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New Jersey moves to slash AI data center tax breaks and reroute money to power relief - Yahoo

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This week, SpaceX revealed plans for its ambitious semiconductor manufacturing facility, which is set to become the largest building on the planet at over 100 million square feet. The proposed site will be located in Grimes, Texas, just outside of Houston.

Elon Musk, the owner, shared on X that "Terafab Texas will be the largest and most valuable building on Earth by far," and he described the design as "stunningly beautiful."

The new facility will be developed in collaboration with Musk's electric vehicle brand, Tesla. According to SpaceX, "This facility will house the manufacturing, packaging and testing of advanced logic and memory devices." The Terafab plant aims to produce chips tailored for edge computing and inference, which will be utilized in devices such as Tesla’s Optimus robots and self-driving Cybercabs, as well as high-powered chips for SpaceX’s space-based data centers.

Despite the ongoing volatility in the market, experts are optimistic about SpaceX's future.

SpaceX has noted that the combined demand for chips by both SpaceX and Tesla is projected to exceed global supply levels. The company emphasized, "While we are deeply appreciative of our current chip suppliers and encourage them to expand production whenever possible, this looming gulf between supply and demand is at the core of Terafab’s necessity."

The establishment of Terafab is expected to create over 3,000 jobs and will have an initial investment requirement estimated at around $16.8 billion.

Governor Greg Abbott of Texas has recently put a hold on new data center initiatives pending a statewide audit of the electrical grid.

Musk highlighted the benefits of the new plant, stating, "Terafab is bringing cutting-edge manufacturing to America, creating thousands of high-paying jobs in the Lone Star State and enabling us to produce AI chips at scale for use on Earth and in space."

SpaceX's initial investment proposal for Terafab was set at $55 billion, with potential growth to $119 billion as outlined in a May filing.

In April, Tesla also began construction on a research facility at the North Campus of its Giga Texas plant, which serves as a precursor to the Terafab project.

Currently, the Pentagon holds the title for the largest building in the U.S., measuring 6.6 million square feet, while the world's largest building is the New Century Global Center in Chengdu, China, spanning 18.9 million square feet.

SpaceX and Tesla select Texas for the world's largest AI chip manufacturing facility.

A sunlit Texas plain at golden hour, dominated by a sprawling new facility—the world’s largest building—its left wing boldly bearing the red TESLA logo, its right wing the sleek black SpaceX insignia. In the foreground, two engineers in hard hats pore over blueprints on a rocky outcrop. Behind them, wind turbines and distant oil derricks under a clear sky underscore the Lone Star setting.

SpaceX and Tesla choose Texas for AI chip manufacturing plant that will be world's largest building - Fox Business

- SpaceX's semiconductor plant, Terafab Texas, will exceed 100 million square feet in size.  
- Terafab aims to produce chips for Tesla's robots and SpaceX's data centers.  
- The facility will create over 3,000 jobs and cost around $16.8 billion initially.  

SpaceX announced this week that its planned semiconductor plant that is expected to become the largest building in the world at more than 100 million square feet will be built in Grimes, Texas, outside of Houston.

"Terafab Texas will be the largest and most valuable building on Earth by far," owner Elon Musk wrote X Thursday. "And it will be stunningly beautiful."

The factory is a joint effort with Musk’s electric car company, Tesla.

"This facility will house the manufacturing, packaging and testing of advanced logic and memory devices," SpaceX said in a press release Thursday. "Terafab will produce chips optimized for edge computing and inference for use in hardware like Tesla’s Optimus robots and self-driving Cybercabs, along with high-power chips designed for operating SpaceX’s space-based data centers."

MARKET EXPERT MAKES CASE FOR SPACEX DESPITE VOLATILITY

SpaceX added that its and Tesla’s combined demand for chips is expected to be in excess of the global supply.

"While we are deeply appreciative of our current chip suppliers and encourage them to expand production whenever possible, this looming gulf between supply and demand is at the core of Terafab’s necessity," SpaceX explained.

The company said Terafab would employ more than 3,000 people, adding that its initial phase is estimated to cost approximately $16.8 billion.

TEXAS GOV. GREG ABBOTT EFFECTIVELY PAUSES NEW DATA CENTER PROJECTS PENDING STATEWIDE GRID AUDIT

"The Terafab is bringing cutting-edge manufacturing to America, creating thousands of high-paying jobs in the Lone Star State and enabling us to produce AI chips at scale for use on Earth and in space," Musk said.

SpaceX proposed initially investing $55 billion into Terafab, which could increase to $119 billion, according to a May filing.

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In April, Tesla broke ​ground on a research facility at the North ​Campus of ⁠its Giga Texas plant, which it called a precursor to Terafab.

The Pentagon is the current largest building in the U.S. at 6.6. million square feet, and China’s New Century Global Center in Chengdu, is the world's largest at 18.9 million square feet.

Reuters contributed to this report.

SpaceX and Tesla choose Texas for AI chip manufacturing plant that will be world's largest building

A parent from Omaha is expressing his apprehensions regarding the integration of artificial intelligence (AI) in educational settings. Nathan Miller, a tech consulting CEO with extensive experience in AI, is also a parent of a student at Westside Middle School and is worried about the implications of using AI in classrooms.

Miller has observed concerning trends among adults, such as cognitive offloading and potential mental health issues. This prompted him to address his worries in an open letter to the Westside School District, referencing sources like a Harvard Business Review article discussing "AI Brain Fry," which highlights how mental exhaustion from AI can lead to disengagement in learners.

In response to Miller's concerns, Westside School District arranged a meeting with him, which he described as a positive step toward ongoing dialogue. He believes that maintaining this conversation is crucial, especially as he notes that issues affecting adults inevitably trickle down to children.

While Miller acknowledges that AI cannot be completely ignored, he advocates for its introduction through the lens of media literacy. He emphasizes the need for students to understand how these tools function, their potential benefits, and the risks associated with their use.

Miller encourages fellow parents to engage with their school districts and deepen their own understanding of AI's role in education. As several school districts gear up to implement AI this academic year, Westside District provided a statement clarifying that use of AI is not mandatory for students or staff. However, the technology has become increasingly prevalent in daily life, showing up in various applications and search engines.

The district noted that while AI can be a valuable resource—helping students grasp complex concepts and generating ideas—it should not replace personalized thinking. Their goal is to guide students and staff in using AI responsibly, discussing appropriate contexts for its use, and reminding them that while AI can appear confident, it can also provide incorrect information. They encourage students to view AI as a supplementary tool rather than a substitute for critical thinking.

