like it's 72 00:04:22,360 --> 00:04:25,520 Speaker 1: ding dang hard to put into practical use, because we've 73 00:04:25,560 --> 00:04:29,080 Speaker 1: already talked about how there are challenges of just gauging 74 00:04:29,440 --> 00:04:32,800 Speaker 1: risk and danger with AI before you actually deploy it. 75 00:04:32,880 --> 00:04:36,479 Speaker 1: Sometimes you only find out after it's too late. Anyway, 76 00:04:36,640 --> 00:04:39,920 Speaker 1: the second of the three policies announced is that any 77 00:04:39,960 --> 00:04:44,040 Speaker 1: agency that is using AI has to disclose that use 78 00:04:44,320 --> 00:04:47,839 Speaker 1: as well as to publish a risk assessment report in 79 00:04:47,960 --> 00:04:51,479 Speaker 1: order to maintain transparency, which also seems reasonable to me, 80 00:04:51,960 --> 00:04:54,440 Speaker 1: though I do suspect some agencies will maybe be a 81 00:04:54,480 --> 00:04:58,400 Speaker 1: little less transparent than others based off history cough cough. 82 00:04:58,520 --> 00:05:02,919 Speaker 1: The FBI, and finally, each federal agency must create the 83 00:05:02,960 --> 00:05:07,280 Speaker 1: position of chief AI Officer. And further, the person in 84 00:05:07,320 --> 00:05:10,160 Speaker 1: that position has to be knowledgeable in the field. So 85 00:05:10,839 --> 00:05:13,320 Speaker 1: no hiring your cousin Bobby so that he has a 86 00:05:13,320 --> 00:05:17,080 Speaker 1: cushy government job unless you know Bobby is also an 87 00:05:17,120 --> 00:05:19,760 Speaker 1: authority in the field of AI. Now I'm having a 88 00:05:19,760 --> 00:05:21,800 Speaker 1: bit of fun with all this, but I do think 89 00:05:21,839 --> 00:05:24,080 Speaker 1: it is a decent start. It's just going to be 90 00:05:24,160 --> 00:05:26,360 Speaker 1: a really long road ahead of us, and it's hard 91 00:05:26,400 --> 00:05:29,160 Speaker 1: to predict if these measures are going to be adequate 92 00:05:29,200 --> 00:05:33,080 Speaker 1: to mitigate future harm caused by AI. In fact, let's 93 00:05:33,279 --> 00:05:36,800 Speaker 1: actually talk about how AI can cause harm. There's a 94 00:05:36,839 --> 00:05:39,920 Speaker 1: piece by Colin Letcher or Leecher. I'm not sure how 95 00:05:39,960 --> 00:05:42,359 Speaker 1: to say your last name. I apologize. It's in the 96 00:05:42,440 --> 00:05:46,120 Speaker 1: markup and it really raised my eyebrows. So late last 97 00:05:46,200 --> 00:05:49,640 Speaker 1: year the Mayor of New York City, Eric Adams, announced 98 00:05:49,640 --> 00:05:53,200 Speaker 1: that an AI powered chatbot tool would help New York 99 00:05:53,240 --> 00:05:56,440 Speaker 1: businesses operate in a way that worked within New York law. 100 00:05:56,560 --> 00:05:59,840 Speaker 1: So if a business owner had a question, they could 101 00:06:00,120 --> 00:06:03,839 Speaker 1: ask this chatbot instead of a real human being, and 102 00:06:03,880 --> 00:06:07,280 Speaker 1: the chatbot would in theory give an answer that was 103 00:06:07,400 --> 00:06:11,040 Speaker 1: in alignment with New York law, and the business person 104 00:06:11,120 --> 00:06:14,960 Speaker 1: could act on that. But Colin says that opposite is 105 00:06:15,000 --> 00:06:18,200 Speaker 1: actually true, that the chatbot has in fact made suggestions 106 00:06:18,200 --> 00:06:22,400 Speaker 1: that outright violate certain New York City laws, which seems 107 00:06:22,440 --> 00:06:24,520 Speaker 1: to be a big old whoop sie, and he provides 108 00:06:24,600 --> 00:06:29,640 Speaker 1: examples of the chatbot encouraging some shady business practices. So, 109 00:06:29,760 --> 00:06:32,360 Speaker 1: for example, in New York City, there is a law 110 00:06:32,360 --> 00:06:36,920 Speaker 1: that states landlords cannot discriminate tenants by source of income. 