1 00:00:05,120 --> 00:00:06,560 Speaker 1: This is what the Flux. 2 00:00:06,640 --> 00:00:10,200 Speaker 2: I'm Brettan, I'm justin and it's Monday, the nineteenth of January. 3 00:00:10,240 --> 00:00:12,720 Speaker 1: Ah, juzzy boy. It is good to have had a break, 4 00:00:12,800 --> 00:00:15,440 Speaker 1: but even better to be back on this fine Monday 5 00:00:15,520 --> 00:00:18,599 Speaker 1: in January. Now, over the Christmas break, household spending jumped 6 00:00:18,680 --> 00:00:21,280 Speaker 1: to more than a two year high, would you believe? 7 00:00:21,280 --> 00:00:23,480 Speaker 1: And judge boy, given the IBA will be making its 8 00:00:23,520 --> 00:00:26,360 Speaker 1: first rate call in febru this year, and it sets 9 00:00:26,400 --> 00:00:29,360 Speaker 1: them up with some very confusing numbers. Is economy booming 10 00:00:29,440 --> 00:00:32,120 Speaker 1: because it's just booming? Or is this time of the 11 00:00:32,200 --> 00:00:34,839 Speaker 1: year with Black Friday and Christmas and holidays just not 12 00:00:34,920 --> 00:00:36,479 Speaker 1: a good time to get a pulse check on the 13 00:00:36,520 --> 00:00:38,920 Speaker 1: overall economy. Only time shall tell. 14 00:00:38,920 --> 00:00:42,080 Speaker 2: Only time shall tell. And then man Ossie Open kicked 15 00:00:42,120 --> 00:00:44,839 Speaker 2: off last night officially and there are some big serves 16 00:00:44,880 --> 00:00:47,280 Speaker 2: and even bigger payouts in this year's Ossie Open. In 17 00:00:47,320 --> 00:00:49,800 Speaker 2: the Flux app today we break down how much the 18 00:00:49,800 --> 00:00:52,360 Speaker 2: Ossi Open tennis players are actually getting paid in the 19 00:00:52,400 --> 00:00:54,600 Speaker 2: twenty twenty six twenty and let me tell you one thing. 20 00:00:54,680 --> 00:00:57,520 Speaker 2: It has jumped by sixteen percent this year, a very 21 00:00:57,520 --> 00:00:58,040 Speaker 2: big number. 22 00:00:58,160 --> 00:01:00,920 Speaker 1: Three generous stories today, Juzzy boy, let's do it for 23 00:01:01,120 --> 00:01:04,000 Speaker 1: our first. The company behind Bonds, Bras and Things and 24 00:01:04,040 --> 00:01:06,839 Speaker 1: Sheridan is up for sale after its new global owner 25 00:01:07,000 --> 00:01:08,280 Speaker 1: wants to clear the decks. 26 00:01:08,400 --> 00:01:11,240 Speaker 2: Talk about a full wardrobe clean out. Be man, including 27 00:01:11,280 --> 00:01:13,880 Speaker 2: the undies draw what is going on here? So Josey boy, 28 00:01:14,000 --> 00:01:14,200 Speaker 2: we know. 29 00:01:14,240 --> 00:01:17,200 Speaker 1: Bonds is one of Australia's most recognizable underwear brands. 30 00:01:17,040 --> 00:01:19,640 Speaker 2: Yep founder back in nineteen fifteen and has been promoted 31 00:01:19,640 --> 00:01:23,039 Speaker 2: by everyone from Michael Clark, Nick Kurios, te Mirandaker and 32 00:01:23,280 --> 00:01:24,200 Speaker 2: power Rafter and. 33 00:01:24,160 --> 00:01:27,680 Speaker 1: Who could forget Robert Irwin in his undies holding that 34 00:01:28,120 --> 00:01:28,760 Speaker 1: snake ye. 35 00:01:29,360 --> 00:01:32,119 Speaker 2: In the eighties, Bonds became part of Pacific Brands group 36 00:01:32,200 --> 00:01:34,400 Speaker 2: alongside Sheridan and Burley as well. 37 00:01:34,240 --> 00:01:37,480 Speaker 1: And Haines Brands. Well, it's the US based clothing giant 38 00:01:37,480 --> 00:01:40,600 Speaker 1: that grew a pretty significant portfolio of companies in Australia. 39 00:01:40,680 --> 00:01:43,640 Speaker 2: Yeah. It acquired the Pacific Brands portfolio of Bond, Sheridan 40 00:01:43,680 --> 00:01:46,320 Speaker 2: Burley back in twenty sixteen for one point one bill 41 00:01:46,560 --> 00:01:46,959 Speaker 2: Then it. 42 00:01:46,959 --> 00:01:49,680 Speaker 1: Acquired the Bras and Things retail stores in twenty eighteen 43 00:01:49,760 --> 00:01:51,120 Speaker 1: for five hundred mili and. 44 00:01:51,080 --> 00:01:53,800 Speaker 2: Together these brands were generating over one point one billion 45 00:01:53,880 --> 00:01:55,640 Speaker 2: dollars in annual revenue for Haines. 