To support focused learning, Westside has implemented restrictions, allowing only educationally endorsed tools on student devices and blocking non-approved applications like ChatGPT. For more information about the district's approach to AI, interested parties can visit the dedicated AI webpage.

Omaha parent and tech consulting CEO expresses worries about AI in education.

A dusky, red-brick Omaha school façade looms behind two adults standing firm in the empty courtyard. On the left, a concerned parent in casual clothes holds up a smartphone screen glowing with scrolling AI code; on the right, a sharp-suited tech consulting CEO grips a tablet displaying a brain-shaped circuit diagram. Chalked binary numbers and a large question mark dot the concrete beneath their feet. Moody natural twilight casts long shadows, muted earthy tones, and a single amber streetlamp adds a warm counterpoint to the tension.

'My No. 1 thought is one of concern': Omaha parent, tech consulting CEO voices concerns on AI in classrooms - KETV

- Omaha parent Nathan Miller expresses concerns about AI's impact on classroom learning.  
- He cites potential cognitive offloading and mental health issues related to AI use.  
- Miller advocates for media literacy to help students understand AI's benefits and risks.  

'My No. 1 thought is one of concern': Omaha parent, tech consulting CEO voices concerns on AI in classrooms

An Omaha parent is speaking out with his concerns regarding AI use in the classroom.

RELATED COVERAGE: Nebraska Department of Education speaks on AI policies

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Nathan Miller works with AI often in his career as CEO of a tech consulting company. He's also a parent of a Westside middle schooler.

"My number one thought (of AI in school) is one of concern," Miller said.

Westside, along with several other districts such as Millard and Lincoln Public Schools, is using AI in certain curricula, and that worries Miller.

RELATED COVERAGE: How Westside Community Schools plans to use AI in curriculum

"I've observed among adults, among professional adults, a number of trends. One is in the space of cognitive offloading. Also a mental health concern," Miller said.

Miller sent an open letter of his concerns to the district, citing several sources, including a Harvard Business Review explaining "AI Brain Fry," the idea that the mental fatigue included with AI use can create disengagement.

Miller said the Westside District quickly responded, and they had an in-person meeting.

"I felt it was a good start to a conversation," Miller said.

Keeping a conversation going is what Miller feels is key.

"As a parent, I'm looking at all of these things that I'm seeing in my professional work that's impacting adults. If it's impacting adults, you can be sure that it's going to impact younger kids," Miller said.

Miller agrees that avoiding AI altogether isn't the solution. He feels it should instead be introduced through a media literacy lens.

"Helping a kid understand what the tool is, what these platforms are, how they work, where they might be helpful, but also where they might be dangerous or unhelpful or unhealthy," Miller said.

Miller said he encourages other parents to start conversations with their district and community, and to make sure they have a good understanding of AI themselves.

Several school districts plan to incorporate AI this school year. In its response to this story, Westside School District said the following:

"Westside Community Schools does not require any students or staff to use Artificial intelligence.

However, AI is quickly becoming part of many students and school staff's everyday life, showing up in search engines, chatbots, and the apps they already use. AI can be a genuinely useful tool: it can explain a tricky math concept, brainstorm ideas for a project, or help a student get unstuck when they're staring at a blank page. AI should not be a replacement for personalized thinking.

Our goal is to help interested students and staff use AI ethically and efficiently. That means talking about when it's appropriate to use (checking their work versus writing the whole assignment), reminding them that AI can sound confident while being wrong, and encouraging them to treat it as a starting point they still question and build on. The most powerful thing you can do is stay curious and keep the conversation open: ask what tools they're using, try them out together, and help them build the judgment to use AI as a support for their own learning rather than a shortcut around it.

To assist our students and teachers, we’ve put tools in place to help student focus. Only recommended, educational focused tools are allowed on student devices. Non-approved tools like ChatGPT, Grok and Perplexity are now blocked on student devices K-12.

You can read more about AI at Westside on our district's AI website: https://teachers.technology/ai-at-westside/"

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Omaha parent, tech consulting CEO voices concerns on AI in school

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YouTube star Hank Green sets new AI policy for himself — and urges other content creators to do the same - Yahoo

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In a recent video posted on Friday, well-known internet creator Hank Green expressed his need to reevaluate his engagement with AI technologies. He admitted to becoming “too reliant on [LLMs] for advice, for editing, and for sourcing,” and vowed to establish a personal “AI policy.” Green also urged his fellow creators to consider adopting similar guidelines.

His comments came after viewers pointed out that one of his videos had utilized an AI-generated script. Just last week, Green acknowledged having employed AI to help him find research papers and resources for various topics, apologizing for not being fully transparent about this process. He announced that he would “readjust” his strategy moving forward, indicating a shift towards producing fewer projects overall.

This is significant given Green's reputation for prolific content creation. He has developed the popular Focus Friend app, co-launched the educational YouTube channel Crash Course, invented 2D glasses, and authored two bestselling novels. Over the last two decades, he has achieved more than many do in a lifetime.

As someone who has followed his YouTube content weekly since 2012, I can attest to Green’s dedication. He uploads to his Vlogbrothers channel every Friday, even when on vacation. His resilience is evident; after a battle with Hodgkin's lymphoma, he humorously detailed his journey in a comedy special shortly after announcing his remission.

However, in light of the AI discourse, Green is pulling back. Instead of a traditional video post last Friday, he opted for an extensive Reddit entry in which he remarked on the unhealthy dopamine rush he experienced from constantly engaging with LLMs. He noted that the pursuit of doing more and more is detrimental both to his well-being and to society, indicating a disconnect from his audience.

In a now-deleted tweet discussing the contentious video, he acknowledged the pressure he was under, admitting, “I did use ChatGPT for research on this script and watching it I definitely get an AI feel.”

I don’t believe Green intended to mislead his audience with the use of generative AI; rather, he viewed it as a tool to manage his many creative ideas and projects. He certainly isn’t alone in this approach—AI-generated content is increasingly prevalent due to its immediate, almost miraculous results. Under tight deadlines, it’s tempting to lean on AI, especially when it appears to be a common practice among peers.

Green likely felt overwhelmed by his myriad projects, and AI offered a glimpse of how he could enhance his productivity. While there are instances where AI can genuinely assist, I remain cautious about the notion that it can boost our output and efficiency without sacrificing quality in some way.

AI-generated content often lacks the personal touch that makes our thoughts distinct. While prompt creators wield some control in shaping the output, the end result remains a product of machinery. When we delegate our thinking to algorithms, we risk losing the unique essence of our expressions.

I’m more interested in Hank Green’s authentic insights than in anything produced by LLMs. Even if he states he “did not use AI to write or fact-check,” the proximity of his video to AI involvement raises concerns.