111 00:06:37,320 --> 00:06:41,279 Speaker 1: In most cases. There are some exceptions for very small 112 00:06:41,279 --> 00:06:44,360 Speaker 1: buildings where the landlord and their family also reside in 113 00:06:44,360 --> 00:06:47,720 Speaker 1: that building, but otherwise they're not allowed to discriminate no 114 00:06:47,800 --> 00:06:52,320 Speaker 1: matter where a renter is getting their rent money, such 115 00:06:52,320 --> 00:06:56,120 Speaker 1: as if it's from a government assistance program. So the 116 00:06:56,200 --> 00:07:00,000 Speaker 1: landlord is not legally allowed to deny anyone the opportun 117 00:07:00,080 --> 00:07:02,120 Speaker 1: unity to rent a home based on that, but the 118 00:07:02,200 --> 00:07:05,839 Speaker 1: chatbot declared that landlords are not required to accept tenants 119 00:07:06,080 --> 00:07:09,800 Speaker 1: if they're on rental assistance programs, which does sound like 120 00:07:09,960 --> 00:07:14,000 Speaker 1: that's advice that violates the law. Further, the chatbot said 121 00:07:14,040 --> 00:07:17,600 Speaker 1: that restaurant owners are entitled to a cut of server tips, 122 00:07:17,600 --> 00:07:20,960 Speaker 1: and that's also not true in New York City. In 123 00:07:21,000 --> 00:07:24,800 Speaker 1: some cases, bosses can use tips to count toward minimum 124 00:07:24,800 --> 00:07:28,160 Speaker 1: wage requirements, which I would argue is sort of like 125 00:07:28,520 --> 00:07:32,400 Speaker 1: taking tips away from servers, but they can't actually dip 126 00:07:32,520 --> 00:07:35,559 Speaker 1: into the tips themselves and take a cut. He also 127 00:07:35,600 --> 00:07:40,440 Speaker 1: includes several other examples in other scenarios, and he says 128 00:07:40,480 --> 00:07:44,320 Speaker 1: that in some tests the Markup did in their offices, 129 00:07:44,400 --> 00:07:48,520 Speaker 1: the bot would actually provide correct answers occasionally, so maybe 130 00:07:48,520 --> 00:07:52,560 Speaker 1: it depends upon how you word the question, although that's 131 00:07:52,600 --> 00:07:56,000 Speaker 1: not entirely clear. The fact that the bot often provided 132 00:07:56,080 --> 00:08:00,840 Speaker 1: a misleading or outright incorrect response is beyond troubling, particularly 133 00:08:00,920 --> 00:08:03,600 Speaker 1: when city government has proclaimed that the bot is a 134 00:08:03,600 --> 00:08:07,520 Speaker 1: way for business owners to navigate government issues. City officials 135 00:08:07,560 --> 00:08:10,240 Speaker 1: have said the bot is merely in a pilot program phase, 136 00:08:10,320 --> 00:08:12,520 Speaker 1: and the whole idea is that it's going to improve 137 00:08:12,760 --> 00:08:15,520 Speaker 1: over time, which I sure hopes so, because right now 138 00:08:15,560 --> 00:08:17,760 Speaker 1: it sounds like it's doing the opposite of what it 139 00:08:17,800 --> 00:08:20,320 Speaker 1: is intended to do, which you know, it's kind of 140 00:08:20,360 --> 00:08:23,160 Speaker 1: hard to get around that, and it all just depends 141 00:08:23,240 --> 00:08:26,440 Speaker 1: upon how you work'd your question. Apparently New York Cities 142 00:08:26,480 --> 00:08:29,680 Speaker 1: mayor ain't stopping there with AI. However, he announced that 143 00:08:29,720 --> 00:08:32,320 Speaker 1: the city is also going to deploy a system made 144 00:08:32,320 --> 00:08:36,400 Speaker 1: by a company called Evolved Technologies, and it's an AI 145 00:08:36,520 --> 00:08:41,160 Speaker 1: powered weapons detection system that's made by a company that 146 00:08:41,240 --> 00:08:45,199 Speaker 1: has been