New & existing home sales are positively correlated, but new sales often lead as builders adjust faster to demand/rates/incentives. Mar 2026: New 682k SAAR (+7.4% MoM); Existing 3.98M SAAR (-3.6% MoM). Leading: New sales. Mortgage rates (~6.4%) suppress both; lumber prices raise new build costs; gold is indirect (inflation hedge).

Nvidia’s new sales chief, Nick Parker, has to sell more than chips as Google, Amazon and OpenAI race to develop AI hardware of their own. Those are among 14 developments we’re watching across AI, chips and Big Tech this fall. Full story:

Nintendo shared new sales numbers for Both Pokémon Pokopia and Pokémon FireRed and LeafGreen have sold over 4 million copies worldwide. Pokémon Pokopia reached 4 million sales in about five weeks. Pokémon FireRed and LeafGreen are old games from 2004. Nintendo released new versions for the original Switch Each costs $19.99. Together they sold 4 million copies in about six weeks

Nottingham-based @BaxterFreight launches recruitment drive for 14 new sales account managers, planning to increase its turnover from £13m to £18m in 2018

U.S. is producing more cheese than ever to meet the global appetite for protein-rich by-product whey, leading dairies in the top cheesemaking state of Wisconsin to seek new sales opportunities in Asia. - Nikkei

AI unlocks insane product velocity But so much of it is being wasted. Startups need to spend more time: 1. New sales pitch training - operationalizing how that leads to changes in the sales pitch + training 2. On Acquisition Funnels - the way you’re AB testing marketing funnels to acquire customers 3. Upsell motion - identifying signals in the data to prioritize customers for upsell and getting your teams upsell motion nailed 4. Product marketing - how you’re doing product marketing on a per user basis to drive more engagement and retention And many more

Crimson Desert maker Pearl Abyss is looking into DLC and other ways to grow the open-world action game. “Crimson Desert will continue to focus on enhancing user satisfaction and driving new sales through continuous updates while expanding its market presence through platform expansion. In addition, we are currently exploring various ways to broaden the game to the next level, including DLC, and we will share the details once the concrete plans are set.” This is a change from what they said in March 2026, when CEO Heo Jin-young said there were no plans yet for paid DLC and the focus was on free updates to sell more copies of the main game. This comes after the game had a strong launch that helped Pearl Abyss earn a record 180 million dollars in one quarter

Yesterday we hosted the “Whose stablecoin, whose rails” event by the lake in Zurich. The event was very oversubscribed, more than 50 bankers, investors, founders and executives came to meet the Rayls team. These events are important because it allows us to show to these decision makers that Rayls is a solid platform with a serious team, capable of delivering institutional-grade solutions. We left with several new sales leads and potential partnerships. We had guests from every major financial institution attending the Point Zero conference, and I personally spent all my time talking to many of them, answering questions and showing how Rayls can be used by their companies.