I commend him for taking the time to reconsider this issue. In a world increasingly dominated by AI that promises enhanced productivity and endless content, choosing to slow down can feel counterproductive. Yet, sometimes, it requires greater effort to do less effectively—and to do it well.

Hank Green is developing a personal AI policy and believes other creators should do the same.

A sunlit home office scene: Hank Green seated at a wooden desk piled with draft pages titled “AI Policy,” pen in hand as he thoughtfully sketches bullet points and simple flowcharts. A subtle, soft-glow abstract AI-brain icon hovers above the notebook. Behind him, warm-toned bookshelves filled with science and writing volumes fade into gentle focus. Natural morning light streams through a window, casting long shadows and highlighting his glasses and cozy sweater—evoking a moment of deliberate creativity as he forges a personal AI policy and inspires other creators.

Hank Green Is Creating ‘an AI Policy for Himself.’ He Thinks Other Creators Should, Too - CNET

- Hank Green admitted becoming too reliant on AI for advice, editing, and sourcing.  
- He plans to establish an AI policy and reduce his project workload.  
- Green emphasized the need for genuine human thought over AI-generated content.  

In a video uploaded Friday, prolific internet creator Hank Green said he had taken time to reconsider his use of AI, acknowledging that he had become “too reliant on [LLMs] for advice, for editing and for sourcing.” He pledged to create an “AI policy for himself,” and encouraged other creators to take a similar approach.

These remarks follow viewers’ claims that one of Green’s videos contained an AI-generated script. Last week, the YouTuber said he had used AI “to locate papers and other resources for learning about topics” and apologized for not being transparent. He said he would “readjust” and scale back his projects. In other words, make fewer things.

This is a big deal, because Hank Green is widely known for making things. He’s built the popular app Focus Friend, co-created the educational YouTube channel Crash Course, invented 2D glasses and written two New York Times bestselling novels. Green has done more in the last 20 years than most people could in 200.

I’ve watched his YouTube videos almost every week since 2012. Green publishes to his Vlogbrothers channel every Friday, even when he’s on vacation. (In June, he posted a video titled “No Video Today!!” that was, in fact, a video.) Nothing, not even cancer, seems able to stop him – within months of announcing he was in remission, he performed a comedy special about his experience with Hodgkin’s lymphoma.

But now, because of generative AI, Green is pumping the brakes.

Instead of uploading a video last Friday, he wrote a lengthy Reddit post, stating, “the level of dopamine I’ve been getting from interacting with LLMs … with doing more and more and more and more … is not healthy for me or good for the world. It is careless and has disconnected me from where people are on this.”

In a now-deleted tweet, Green commented on the video that sparked the controversy, “I produced this under a ton of pressure … I did use ChatGPT for research on this script and watching it I definitely get an AI feel.”

I don’t think Green used generative AI with the intention of deceiving his audience. I think he used it because it was a tool to help him keep up with the pace of his own ideas and ambitions. And I don’t think he’s the only one guilty of doing this.

AI-generated content is everywhere. It provides instant results that can feel magical. Under the pressure of deadlines, it might make sense to resort to using AI, especially if it seems like everyone else is doing it.

I imagine Green was caught up in too many projects (Here’s a list of most of them, provided by his brother and frequent collaborator, John) and saw the potential of AI to help him be more productive.

There are some cases when AI can be genuinely helpful. However, I’m wary of claims that AI will increase our output and save us time, without costing us something else.

What we generate with AI tends to lack intention. It’s true that users of ChatGPT or Claude will make choices in the process of creating a prompt, and those prompts can be continually refined. But the resulting AI-generated images or writing ultimately comes from a machine. And when we outsource our thinking to machines, we lose what makes our thoughts unique in the first place.

I’m interested in what Hank Green personally has to say – not what his LLMs generate. Even if he “did not use AI to write or fact-check”, there’s something about his video’s proximity to ChatGPT that rubs me the wrong way.

I applaud him for stopping to reflect on the situation. With AI promising us more productivity and more content and more everything, it often feels counterintuitive to slow down. Sometimes, it takes more effort to do less — and do better.

Hank Green Is Creating ‘an AI Policy for Himself.’ He Thinks Other Creators Should, Too

This past week, the software as a service (SaaS) sector experienced a tumultuous ride, with investor sentiment shifting dramatically. After the low-cost acquisition of Airtable, previously valued at nearly $12 billion, by Bending Spoons for under $1.3 billion, concerns were heightened among investors regarding the potential impact of artificial intelligence on traditional software products. "I do believe companies will increasingly harness these coding agents to enhance their offerings, potentially affecting renewal rates," mentioned Matt Hedberg, a software analyst at RBC Capital Markets. However, by the end of the week, Wall Street showcased a remaining vigor within the industry, which not long ago was perceived as a key driver of technological progress and commanded high market valuations.

Both Twilio, specializing in cloud-based communications, and Atlassian, known for its collaboration tools, saw substantial gains of over 20% after releasing quarterly earnings. Cloudflare, dedicated to safeguarding websites from cyber threats, also rose by 5.6%. Box CEO Aaron Levie, a proponent of cloud software for over 20 years, expressed admiration for Atlassian, which reported one of its most profitable quarters since 2021. "Huge Atlassian quarterly beat," Levie tweeted, suggesting the previous pessimism about the impact of coding agents on specific software sectors may have been misplaced.

His reaction highlights a nuanced perspective that is often lost amidst broader market sell-offs. Salesforce's CEO Marc Benioff has spent months assuring investors that his company's established software solutions for sales and marketing would not be overtaken by emerging technologies. Despite Salesforce witnessing a 40% decline in its value since late 2024, driven by concerns over AI tools like OpenAI's Codex, it has shown consistent revenue growth and maintained stable profit margins.

The iShares Expanded Tech-Software Sector ETF faced its worst quarter since 2008, plummeting 24%, but it has since rebounded, currently only showing a 3% dip year-to-date, while the Nasdaq has risen by 15%. According to RBC's Hedberg, the first quarter was marked by such negative sentiment that many clients avoided discussions about software altogether, leading to challenges for venture-backed companies that had previously raised funds at high valuations in what is now dubbed the pre-AI era.

The continued lack of significant SaaS IPOs in 2026, coupled with a staggering 86% of private deal value going to AI firms in the first half of the year, amplifies these concerns. The situation intensified on Tuesday with the announcement of Airtable's acquisition, highlighting the shifting landscape for once-promising startups.