tied with issues relating to weapons detection, namely 147 00:08:45,520 --> 00:08:50,080 Speaker 1: problems like false positives or failure to detect. So just 148 00:08:50,120 --> 00:08:53,079 Speaker 1: imagine that you're trying to commute to work, but then 149 00:08:53,120 --> 00:08:55,680 Speaker 1: you get stopped by New York Police because an AI 150 00:08:55,679 --> 00:08:59,960 Speaker 1: protocol running on what is essentially a surveillance system mistaken 151 00:09:00,240 --> 00:09:04,160 Speaker 1: believes you're packing. So this particular system is similar to 152 00:09:04,200 --> 00:09:07,120 Speaker 1: a metal detector. It's something that commuters will have to 153 00:09:07,160 --> 00:09:09,679 Speaker 1: pass through before they can get on the subway, and 154 00:09:09,720 --> 00:09:14,920 Speaker 1: it uses quote ultra low frequency electromagnetic fields end quote 155 00:09:14,960 --> 00:09:17,920 Speaker 1: to detect weapons. The New York City Police Department will 156 00:09:17,960 --> 00:09:19,840 Speaker 1: be in charge of the program, and according to New 157 00:09:19,920 --> 00:09:22,920 Speaker 1: York law, they will have to publish a report about 158 00:09:22,960 --> 00:09:26,400 Speaker 1: the system's effectiveness and impact and sort of like a 159 00:09:26,520 --> 00:09:30,040 Speaker 1: risk assessment sort of thing once it's been in place 160 00:09:30,120 --> 00:09:33,439 Speaker 1: for a while. Okay, we're going to take a quick 161 00:09:33,480 --> 00:09:35,440 Speaker 1: break and when we come back, I've got a few 162 00:09:35,440 --> 00:09:48,120 Speaker 1: more news stories to get through. We're back, So moving 163 00:09:48,160 --> 00:09:53,040 Speaker 1: on away from AI in politics. Last week, Reddit finally 164 00:09:53,080 --> 00:09:56,880 Speaker 1: held its initial public offering or IPO, which means it 165 00:09:56,920 --> 00:10:00,480 Speaker 1: has now become a publicly traded company on the stock market. 166 00:10:00,640 --> 00:10:03,440 Speaker 1: And this week the company saw the stock price drop 167 00:10:03,559 --> 00:10:06,840 Speaker 1: like a rock after it had surged early on. So 168 00:10:07,000 --> 00:10:11,320 Speaker 1: like initially, Reddit stocks were doing great when it launched. 169 00:10:11,520 --> 00:10:14,560 Speaker 1: The stocks price was intended to be forty six dollars 170 00:10:14,840 --> 00:10:18,440 Speaker 1: upon the stock market opening. It actually opened above that 171 00:10:18,559 --> 00:10:22,440 Speaker 1: value at nearly forty nine dollars per share, and it 172 00:10:22,520 --> 00:10:24,800 Speaker 1: closed at the end of the day at fifty one 173 00:10:24,880 --> 00:10:27,080 Speaker 1: dollars per share, So at the end of first day 174 00:10:27,240 --> 00:10:31,559 Speaker 1: of trading, it was already up significantly from where you 175 00:10:32,040 --> 00:10:35,600 Speaker 1: was intended to launch at. On March twenty sixth, it 176 00:10:35,679 --> 00:10:38,800 Speaker 1: hit its high of nearly seventy five dollars per share, 177 00:10:39,160 --> 00:10:41,880 Speaker 1: but then things took a bit of a turn so 178 00:10:42,040 --> 00:10:45,920 Speaker 1: why did that happen. Well, a couple of disclosures really 179 00:10:46,400 --> 00:10:49,400 Speaker 1: seemed to do a number on Reddit stock prices. One 180 00:10:49,960 --> 00:10:53,240 Speaker 1: was that some risk management companies reported that the stock 181 00:10:53,360 --> 00:10:57,320 Speaker 1: was being overinflated beyond its actual value, essentially saying this 182 00:10:57,480 --> 00:11:00,440 Speaker 1: stock does not reflect what Reddit's actual value you is. 183 00:11:00,600 --> 00:11:03,200 Speaker 1: Keep in mind, Reddit, like a lot of tech companies 184 00:11:03,240 --> 00:11:06,840 Speaker 1: out there, hasn't posted a profit. It is a revenue 185 00:11:07,000 --> 00:11:10,840 Speaker 1: losing business. But that doesn't always matter on the stock market. 