46 00:01:55,360 --> 00:01:58,040 Speaker 1: Brands but Juseboy. In August last year, Guilden Active were 47 00:01:58,080 --> 00:02:01,320 Speaker 1: acquired Haines Brands globally for two into billion US dollars, and. 48 00:02:01,240 --> 00:02:03,400 Speaker 2: As part of this, Guilden CEO said at the time 49 00:02:03,480 --> 00:02:06,880 Speaker 2: that the Australian portfolio could end in a quote sale 50 00:02:06,960 --> 00:02:08,359 Speaker 2: or other transaction. 51 00:02:07,960 --> 00:02:10,880 Speaker 1: And does We're given the Australian businesses slide in profit 52 00:02:10,919 --> 00:02:13,440 Speaker 1: from operations. The new owner has decided to put it 53 00:02:13,560 --> 00:02:14,680 Speaker 1: on the chopping block. 54 00:02:14,960 --> 00:02:16,359 Speaker 2: So what is the key learning here? 55 00:02:16,520 --> 00:02:20,760 Speaker 1: Acquirers rarely buy a business just to keep everything the same. 56 00:02:20,840 --> 00:02:21,040 Speaker 2: Yeah. 57 00:02:21,080 --> 00:02:23,359 Speaker 1: They often come with a clear vision of what they 58 00:02:23,440 --> 00:02:25,519 Speaker 1: want to own, a clear vision of what they want 59 00:02:25,560 --> 00:02:26,520 Speaker 1: to scale. 60 00:02:26,280 --> 00:02:28,680 Speaker 2: And a clear vision of what no longer fits. 61 00:02:28,800 --> 00:02:31,840 Speaker 1: Anything that doesn't align gets stopped down like Rob Irwin 62 00:02:32,200 --> 00:02:33,320 Speaker 1: or sold Fast and be Man. 63 00:02:33,360 --> 00:02:35,760 Speaker 2: With Haynes Brand's profit dropping by fifty percent over the 64 00:02:35,760 --> 00:02:38,120 Speaker 2: past twelve months, it would look like a pretty easy 65 00:02:38,160 --> 00:02:39,400 Speaker 2: cut for the global giant. 66 00:02:39,560 --> 00:02:43,200 Speaker 1: Yeap profit from ops fell from nearly ninety nine million 67 00:02:43,240 --> 00:02:45,000 Speaker 1: bucks to just forty two million bucks. 68 00:02:45,000 --> 00:02:47,160 Speaker 2: And be Man, We've seen this play out plenty of 69 00:02:47,240 --> 00:02:47,880 Speaker 2: times before. 70 00:02:48,000 --> 00:02:50,639 Speaker 1: Well, I remember When Disney acquired twenty first century Fox 71 00:02:50,639 --> 00:02:53,359 Speaker 1: in twenty nineteen for seventy one billion US dollars, do 72 00:02:53,440 --> 00:02:54,280 Speaker 1: you remember. 73 00:02:54,000 --> 00:02:57,120 Speaker 2: I do? Almost immediately after the deal closed, Disney carved 74 00:02:57,160 --> 00:02:59,280 Speaker 2: up the business based on what fit into its long 75 00:02:59,400 --> 00:03:01,760 Speaker 2: term vision, and that is likely what's happening here with 76 00:03:01,840 --> 00:03:02,600 Speaker 2: Guilden as well. 77 00:03:02,800 --> 00:03:06,000 Speaker 1: For our second story, Wikipedia has announced a new enterprise 78 00:03:06,040 --> 00:03:09,280 Speaker 1: partnership with not one, not two, but three big tech 79 00:03:09,280 --> 00:03:11,120 Speaker 1: companies to help train AI models. 80 00:03:11,160 --> 00:03:14,040 Speaker 2: Wikipedia didn't change be Man, but the world did, so 81 00:03:14,200 --> 00:03:15,360 Speaker 2: tell me what's going on here? So? 82 00:03:15,440 --> 00:03:19,440 Speaker 1: The Wikimedia Foundation is a not for profit organization that's 83 00:03:19,440 --> 00:03:20,760 Speaker 1: been around since two thousand and one. 84 00:03:20,800 --> 00:03:22,960 Speaker 2: And yes, you might have guessed it, these people are 85 00:03:23,000 --> 00:03:27,280 Speaker 2: the ones behind it. Wikipedia the world's largest publicly sourced encyclopedia. 