Shortly after the Airtable news, HubSpot faced its most significant market drop in ten years, plummeting 19% and marking a total loss of over 50% for the year. Datadog, a player in software monitoring, also faced a significant decline of 19%, its most pronounced fall since going public in 2019, although it still boasts a 72% yearly gain. The reduction in usage from its largest AI client, widely believed to be OpenAI, has sparked additional worries. "These moments remind us that we are still facing challenges," said Hedberg, noting the turbulent nature of the current market.

Positive sentiment returned on Thursday evening after encouraging quarterly reports from Atlassian, Twilio, and Cloudflare. Atlassian's CEO Mike Cannon-Brookes acknowledged the contributions of their R&D teams, which worked diligently to offer optimal quality at competitive prices. This announcement came shortly after the company laid off 10% of its workforce, a move Cannon-Brookes deemed essential for reinvestment into AI and enterprise sales while strengthening financial health. Atlassian experienced a remarkable 35% increase on Friday, marking its best performance since it went public in 2015. Despite this surge, the company remains down 8% for the year after a significant drop in 2025.

RBC analyst Rishi Jaluria suggested that some of Atlassian's stock surge may be due to short covering, where investors who had bet against the stock are now scrambling to buy shares to cover their positions. "The consensus was that Atlassian would struggle against AI tools like Claude Code and OpenAI Codex, so proving those predictions wrong could lead to significant gains," Jaluria stated, highlighting the ongoing unpredictability of the software sector.

Debate over 'SaaSpocalypse' grows as software stocks fluctuate dramatically

A dimly lit trading war room with towering screens showing wild red and green SaaS stock charts swinging like lightning; a flickering neon “SaaSpocalypse” banner casts crimson shadows across consoles; a lone trader’s silhouette grips his desk as storm clouds churn over a rain-soaked city through panoramic windows.

‘SaaSpocalypse’ debate intensifies as software stocks swing wildly - CNBC

- The SaaS sector faces pressure due to AI's impact on software product value.  
- Notable gains came from Twilio and Atlassian, despite overall market concerns.  
- Airtable's acquisition for under $1.3 billion highlights challenges in the software industry.  

In this article BSP

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Michael Cannon-Brookes, co-CEO of Atlassian. Scott Mlyn | CNBC

The "SaaSpocalypse" was raging this week. Then it got a reprieve. The pennies-on-the-dollar acquisition of one-time highflyer Airtable, followed by plunges in HubSpot , Datadog and Figma on earnings reports, all served to underscore investor concerns that artificial intelligence models are eating away at the value of costly software products. "I do think that companies are going to continue to use these coding agents to build functionality to maybe act as a little bit of pressure on renewals," said Matt Hedberg, a software analyst at RBC Capital Markets. But by the end of the week, Wall Street was reminded that there's still juice left in an industry that, not long ago, was viewed as driving innovation and was trading at hefty market multiples. Twilio , a provider of cloud-based communications software, and Atlassian , which sells collaboration tools, each saw pops of well over 20% on Friday after reporting quarterly results. Cloudflare , which helps protect websites from cyber attacks, gained 5.6%. Box CEO Aaron Levie, who's been evangelizing cloud software since he started his file-sharing company more than two decades ago, was particularly impressed with Atlassian, which reported its most profitable quarter since 2021. "Huge Atlassian quarterly beat," Levie wrote on X. "There was a misplaced thesis over the past 6 months that somehow agents would be bad for certain software categories. There's definitely truth in this in some areas, but many were parsing this poorly."

Levie's reaction reflects a level of nuance that's not always apparent in the sell-it-all trade. Salesforce CEO Marc Benioff has spent months trying to convince investors that his company's longstanding software for sales, marketing and customer service won't be vibe-coded away. Salesforce has lost more than 40% of its value since the end of 2024 despite accelerating revenue growth and consistent margins. Salesforce and its software peers have been under pressure, less because of their current growth rates and more because of the increasingly popular view that tools like OpenAI's Codex and Anthropic's Claude Code will slowly — and then rapidly — hollow out their economics. The iShares Expanded Tech-Software Sector ETF plummeted 24% in the first quarter, its worst performance since 2008. However, it's since rebounded sharply and is now down just 3% for the year, while the Nasdaq is up 15%.

RBC's Hedberg said sentiment was so bad in the first quarter that clients didn't even want to discuss software. "People were pencils down in the space," he said. "They just didn't feel like it was worth their time to meet with software companies back then, and that was even high-quality companies." That attitude has become a big problem for venture-backed software companies that raised money at hefty valuations in the pre-AI era and are now trying to figure out what's next for them. There haven't been any notable SaaS IPOs this year, and 86% of private deal value in the first half of 2026 went to AI companies, according to PitchBook data. Concerns were amplified on Tuesday, when Italian software buyout firm Bending Spoons said it had agreed to buy workflow management startup Airtable for less than $1.3 billion. At its peak in 2021, Airtable was valued at nearly $12 billion. Bending Spoons, which held its own IPO in July, owns brands including AOL, Evernote and Vimeo, and now has a market cap of $28 billion.

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Two days after the Airtable deal, HubSpot had its worst day on the market in a decade, sliding 19% and bringing its loss for the past year to over 50%. Datadog, which sells monitoring software, also sank 19% on Thursday, the steepest slump since its IPO in 2019. Still, the stock has been an outperformer, climbing 72% this year. Datadog said its biggest AI client, which analysts suspect is OpenAI, has cut usage since June. "There's these reminders that we're not out of it," Hedberg said. "Everything's not smooth sailing." The mood improved after the close on Thursday, thanks to stronger reports from Atlassian, Twilio and Cloudflare. Atlassian CEO Mike Cannon-Brookes credited his company's research and development teams for working hard to give customers "the best quality at the best price and the best speed." The report came five months after Atlassian slashed 10% of its workforce, or 1,600 jobs, which Cannon-Brookes said at the time was necessary to "self-fund further investment in AI and enterprise sales, while strengthening our financial profile." Atlassian's 35% surge on Friday marked its best day since the company went public in 2015. It's still down 8% this year after losing one-third of its value in 2025. Rishi Jaluria, another RBC analyst, said some of the pop for Atlassian is likely attributable to short covering, meaning that investors who bet against the stock by borrowing shares and selling them are now buying them back. "Everyone says Atlassian is going to get crushed by Claude Code and OpenAI Codex, and so they're done," said Jaluria, who doesn't cover the company. "Any proof point that says, oh wait, this company isn't dead, is going to result in a short squeeze." WATCH: We do not think AI will kill 'SaaS' but slow decay is bad enough: CLSA

‘SaaSpocalypse’ debate intensifies as software stocks swing wildly

During the International Telecommunication Union (ITU) AI for Good Global Summit held in Geneva on May 30, 2024, the growing potential of artificial intelligence (AI) and its implications for humanity were highlighted by a senior official from the United Nations. The official emphasized the pressing need for society to harness AI's immense capabilities while simultaneously steering clear of associated dangers.