186 00:11:10,960 --> 00:11:13,800 Speaker 1: In fact, you could say that often it seems to 187 00:11:13,840 --> 00:11:18,040 Speaker 1: have no impact on the stock market whatsoever. But another 188 00:11:18,120 --> 00:11:21,840 Speaker 1: really big set of disclosures that I think hit that 189 00:11:21,920 --> 00:11:25,600 Speaker 1: stock price hard is that a corporate filing revealed that 190 00:11:25,640 --> 00:11:30,280 Speaker 1: Reddit CEO Steve Huffman sold half a million shares when 191 00:11:30,360 --> 00:11:33,400 Speaker 1: it went public, So he cashed in big time, like 192 00:11:34,040 --> 00:11:36,800 Speaker 1: five hundred thousand shares. And I should really do a 193 00:11:36,800 --> 00:11:40,000 Speaker 1: full episode on Steve Huffman because he has an absolutely 194 00:11:40,120 --> 00:11:44,520 Speaker 1: terrible reputation among Reddit users for various reasons, and there 195 00:11:44,640 --> 00:11:47,520 Speaker 1: is a lot to go into. But more than that, 196 00:11:47,960 --> 00:11:52,520 Speaker 1: Reddit's COOO, Jennifer Wong, also disclosed selling more than five 197 00:11:52,640 --> 00:11:56,360 Speaker 1: hundred thousand of her shares in the company. And when 198 00:11:56,400 --> 00:12:00,000 Speaker 1: your corporate leaders are offloading stock, they'll keep in mind 199 00:12:00,160 --> 00:12:04,240 Speaker 1: both Huffman and Wong still own a butt load of shares. Well. 200 00:12:04,760 --> 00:12:07,920 Speaker 1: If the leaders are starting to sell off large numbers 201 00:12:07,960 --> 00:12:11,439 Speaker 1: of shares, that can really shake investor confidence. I mean 202 00:12:11,440 --> 00:12:13,880 Speaker 1: it seems to send the message of hey, if the 203 00:12:13,880 --> 00:12:19,280 Speaker 1: people in charge are offloading their stake in the company, 204 00:12:19,800 --> 00:12:24,679 Speaker 1: why am I staying in. So shares dropped down to 205 00:12:24,880 --> 00:12:27,920 Speaker 1: forty nine dollars thirty two cents per share by the 206 00:12:28,040 --> 00:12:31,959 Speaker 1: end of yesterday, which was below the closing price that 207 00:12:32,040 --> 00:12:36,080 Speaker 1: they traded at on opening day. Now, today is good Friday, 208 00:12:36,360 --> 00:12:39,160 Speaker 1: so the stock market is not open today and we'll 209 00:12:39,160 --> 00:12:41,839 Speaker 1: have to see next week where things go from there. 210 00:12:42,280 --> 00:12:45,920 Speaker 1: But over on Reddit there are a lot of armchair 211 00:12:46,520 --> 00:12:50,080 Speaker 1: stock analysts who are essentially saying told you so, or 212 00:12:50,120 --> 00:12:54,520 Speaker 1: they're saying this is just desserts. Mashables Matt Binder reports 213 00:12:54,520 --> 00:12:57,880 Speaker 1: on a study that was conducted by censor Tower, and 214 00:12:58,040 --> 00:13:02,040 Speaker 1: this study claims that x formerly known as Twitter, has 215 00:13:02,080 --> 00:13:06,720 Speaker 1: seen a sharp decline in daily mobile app user numbers 216 00:13:06,760 --> 00:13:10,839 Speaker 1: since Elon Musk took control back in November twenty twenty two. 217 00:13:10,880 --> 00:13:13,720 Speaker 1: And by sharp decline, I mean a twenty three percent 218 00:13:13,880 --> 00:13:17,880 Speaker 1: drop in app usage, so nearly a quarter of all 219 00:13:18,000 --> 00:13:21,320 Speaker 1: users have dropped off if you were looking at it 220 00:13:21,360 --> 00:13:25,120 Speaker 1: from that perspective, and that mobile app usage in general 221 