Interesting call on the Google - $RDDT partnership with a former lead at Google, as well as why AI talent is leaving $GOOGL The read is that Reddit's negotiating position has deteriorated materially His framing is that the original agreement was a function of where large language models sat at that specific moment. Bard was becoming Gemini, hallucination rates were high, and the model had no grounding in current events. Training produces a model that is effectively six months stale by the time it ships. Reddit solved a narrow, acute problem: real-time human commentary at breadth and depth. The competitive set for that data was thin. X, Meta's properties and Threads are walled gardens aligned with rival frontier labs and were never gettable. Reddit was the one large corpus of unfiltered human language actually available for purchase, which is why both Google and OpenAI ended up there. The price reflects how little this mattered to Google's P&L. Roughly $60m: "For Google, $60 million to buy specific data is not a lot of money." His view is that the cash was the least interesting component. The valuable consideration was prompt data flowing back—what the user asked, and what they asked that led to a click through to Reddit. Both sides of the intent coin. He connects this to the trajectory of Reddit's advertising business, which has scaled well beyond what a new sales team and new infrastructure alone would explain. On the widely discussed point that AI Overviews traffic converts poorly, he broadly accepts it but argues the second-order effect dominates. Reddit held primacy in the citation slot, so volume was high even if quality was low, and the learning from that volume compounded. Google has since connected its search index and corpora more directly to the model layer, so the original grounding gap has largely closed. YouTube citation share has overtaken Reddit. Google News, Merchant Center and Places cover most of what Reddit was a shortcut to. His read on Reddit publicly signaling it might walk is that this is negotiation conducted through the press, and that it implies Google came back with worse terms—likely stripping preferences and the prompt data return rather than simply cutting the number. Google's standard posture on data rights is full and unrestricted use, and carve-outs on usage are not how Google contracts. "They probably don't need it. They probably want it." He expects a deal—the relationship is warm and mutually beneficial—but on terms that reset Reddit's expectations. Money is the secondary variable. The variable that matters to $RDDT holders is whether prompt-level signal keeps flowing. Asked whether Google would simply take the data if talks collapse, he says no on cultural grounds, that it is not in the corporate DNA to do that after the fact. More interesting is his description of how frontier labs behave when sued over training data: they do not settle, because a settlement establishes a price and invites every other rights holder. They litigate, spend, and drag it to a quiet resolution specifically to avoid setting precedent. Independent of the Google relationship, he identifies the harder issue: Reddit sits in the middle of a considered purchase journey with nothing to sell at the end of it. A user researches a bike on Reddit and buys it somewhere else. Two steps, and the second one is where the margin lives. The old funnel involved ten websites and fifty data points before purchase. That discovery phase is now collapsing into the chat interface, and the losers are the intermediaries that monetized the journey rather than the transaction. This is a Gemini problem, a Claude problem and an OpenAI problem simultaneously, not a Google-specific one. Search advertising worked because the system was deterministic—a tree you navigate from trunk to branch to leaf, ending in a transaction. Token predictors give wildly different answers to marginally different prompts, and the labs do not fully understand their own models' behavior post-training. Tuning that for advertiser ROAS is closer to dark arts than to keyword auction mechanics. The deeper constraint is grounding. Merchant Center is the largest product data repository in the world, Places the largest inventory of shops and locations, and every flight and hotel has been tuned within an inch of its life because advertisers were trained over two decades to feed that system. OpenAI has none of it. He is measured on the disruption question rather than dismissive. He cites Walmart attributing roughly 20% of traffic to OpenAI as evidence the relay is real, and he sees a credible alternative path: merchants exposing their own data through open interfaces that models come and fetch, rather than piping it into Merchant Center. That inverts the current architecture, but it needs an ROI case to bootstrap and there is a chicken-and-egg problem. His conclusion is share erosion and ad revenue siphoning, not collapse. On the suggestion that OpenAI should just acquire an ad tech engine, he is dismissive for the right reason: buying keyword-era infrastructure is buying an internal combustion engine in an electric vehicle world. A paragraph-long voice prompt carries vastly more intent than a three-word query, and the extraction method has to be built for that, not retrofitted. "Google has innovator's dilemma on steroids." A USD 250bn high-margin advertising business, powered by data that advertisers were trained to supply, cannot be hard-switched to Gemini. The chosen path is to infiltrate Search with AI and tolerate a messy middle until ROAS re-stabilizes on the new medium. He is candid that the current state is worse than Search at its peak, and he references the reported history of deliberately degrading search ad quality to increase monetization as evidence that the profit motive is explicit. On Google execution speed - Every objective must ladder from the most junior contributor up through director and VP. Search sits at the top of the tree, Android just behind, peripheral products have no influence. Strategy gets negotiated between silos, which is slow. "innovation is by definition outside of that data structure." Work outside the ladder is unsanctioned, and unsanctioned work costs you promotions and raises. He extends this to DeepMind, which he believes is now materially less isolated than it was and is being pulled into commercial delivery, and offers that as the explanation for researcher attrition to Anthropic and OpenAI. Not compensation—loss of research autonomy.