As AI continues to dominate the business landscape, the venture capital market has sharply focused on this sector. In the first quarter of 2026 alone, AI startups attracted $242 billion, accounting for an impressive 80% of global venture financing, as reported by Crunchbase. Meanwhile, the technology enabling this funding has become increasingly accessible, allowing for rapid duplication.

Rime Salmi, founder and CEO of Fractl, operates a network in Toronto that organizes exclusive gatherings for founders and investors during major conferences like Token2049 in Singapore, Korea Blockchain Week, and Consensus in Miami. Her role involves selecting participants for these events. She noted that many attending are not genuine investors, but rather individuals seeking to capitalize on investment opportunities or engage in fraudulent practices.

Salmi observed the uncertainty among investors regarding future trends, making it challenging for them to maintain strong, sustainable investment theses. She noted that while AI is a prevalent buzzword, the role of humans in the ecosystem is becoming ambiguous. Investors appear to be responding to the uncertain landscape by heavily investing in AI-related ventures. In the first half of the year, global venture investments surged to $510 billion, surpassing the entire previous year's total. Notably, companies like OpenAI and Anthropic claimed a staggering 43% of this funding. Ventures that do not fall within this lucrative category face scrutiny regarding their uniqueness and ability to offer something that cannot be easily replicated. Salmi argued that much of the competition can be imitated, challenging the credibility of any app built with AI in a matter of seconds. However, she insists that speed in development does not equate to validation. In her view, the two essential advantages remaining for startups are distribution and proprietary data. If a company possesses unique data, it becomes a significant barrier for competitors to replicate.

This sentiment is echoed by other industry professionals. For instance, Paul Irving of GTMfund described distribution as a vital and sustainable competitive edge, while Balaji Srinivasan noted the necessity for each startup to find a founding content creator. The phenomenon of "vibe coding" has impacted industries beyond software, extending into trading as well.

Salmi pointed out a shift where founders can serve as their own distribution channels. Building a direct connection with an audience allows founders to create a dedicated following that is challenging for competitors to disrupt. Daniel Bitton, who leads a clipping marketplace, highlighted that companies need to embrace media and culture to stay relevant. His platform specializes in generating short-form content for brands, capitalizing on the dynamics of digital marketing. Salmi emphasized the importance of organic audience growth, which persists even when advertising budgets dwindle, and thus she conducts storytelling workshops for founders to help them effectively engage their audiences.

Addressing the exclusivity of her events, Salmi expressed a commitment to maintaining quality over creating a pay-to-play environment. Her approach involves meticulous vetting of applicants to ensure that only serious participants join. This method transforms the initial selection process into a pitch, building an atmosphere of credibility within the room. Salmi believes that in an increasingly competitive market, a more selective approach is beneficial compared to the traditional broad access that characterized the venture space, which historically favored young, male graduates from top universities.

Salmi's path into event management traces back to her experience running a record label and her initial involvement in hosting founder-investor brunches. The success of these gatherings led her to transition full-time into this venture, which began outpacing her prior marketing agency. Despite the operational challenges of scaling, including managing numerous events across diverse locations, she remains honest about her learning curve. The financial elements of her business are also influenced heavily by the volatile cryptocurrency market, prompting her to seek more stable sponsorship opportunities beyond the crypto sphere.

In the AI era, the sole competitive advantages for startups are data and distribution.

A sunlit scene of a sleek medieval-style castle labeled “Startup” perched on a floating island. Two protective moats encircle it: one moat pulses with glowing binary streams and data packets, the other is a network of roads and pipelines branching out toward distant marketplaces. Soft natural daylight highlights the stone walls and swirling bits, conveying that data and distribution are the only remaining defenses.

The Only Startup Moats Left In The AI Era Are Data And Distribution - Forbes

- AI companies dominated venture funding in 2026, capturing 80% of global investments.  
- Founders must build their own distribution channels for sustainable business success.  
- Rime Salmi emphasizes vetting investors to maintain quality at investor-focused events.  

TOPSHOT - A robot using artificial intelligence is displayed at a stand during the International Telecommunication Union (ITU) AI for Good Global Summit in Geneva, on May 30, 2024. Humanity is in a race against time to harness the colossal emerging power of artificial intelligence for the good of all, while averting dire risks, a top UN official said. (Photo by Fabrice COFFRINI / AFP) (Photo by FABRICE COFFRINI/AFP via Getty Images) AFP via Getty Images

Venture capital has narrowed to a single trade. AI companies took $242 billion in the first quarter of 2026, 80% of all global venture funding, according to Crunchbase. At the same time, the software those checks used to underwrite has become cheap enough to copy in an afternoon.

Rime Salmi watches the consequences from the door. She is the founder and chief executive of Fractl, a Toronto-registered network that runs invite-only founder-and-investor events, brunches and cocktail hours timed to conference weeks like Token2049 in Singapore, Korea Blockchain Week and Consensus in Miami. Her job is deciding who gets in. "There are a lot of people who are posing as investors and they’re absolutely not. They're looking for investments or grifting actually," she said in an interview.

Two Moats Left "Investors are not sure where the world is going. So it's hard for them to have a very strong thesis that they can sustain," Salmi said. "AI is a big buzzword, but we don't know where the human fits in that anymore. Are we okay with one person company or are we still funding solid big teams?" The money says investors have mostly answered by piling into one trade. Global venture investment hit $510 billion in the first half of the year, more than all of 2025, with OpenAI and Anthropic alone capturing 43% of it. Everything outside that trade has to explain why it cannot be copied. Salmi's answer is that most of it can be. Asked whether an app someone assembled with AI in ten seconds can be taken seriously, she pushed back before the question was finished. "It doesn't mean it's not going to be super successful," she said. Speed of construction, in her words, is "not a measure of validity anymore." What survives, she says, are two moats: distribution and data. "If a startup has solid proprietary data, that's something that would be very difficult to vibe code them out of because it would just take years to get really high quality in depth data." Venture investors have spent 2026 saying versions of the same thing. GTMfund's Paul Irving calls distribution the last sustainable moat, and Balaji Srinivasan argues every startup now needs a founding content creator, as Forbes reported in April. Vibe coding, the collapsed technical moat's proximate cause, has already spread beyond software into trading. "It's the age of the solo entrepreneur," Nimrod Lehavi, founder of the AI venture studio Inevitable AI Group, said in an interview. His firm builds one-person clones of billion-dollar software companies and raised a $6 million pre-seed led by Aleph on the thesis.