00:13:25,360 --> 00:13:28,240 Speaker 1: is down eighteen percent year over year here in the 222 00:13:28,320 --> 00:13:32,520 Speaker 1: United States. So in this regard, X slash Twitter is 223 00:13:32,559 --> 00:13:35,880 Speaker 1: really leading the way among social platforms, because no other 224 00:13:35,960 --> 00:13:39,280 Speaker 1: social platform has seen that large of a decline over 225 00:13:39,320 --> 00:13:42,760 Speaker 1: that same amount of time. X has posted a rebuttal 226 00:13:43,080 --> 00:13:46,679 Speaker 1: to this report saying that sensor Tower doesn't have access 227 00:13:46,679 --> 00:13:50,120 Speaker 1: to all of X's data and thus the conclusions are wrong. 228 00:13:50,880 --> 00:13:54,360 Speaker 1: The reports I've seen have said that since Tower does 229 00:13:54,559 --> 00:13:59,760 Speaker 1: have access to the data about mobile apps but not website, 230 00:14:00,000 --> 00:14:03,000 Speaker 1: if you were to go through your browser on your 231 00:14:03,040 --> 00:14:05,880 Speaker 1: phone and go to X that way, they don't have 232 00:14:05,920 --> 00:14:08,319 Speaker 1: access to that information, but they do have access to 233 00:14:08,360 --> 00:14:11,920 Speaker 1: the information of people who are using the actual app 234 00:14:12,360 --> 00:14:17,000 Speaker 1: to access the service. And meanwhile, X is also not 235 00:14:17,080 --> 00:14:20,240 Speaker 1: providing any evidence to back up their own statement that 236 00:14:20,680 --> 00:14:23,400 Speaker 1: this is all off. So it's kind of a he said, 237 00:14:23,480 --> 00:14:26,920 Speaker 1: she said situation. At the moment, there's certainly been the 238 00:14:26,960 --> 00:14:31,160 Speaker 1: perception that usage on X has dropped, at least among 239 00:14:31,440 --> 00:14:36,960 Speaker 1: traditional users, and that the service has largely devolved into 240 00:14:37,240 --> 00:14:39,720 Speaker 1: a hive of scum and villainy. That may be my 241 00:14:39,800 --> 00:14:43,040 Speaker 1: own personal bias coming through. Actually, let's not say that. 242 00:14:43,080 --> 00:14:45,600 Speaker 1: Maybe that's my own personal bias coming through, and I 243 00:14:45,640 --> 00:14:49,040 Speaker 1: fully admit that, And honestly I can only anecdotally talk 244 00:14:49,080 --> 00:14:53,320 Speaker 1: about this because I got off Twitter last year. Anyway, 245 00:14:53,760 --> 00:14:56,240 Speaker 1: there's no denying the access encounter more than a few 246 00:14:56,320 --> 00:14:59,720 Speaker 1: roadbumps since Musk took over the company. Obviously, the excess 247 00:14:59,800 --> 00:15:04,760 Speaker 1: can continued to struggle with landing advertisers and partners in 248 00:15:04,800 --> 00:15:09,280 Speaker 1: the wake of some of the more controversial decisions. Aw 249 00:15:09,440 --> 00:15:13,720 Speaker 1: Olheiser has an article on vox dot com titled the 250 00:15:13,760 --> 00:15:16,920 Speaker 1: slow Death of Twitter is measured in disasters like the 251 00:15:16,960 --> 00:15:21,200 Speaker 1: Baltimore Bridge collapse, and it seems to provide some anecdotal 252 00:15:21,240 --> 00:15:25,600 Speaker 1: support for censer Tower's findings. So Olheiser argues that in 253 00:15:25,640 --> 00:15:29,200 Speaker 1: the good old days, breaking news would proliferate across Twitter 254 00:15:29,520 --> 00:15:32,200 Speaker 1: in real time, so folks would learn about world events 255 00:15:32,320 --> 00:15:36,320 Speaker 1: shortly after they happened, and news would spread incredibly far 256 00:15:36,360 --> 00:15:39,400 Speaker 1: and wide in just a matter of hours, if not minutes. 