86 00:03:27,360 --> 00:03:29,880 Speaker 1: Now, if you've ever fallen down a rabbit hole researching 87 00:03:29,880 --> 00:03:33,440 Speaker 1: your favorite athletes or origin story, chances are you've probably 88 00:03:33,480 --> 00:03:34,760 Speaker 1: ended up on a Wikipedia. 89 00:03:34,880 --> 00:03:37,760 Speaker 2: And today Wikipedia is a massive online library with over 90 00:03:37,840 --> 00:03:39,960 Speaker 2: sixty five million articles. 91 00:03:39,560 --> 00:03:42,320 Speaker 1: And Jesse, what here's the kicker. This free public data 92 00:03:42,320 --> 00:03:44,960 Speaker 1: has played a huge role in training AI models into 93 00:03:45,000 --> 00:03:45,720 Speaker 1: what we know today. 94 00:03:45,800 --> 00:03:48,120 Speaker 2: Except be Man. It's not actually free, is it? 95 00:03:48,400 --> 00:03:51,680 Speaker 1: Well? The rise of AI webscraping has put serious pressure 96 00:03:51,680 --> 00:03:52,800 Speaker 1: on Wikipedia service, and. 97 00:03:52,800 --> 00:03:55,720 Speaker 2: It's driven up costs for an organization that's been mainly 98 00:03:55,760 --> 00:03:56,960 Speaker 2: funded by donations. 99 00:03:57,080 --> 00:04:01,080 Speaker 1: So that's why Wikimedia has struck deals with major tech companies. 100 00:04:01,080 --> 00:04:04,120 Speaker 2: We're talking big shots like Microsoft and Meta and Amazon. 101 00:04:04,320 --> 00:04:06,680 Speaker 1: And what will these partnerships actually mean, Juzzy boy. 102 00:04:06,720 --> 00:04:09,360 Speaker 2: Well, it means they'll pay for use of Wikipedia's content 103 00:04:09,480 --> 00:04:11,080 Speaker 2: in their AI training models. 104 00:04:11,280 --> 00:04:14,000 Speaker 1: So if the AI giants are using it anyway, they 105 00:04:14,040 --> 00:04:15,839 Speaker 1: might as well help cover the bill. Am I right? 106 00:04:16,000 --> 00:04:17,400 Speaker 2: So what is the key learning here? 107 00:04:17,520 --> 00:04:20,360 Speaker 1: The AI era has dramatically increased the value of all 108 00:04:20,440 --> 00:04:21,320 Speaker 1: data and all. 109 00:04:21,200 --> 00:04:24,920 Speaker 2: Content, especially data that's seen as neutral and trustworthy A 110 00:04:25,160 --> 00:04:28,880 Speaker 2: man For years, data is something that companies collected almost accidentally. 111 00:04:28,920 --> 00:04:31,720 Speaker 1: But now with new AI models being launched left, right 112 00:04:31,760 --> 00:04:34,719 Speaker 1: and center, AI companies need large sources of data to 113 00:04:34,760 --> 00:04:37,880 Speaker 1: train their models to become smarter and faster than their competitors. 114 00:04:37,880 --> 00:04:40,520 Speaker 2: Seev Man, this has turned data into an asset class 115 00:04:40,560 --> 00:04:42,479 Speaker 2: that's actually monetizable, and I reckon. 116 00:04:42,520 --> 00:04:44,000 Speaker 1: We've seen this happen before, haven't we. 117 00:04:44,240 --> 00:04:47,039 Speaker 2: Just two years ago, Reddits struck a content licensing deal 118 00:04:47,080 --> 00:04:49,680 Speaker 2: with Google to allow its forums to be used to 119 00:04:49,720 --> 00:04:52,680 Speaker 2: help train Google's AI models, So the future of AI 120 00:04:52,920 --> 00:04:55,400 Speaker 2: won't be defined by who builds the fastest model, but 121 00:04:55,400 --> 00:04:57,880 Speaker 2: by who has access to the most meaningful data. 122 00:04:57,920 --> 00:05:01,280 Speaker 1: For our third and final story, Rio Tinto is in 123 00:05:01,480 --> 00:05:03,960 Speaker 1: talks to buy glen Core, a deal that could create 124 00:05:04,000 --> 00:05:08,039 Speaker 1: the world's biggest mining company, and has invested feeling very divided. 