The Founder As The Channel "Now a founder has the opportunity to be their own distribution channel," Salmi said. A founder who builds a following directly, rather than renting reach, ends up with "an audience that is captive and owned," she said, and that audience is hard for a competitor to take because it is personal. "Every company needs to turn into a media company. You need to hit the culture. You need to be relevant at all times," Daniel Bitton, founder of a clipping marketplace, said on the On The Margin podcast. Renting that relevance is an industry of its own. His platform pays armies of short-form video clippers to flood feeds for brands, the model behind the clipping farms driving fintech marketing and crypto gambling's viral machine. Salmi's point is that bought reach stops when the budget stops. The owned audience keeps compounding, which is why she runs storytelling workshops for founders on the side. The skill she teaches is "the art of telling a good story every time you meet someone," she said. "I think there's a lot more human being injected in selling tech. And I think that's a great thing."

Who Gets In The Room "We don't want this to become just another pay to play room," Salmi said of her refusal to sell tickets. "The reason the quality diminishes in other investor focused events is because you can pay to be in the room." Founders mid-raise are the last people who should be charged, she said: "It just didn't feel right to take money from the people who don't have it." So Fractl vets instead. Every applicant is reviewed, she said, to filter out the people posing as investors. The screening is the pitch. "When you get in the room, you're like, okay, you made it. So you must be somebody worth talking to." Venture capital has always had a velvet rope; it just used to hang somewhere less transparent. "The market was looking for male early twenties from top ten schools," Howard Marks, co-founder of StartEngine, said on the On The Margin podcast, recalling the funding market he set out to disrupt with equity crowdfunding. The top universities, he said, "represented about 80% of where the venture capital money came in, and angel money." Marks spent a decade betting that opening the gates to everyone was the fix. Salmi is betting the opposite: that in a noisier market, a better-guarded gate is worth more. The casualties of picking wrong fill the YC fintech graveyard.

Running The Room Is A Business Too "I used to run a record label," Salmi said of how she fell into events, after a friend who she says had worked at the London web3 fund Fabric Ventures handed over a founder-investor brunch series. "I can probably manage a brunch." The first one drew more than 200 people, over 100 of them investors by her count. Fractl grew out of a fractional-CMO marketing agency she wound down after the events outsold it. "After event five, I was like, I think we're shutting down the agency." Clients kept asking for her personally. "People were like, no, we want to work with you." She is unusually plain about what scaling it cost. "I'm not a very efficient operations person. I'm more of a salesperson," she said. "There was a year where I ran like seven events within five weeks in five different cities across six different time zones." She kept staff on monthly salaries when the work was event-based, and says she has tried eight different operators in the operations seat. "I don't even blame the people. I blame myself for letting it leak for so long." The revenue side carries a crypto-shaped risk she names just as readily. "If bitcoin is doing well, then people are spending on marketing," she said. "The sponsorship fluctuating based on the crypto market is kind of rough to base the business on." She is courting web2 sponsors to smooth it out.

The Only Startup Moats Left In The AI Era Are Data And Distribution

During a recent AI Day event held on November 5, 2025, at Xpeng's headquarters in Guangzhou, He Xiaopeng, the co-founder and chairman of the Chinese electric vehicle company, unveiled the company’s latest creation: the next-generation Iron humanoid robot. This development showcases Xpeng's commitment to advancing technology in a competitive market.

The conversation surrounding artificial intelligence often polarizes into two opposing camps of optimists and pessimists, but the reality is that the discourse is far more intricate than the headlines suggest. Central to this discussion are two dominant narratives, both stemming from a shared concern. The first viewpoint posits that AI's functionality poses a significant risk, as highlighted by Dario Amodei in a January 2026 essay, where he termed AI a “general labor substitute for humans.” A February report from Citrini Research echoed this sentiment, forecasting that AI could enhance hourly output to levels not seen since the 1950s, potentially leading to a collapse in employment and demand.

Conversely, the second narrative contends that AI will yield substantial benefits and broader economic gains. Cathie Wood responded to Citrini's analysis on social media platform X, predicting a surge in productivity and real GDP growth while also suggesting a decrease in inflation. Both narratives rely on the assumption that AI will perform remarkably well; however, neither challenges the fundamental trade-off involved.

According to technology historian Lee Vinsel, this trend falls under the label of “criti-hype,” where critiques acknowledge industry promises without skepticism, allowing caution to serve as promotional material. On July 24, Peter McCrory, Anthropic’s head of economics, published insights on X indicating that AI has not significantly influenced U.S. employment, referencing a June unemployment rate of 4.2%. He expressed little expectation for a notable rise in unemployment within the following year, despite contrary statements made by his CEO over the previous year.

The bearish perspective consists of a series of arguments under a unified label, revealing deeper disagreements. Investor Michael Burry focused on accounting practices, posting on X about how inaccurately reporting asset depreciation can artificially inflate earnings. He estimated that, from 2026 to 2028, there would be approximately $176 billion in understated depreciation across the sector, with significant discrepancies in earnings reports from companies like Oracle and Meta. Advocates of longer depreciation schedules defend their validity by arguing the prolonged lifecycle of chips used in AI technology, which undergoes extensive stages from training to inference.

Further disagreements arise regarding revenue quality, with Bill Gurley spotlighting problematic circular transactions between tech giants and startups, and Ed Zitron questioning the solvency of firms like OpenAI, dubbing it one of the largest liabilities in recent times. The deployment critique came from MIT’s Project NANDA, which revealed that roughly 95% of enterprise generative AI pilot programs had no discernible impact on profit and loss metrics. The findings drawn from about 300 public initiatives, 52 organizational interviews, and 153 survey responses from industry events underscore an ongoing issue with enterprise systems that struggle to incorporate feedback and adapt to contexts.

In the discourse of AI, some observers misinterpret the skepticism surrounding the technology. In an essay by Arvind Narayanan and Sayash Kapoor, they argue that AI is as transformative as electricity or the internet, but emphasize that its impact is driven more by adoption rates than by its innovative capacity. They note a “capability-reliability gap,” highlighting a critical barrier to realizing fully functional AI agents. Economist Daron Acemoglu has shifted positions over the summer; previously measured in his assessments, he recently joined over 200 researchers, including 16 Nobel laureates, in cautioning against potential disruptions to white-collar employment. This pivot elevates the significance of his insights, surpassing any timeline set by tech founders.