257 00:15:39,920 --> 00:15:45,280 Speaker 1: But Olheiser says something different happened when the cargo ship 258 00:15:45,520 --> 00:15:49,760 Speaker 1: hit the Francis Scott Key Bridge in Baltimore, Maryland, causing 259 00:15:49,800 --> 00:15:54,240 Speaker 1: that bridge to collapse. So rather than lots of messages 260 00:15:54,280 --> 00:15:58,640 Speaker 1: posted to X linking to articles about the disaster, Olheiser 261 00:15:58,640 --> 00:16:03,080 Speaker 1: says that X was bombarded with conspiracy theorists and folks 262 00:16:03,080 --> 00:16:06,920 Speaker 1: trying to capitalize on the tragedy through engagement baiting. And yeah, 263 00:16:07,040 --> 00:16:09,600 Speaker 1: that kind of stuff also happened in the good old 264 00:16:09,680 --> 00:16:13,320 Speaker 1: days of Twitter as well, Like that's not like unheard 265 00:16:13,320 --> 00:16:16,000 Speaker 1: of back then, But back then, all of that was 266 00:16:16,080 --> 00:16:20,120 Speaker 1: kind of a layer that was beneath the legitimate reporting 267 00:16:20,160 --> 00:16:24,360 Speaker 1: on world news that was proliferating across the platform. Now, 268 00:16:24,840 --> 00:16:28,800 Speaker 1: Olheiser says, this underlying layer has sort of been promoted 269 00:16:28,920 --> 00:16:33,320 Speaker 1: above everything else. It's dominating conversation, and beyond that things 270 00:16:33,360 --> 00:16:38,000 Speaker 1: got really ugly. Olheiser ends up quoting several Twitter accounts 271 00:16:38,080 --> 00:16:42,120 Speaker 1: that pushed outright racist narratives in relation to the disaster. 272 00:16:42,800 --> 00:16:45,800 Speaker 1: I do recommend reading the whole piece on box. It 273 00:16:46,040 --> 00:16:51,000 Speaker 1: is pretty compelling. Again, the title of the pieces, the 274 00:16:51,080 --> 00:16:54,160 Speaker 1: slow Death of Twitter is measured in disasters like the 275 00:16:54,240 --> 00:16:58,560 Speaker 1: Baltimore bridge collapse. Turning to video games, the publisher Take 276 00:16:58,640 --> 00:17:01,760 Speaker 1: two Interactive, which c really it's a holding company. It 277 00:17:01,840 --> 00:17:05,200 Speaker 1: owns a couple of other publishers and developers. They own 278 00:17:05,320 --> 00:17:08,600 Speaker 1: rock Star that's the company behind the Grand Theft auto franchise. 279 00:17:08,840 --> 00:17:11,600 Speaker 1: They also own two K, which is a publisher that 280 00:17:11,640 --> 00:17:16,960 Speaker 1: publishes a ton of different games, including like WWE games 281 00:17:16,680 --> 00:17:19,080 Speaker 1: that speak to my heart, although I haven't played one 282 00:17:19,119 --> 00:17:22,680 Speaker 1: in a few years anyway. They've acquired another video game company, 283 00:17:22,920 --> 00:17:25,800 Speaker 1: a historic one, a storied one. That is, they have 284 00:17:25,880 --> 00:17:31,760 Speaker 1: acquired Gearbox from Embracer Group. So Gearbox is the developer 285 00:17:31,840 --> 00:17:35,479 Speaker 1: behind the Borderlands series of video games, among other things. 286 00:17:36,000 --> 00:17:38,200 Speaker 1: Borderlands is going to have its own live action film 287 00:17:38,240 --> 00:17:40,679 Speaker 1: coming out in theaters later this year, so this timing 288 00:17:40,760 --> 00:17:43,560 Speaker 1: is kind of interesting. The deal was for four hundred 289 00:17:43,600 --> 00:17:48,720 Speaker 1: and sixty million smackaroos, which is a princely sum. It's 290 00:17:48,760 --> 00:17:52,320 Speaker 1: not a done deal yet, right, These acquisitions always have 291 00:17:52,400 --> 00:17:55,560 Speaker 1: to go through a process. The deal is set to 292 00:17:55,680 --> 00:18:01,240 Speaker 1: close in June, assuming that regulators don't object to the transaction. 