125 00:05:08,040 --> 00:05:10,640 Speaker 2: Big miners, big metals, and some big egos as well. 126 00:05:10,640 --> 00:05:12,000 Speaker 2: Be man, so tell me more so. 127 00:05:12,120 --> 00:05:15,080 Speaker 1: Rio Tinto is the heavyweight champ of iron ore mining, 128 00:05:15,240 --> 00:05:16,279 Speaker 1: the largest in the world. 129 00:05:16,400 --> 00:05:19,400 Speaker 2: We'd be talking the production of iron ore and copper, aluminium, 130 00:05:19,400 --> 00:05:21,520 Speaker 2: critical minerals, and a heap of other materials too. 131 00:05:21,560 --> 00:05:23,239 Speaker 1: It's worth more than two hundred billion dollars. 132 00:05:23,279 --> 00:05:25,880 Speaker 2: And glen Cole they're the Anglo Swiss company that does 133 00:05:25,920 --> 00:05:27,880 Speaker 2: both commodity training and mining. 134 00:05:27,960 --> 00:05:31,760 Speaker 1: We're talking coal, copper, your Zinx, your Nickels. 135 00:05:32,000 --> 00:05:34,400 Speaker 2: And last week b Man, the two companies confirmed they're 136 00:05:34,440 --> 00:05:37,320 Speaker 2: in early stage talks about a potential merger. 137 00:05:37,440 --> 00:05:40,479 Speaker 1: Now, if it happens, you're looking at one monster miner 138 00:05:40,600 --> 00:05:42,320 Speaker 1: worth more than three hundred billion dollars. 139 00:05:42,400 --> 00:05:44,520 Speaker 2: And yes, b Man, I see your mind spinning, because 140 00:05:44,560 --> 00:05:47,440 Speaker 2: this is the third time Rio and Glenkell have flirted 141 00:05:47,480 --> 00:05:48,440 Speaker 2: with a merger. 142 00:05:48,240 --> 00:05:50,880 Speaker 1: But this time this seems to look a little different. 143 00:05:50,880 --> 00:05:54,280 Speaker 1: Maybe it's because of new Rio Tinto management, or maybe 144 00:05:54,480 --> 00:05:57,720 Speaker 1: it's because of a renewed obsession with a very old mineral. 145 00:05:58,040 --> 00:06:00,200 Speaker 1: Fascinating insight, Jesse Boyce, So what's the keyl. 146 00:06:00,560 --> 00:06:02,680 Speaker 2: Copper is having its moment in the sun. 147 00:06:02,800 --> 00:06:04,880 Speaker 1: And let me tell you one thing. It's not because 148 00:06:04,920 --> 00:06:06,320 Speaker 1: it looks nice in wiring. 149 00:06:06,680 --> 00:06:10,080 Speaker 2: No, no, no. Copper has become essential for many future 150 00:06:10,120 --> 00:06:11,800 Speaker 2: facing industries and technology. 151 00:06:11,920 --> 00:06:15,720 Speaker 1: Think electric vehicles, renewable energy grids, data centers, have you heard. 152 00:06:15,640 --> 00:06:18,360 Speaker 2: Of those I have? And anything I related that obviously 153 00:06:18,400 --> 00:06:19,440 Speaker 2: guzzles power as well. 154 00:06:19,440 --> 00:06:22,760 Speaker 1: In fact, dosyboy. According to industry forecasts, global copper demand 155 00:06:22,800 --> 00:06:25,880 Speaker 1: could rise around fifty percent by twenty forty and. 156 00:06:25,920 --> 00:06:29,440 Speaker 2: Supply is expected to fall well short. For companies like 157 00:06:29,520 --> 00:06:33,239 Speaker 2: Rio Tinto, copper offers growth potential that iron ore and coal, 158 00:06:33,279 --> 00:06:35,760 Speaker 2: which they specialize in, just doesn't offer anymore. 159 00:06:35,880 --> 00:06:39,040 Speaker 1: And hello, Glencore has a whole lot of copper. 160 00:06:38,800 --> 00:06:40,960 Speaker 2: Minds, Foxdamm. Did you know the winners of the male 161 00:06:41,040 --> 00:06:43,720 Speaker 2: and female tournaments in the Aussie Open will both earn 162 00:06:43,800 --> 00:06:47,000 Speaker 2: over four million buccernies each in the flux app. Today 163 00:06:47,040 --> 00:06:49,240 Speaker 2: we take a deep dive into the prize money for 164 00:06:49,320 --> 00:06:52,600 Speaker 2: all Australian Open players, as well as investment the Victoria 165 00:06:52,720 --> 00:06:54,359 Speaker 2: is making in this huge tournament. 166 00:06:54,440 --> 00:06:57,200 Speaker 1: Thanks for listening and we'll see you on Wednesday.