The Four AI Narratives Currently Dividing Venture Capital

A sunlit boardroom with warm morning light pouring through tall windows onto a polished mahogany table. At its center, a pile of golden coins splits into four glowing streams, each arcing toward a distinct futuristic pillar: one bathed in neon blue with a robotic arm icon, one in verdant green etched with neural-network patterns, one in ruby red featuring a circuit-infused medical caduceus, and one in deep violet bearing a shield motif. Silhouetted figures in sharp business attire stand around the table, watching the four AI narratives pull venture capital in different directions.

The Four AI Narratives Splitting Venture Capital Right Now - Forbes

- Xpeng launched its next-gen Iron humanoid robot, showcasing advancements in AI technology.  
- The AI debate includes nuanced perspectives, challenging simplistic optimistic or pessimistic narratives.  
- Concerns persist regarding AI's impact on employment and productivity, indicating ongoing skepticism.  


TOPSHOT - He Xiaopeng, cofounder and chairman of Chinese electric vehicle maker Xpeng, launches Xpeng's next-gen Iron humanoid robot during AI Day press conference at its headquarter in Guangzhou, in southern China's Guangdong province on November 5, 2025. (Photo by Jade GAO / AFP) (Photo by JADE GAO/AFP via Getty Images) AFP via Getty Images

The AI debate gets reported as optimists against pessimists. But in reality, there is much more nuance, layer and tension then what appears in the headline on tweet.

The premise the loudest camps share Two narratives dominate coverage, and both sit on the same side of the only question that counts. The first holds that AI works and that is precisely the danger. Dario Amodei doubled down in a January 2026 essay calling AI a “general labor substitute for humans.” Citrini Research’s February note imagined agents lifting output per hour to rates unseen since the 1950s while employment and demand collapse. The second holds that AI works and the gains spread. Cathie Wood rebutted Citrini on X, writing that ARK forecasts “a productivity boom, an acceleration in real GDP growth” alongside lower inflation. Each requires AI to be extraordinary. Neither argues against the trade. Technology historian Lee Vinsel calls the pattern criti-hype , criticism that accepts an industry’s grandest claims at face value so that warnings double as marketing. On July 24, Anthropic’s head of economics Peter McCrory published an X essay finding no material AI effect on US employment, citing a June unemployment rate of 4.2%. “I don’t expect unemployment to be noticeably higher a year from now,” he wrote. His chief executive had spent a year saying the reverse.

The bear case is four arguments wearing one label Below the capability line the disagreement is real, and it fragments immediately. Michael Burry’s target is the accounting. He posted on X that “understating depreciation by extending useful life of assets artificially boosts earnings,” estimating $176 billion of understated depreciation industry-wide from 2026 to 2028, with Oracle overstating earnings by near 27% and Meta near 21%. The counterargument is that a chip cascades from frontier training into inference and cloud rendering across six years, which makes the longer schedules defensible, and auditors keep approving them. The dispute reduces to whether inference demand grows fast enough. Bill Gurley questions revenue quality instead, flagging circular deals in which a technology giant invests in a startup that spends the money back on the investor’s cloud. Ed Zitron goes at solvency, calling OpenAI one of the largest liabilities in recent economic history. The deployment critique belongs to MIT’s Project NANDA, which found that roughly 95% of enterprise generative AI pilots delivered no measurable profit-and-loss impact. That number gets repeated far more often than it gets read. The report describes itself as preliminary findings drawn from around 300 publicly disclosed initiatives, 52 organizational interviews and 153 survey responses collected at industry conferences. What has held is the mechanism rather than the figure; enterprise systems that cannot retain feedback or adapt to context. A VC investor can be right about depreciation and wrong about deployment, and most commentary treats the package as indivisible. AI Narratives: market map Josipa Majic Predin

The camp everyone misreads The remaining position gets filed as AI skepticism and is something else. Arvind Narayanan and Sayash Kapoor’s essay argues AI is transformative in the way electricity and the internet were transformative, with tempo set by adoption rather than invention, and identifies the “capability-reliability gap” as the barrier to working agents. Daron Acemoglu moved between camps this summer. He was the economist whose modest estimates gave policymakers room to wait. In July he signed a letter with more than 200 researchers, sixteen of them Nobel laureates, warning of white-collar disruption. Nothing in his book improves from that reversal, which makes it worth more than any founder’s timeline.

The Four AI Narratives Splitting Venture Capital Right Now

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Jewish marriage platform adopts AI while emphasizing the importance of human connection.

A sunlit study table by a window, a traditional ketubah parchment spread open on warm wood. Beside it, a modern laptop displays a friendly AI matchmaking interface in soft, natural tones. An elderly matchmaker’s hand hovers with a quill pen over the parchment, blending digital algorithms with the promise of a human touch.

Jewish marriage platform embraces AI — but says the human touch is still essential - Fox News

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Jewish marriage platform embraces AI — but says the human touch is still essential

AI coding tools have proven to be incredibly beneficial, significantly enhancing speed and productivity metrics at Databricks and various other organizations. Despite these advantages, businesses implementing AI at scale are encountering a common challenge: surging expenses. This escalation poses a sustainability issue, where unchecked costs may eventually overshadow revenues. Companies find themselves torn between aggressively pursuing AI advancements and managing expenditures that could hinder the very efficiency benefits that AI aims to bring.

Fortunately, many of the early adopters of large-scale AI tools have developed strategies to tackle this challenge, achieving a "dual mandate": providing widespread access to AI resources with minimal barriers while keeping costs per user within a manageable range. This article outlines effective cost management practices gleaned from Databricks' experiences and discussions with other tech pioneers like Stripe, Coinbase, Uber, and Ramp. A summary table illustrates current methods and estimated savings, based on informal surveys from development teams.

Some strategies can be seamlessly integrated with existing software, while others necessitate new infrastructures, particularly those that modify end-user interfaces or reroute traffic across models. At Databricks, we've made significant infrastructure components, such as the end user meta-harness (Omnigent) and our AI Gateway (Unity AI Gateway), available as open source or free tools. This article also discusses additional software utilized by other organizations we've consulted.

The "Efficiency Frontier" in Coding Models

The key to reducing coding costs lies in transitioning to more efficient models as they become available. While it may sound straightforward to say "cheaper models," the relationship between model cost and quality is more intricate.

"Frontier models" refers to those developed with the highest intelligence levels – typically the focus of innovative labs that push the boundaries of AI capabilities. These models can address complex challenges in areas like math and cybersecurity. However, in large-scale deployments, the efficiency frontier becomes crucial. This frontier comprises models that offer the best price-to-performance ratio for typical software engineering tasks, which usually do not require the advanced capabilities that frontier models offer. As such, the efficiency frontier is evolving rapidly, with new models being introduced almost weekly that outperform their predecessors in terms of cost efficiency relative to intelligence.