293 00:18:01,800 --> 00:18:05,639 Speaker 1: Since Embracer Group is kind of like the Kirby of 294 00:18:05,760 --> 00:18:09,040 Speaker 1: video game companies out there, meaning it gobbles up pretty 295 00:18:09,080 --> 00:18:12,480 Speaker 1: much every company it sets its sights on, I think 296 00:18:12,560 --> 00:18:16,919 Speaker 1: regulators might be inclined to see a property, leave embracer 297 00:18:17,000 --> 00:18:22,199 Speaker 1: groups you know embrace and finally to cover a weird 298 00:18:22,320 --> 00:18:26,359 Speaker 1: story about butts and bosoms. Butts and bosoms sounds like 299 00:18:26,400 --> 00:18:28,680 Speaker 1: a raunchy role playing game when I say it that way, 300 00:18:28,720 --> 00:18:31,159 Speaker 1: but you know games are involved in this story. I 301 00:18:31,200 --> 00:18:34,760 Speaker 1: am talking about Twitch, the streaming service that started off 302 00:18:34,800 --> 00:18:37,760 Speaker 1: as a way for gamers to stream themselves playing games 303 00:18:37,800 --> 00:18:40,680 Speaker 1: live on the Internet to their adoring fans, and these 304 00:18:40,760 --> 00:18:43,000 Speaker 1: days Twitch does a lot more than that, but games 305 00:18:43,040 --> 00:18:45,880 Speaker 1: are still at the core of the servants, and recently 306 00:18:46,160 --> 00:18:48,760 Speaker 1: the company had to create a few new policies to 307 00:18:48,800 --> 00:18:53,400 Speaker 1: fight back over some innovations in the streaming space, namely 308 00:18:53,600 --> 00:18:57,680 Speaker 1: that certain Twitch streamers are wearing green or blue clothing 309 00:18:58,280 --> 00:19:03,120 Speaker 1: so that they can reject their gameplay on their bodies, 310 00:19:03,359 --> 00:19:08,119 Speaker 1: typically their butts or their chest. This was the case 311 00:19:08,160 --> 00:19:11,040 Speaker 1: with Twitch streamer morg Pi, who also came up with 312 00:19:11,080 --> 00:19:14,560 Speaker 1: a very clever work around where she created a green 313 00:19:15,359 --> 00:19:19,280 Speaker 1: shirt that had the chest region cut out of it, 314 00:19:19,640 --> 00:19:23,840 Speaker 1: so that when she keyed out the color, only her 315 00:19:23,920 --> 00:19:28,400 Speaker 1: head and chest showed up on screen as the player 316 00:19:28,920 --> 00:19:32,879 Speaker 1: view and everything else was showing the gameplay of the 317 00:19:32,880 --> 00:19:35,840 Speaker 1: game she was actually playing on stream, So Twitch is 318 00:19:35,920 --> 00:19:38,080 Speaker 1: now saying that this is against their policy and that 319 00:19:38,200 --> 00:19:42,080 Speaker 1: quote content that focuses on intimate body parts for a 320 00:19:42,119 --> 00:19:45,919 Speaker 1: prolonged period of time will not be allowed. Quote. Twitch 321 00:19:45,920 --> 00:19:49,000 Speaker 1: has had a very long history of trying to balance 322 00:19:49,040 --> 00:19:53,280 Speaker 1: out what is popular among users and streamers on the 323 00:19:53,320 --> 00:19:57,560 Speaker 1: service with what is popular among advertisers, and to that end, 324 00:19:57,600 --> 00:20:01,600 Speaker 1: the company has made several moves, sometimes humorous consequences, to 325 00:20:01,680 --> 00:20:05,160 Speaker 1: tamp down on the more salacious streamers who are out there. 326 00:20:05,400 --> 00:20:08,600 Speaker 1: What a world. All right, that's it for this week. 327 00:20:08,800 --> 00:20:11,440 Speaker 1: I hope all of you are well, and I'll talk 328 00:20:11,480 --> 00:20:21,920 Speaker 1: to you again really soon. Tech Stuff is an iHeartRadio production. 329 00:20:22,200 --> 00:20:27,240 Speaker 1: For more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts, 330 00:20:27,359 --> 00:20:29,320 Speaker 1: or wherever you listen to your favorite shows.