Cost Management Strategy #1: Transitioning to Open Source and Cost-Effective Models

Swift adoption of cutting-edge models provides the most significant cost benefits. However, organizations must first assess which new models actually outperform their existing options. Given that public benchmarks often fail to reflect real-world performance adequately, many companies, including Databricks, have established automated evaluation processes more representative of their unique development environment. This led to the successful internal deployment of GLM after observing its competitive price/performance.

In some instances, new models do not yield efficiency gains. For example, Stripe determined that Opus 4.7 did not improve quality over its predecessor while also increasing costs, leading to the decision against its internal introduction. Similarly, Databricks encountered cost inefficiencies when comparing Opus models.

Harnesses and Model Flexibility

Since the best cost savings arise from adopting new models, employing user tools that facilitate model flexibility has become essential in keeping expenses down. The tool known as a harness is commonly associated with specific models, and certain proprietary models are increasingly designed to integrate well with designated harnesses. Companies can preserve model independence through two primary strategies:

1. Encourage users to switch harnesses: Companies can provide a selection of harnesses and allow developers to choose among them when migrating to lower-cost models. Although this option supports user preferences, switching between harnesses may incur high costs for developers, risking de facto model family lock-in.

2. Implement a meta-harness: A more prevalent strategy involves using a meta-harness, which offers a unified experience for developers but routes requests to various underlying harnesses, thus maintaining independence and reducing switching costs. Databricks’ default Omnigent setup follows this model, while some companies have created custom internal meta-harnesses.

Cost Management Strategy #2: Dynamic Request and Task Routing

Instead of requiring users to select appropriate models, emerging research suggests that automatic selection may further enhance efficiency in agentic coding workflows. Routing methodologies can be categorized into three types:

1. Request Level Routing: A stateful proxy intermediates client requests and the foundational models, striving to direct queries to the lowest-cost compatible model. This method also addresses server-side caching, which is crucial for managing large context workloads. New routing products demonstrating promising results include Cursor Router and OpenRouter's AutoRouter, alongside Databricks’ Smart Routing feature.

2. Task Level Routing (Meta Harness): A client-side mechanism distributes tasks based on complexity. For example, it determines whether a task is straightforward, like renaming a component, or more complex, like evaluating design considerations to reduce latency, and subsequently delegates it to the appropriate model.

3. Escalation/Delegation Patterns: This involves pairing two models, one high-cost and one low-cost, with task management shifting between them based on complexity. For instance, Claude's Advisor Tool designates a lower-cost model for most tasks, escalating to a higher-cost model when needed.

Internal findings at Databricks indicate that our AI Gateway’s Smart Router consistently reduces average costs per task by more than 30% while maintaining the quality of higher-cost models. Other companies we've surveyed have reported similar outcomes.

Cost Management Strategy #3: Enhancing Visibility, Checkpoints, and Budgets

It may be unexpected that this discussion has not started with the recommendation to simply assign users a monthly budget. Hard limits that suspend usage upon reaching specific spending thresholds are rarely viable, as they can hinder productivity. Two primary reasons dissuade companies from relying on rigid budgets: halting access to AI tools when budgets are depleted can severely impact work output, and high-spending users may be those driving substantial efficiency gains.

Instead of fixed budgets, organizations are trending toward a more sophisticated approach that emphasizes visibility and introduces increasing levels of friction as spending rises:

- Visibility: Companies provide near-instant feedback on spending, including actionable insights on cost-saving strategies. Developers can track their expenses across tools, informed by dashboards that show active spending.

- Spending Checkpoints: Developers can be prompted to take specific actions or seek approvals as spending approaches or exceeds predetermined thresholds. At Databricks, we have found self-clearing warnings effective for preventing unintentional overspending. Further steps may involve requiring managerial approval for budget breaches.

- Downshifting: If a developer reaches a spending limit, they may be transitioned to a less expensive model instead of having access revoked entirely. Since lower-cost models significantly reduce expenses compared to high-intelligence options, this strategy enables continued productivity without unsustainable costs.

- Suspension: Ultimately, companies retain the option to fully suspend access, but typically this is a last-resort measure and a starting point for discussions about efficient AI use.

Cost Management Strategy #4: Minimizing Token Overhead

When users submit relatively straightforward requests to an AI coding agent, the requests often gather extensive context, invoke numerous tools, and engage various system inputs before incurring costs associated with lengthy model inference. As a result, the user's original request contributes minimally to the overall data processed, leading to inflated expenses driven by unnecessary context. To combat this, several strategies are being explored, including:

- Encouraging more frequent context compaction or compression.
- Utilizing harnesses that are designed to be more token-efficient or adjusting existing ones.
- Streamlining verbose tools and fostering developers to break tasks down into smaller units.

When contexts expand, prompt caching also becomes crucial to performance. Both proprietary and open source models have features that allow for caching and tuning duration, balancing the expense of cache writes against cost savings from cached reads. Companies tuning these settings effectively can see substantial reductions in overall costs.

At Databricks, simple tweaks in our harness and caching strategies resulted in nearly a 50% cut in generated tokens and their associated costs, without any noticeable decline in quality for developers. Ongoing exploration in this area suggests further optimization opportunities remain.

The AI Gateway Design Approach

The previously discussed techniques come with several technical requirements. To swiftly capitalize on new models, firms should maintain centralized management of the "model menu" while ensuring end users have compatible tools for model variety. For budget oversight across AI resources, a centralized observability framework is needed. Additionally, to manage context bloating, organizations should track common tool outputs and enforce data compression practices. These needs are addressed by a new class of infrastructure software termed an AI Gateway. An AI Gateway centralizes:

- Management and proxying access to underlying models, whether proprietary or open-source.
- Budget tracking and enforcement, including advanced policies for gradual friction levels and model adjustments.
- Configuration management for user tools, which enforces model preferences and compaction settings.
- Logging of coding session activities for subsequent efficiency evaluations and benchmarks.

Databricks leverages Unity AI Gateway to fulfill these requirements.

Summarizing the Insights

The rapid rise in AI coding expenses does not have to be an unavoidable consequence; it represents a challenge that organizations can address through solid engineering and governance practices. Companies that have successfully navigated this issue adhere to a common framework: focus relentlessly on the efficiency frontier rather than elevating intelligence alone, adopt tools that maintain model flexibility, smartly route work to the most cost-effective capable model, replace rigid budgets with transparency and adaptable friction, and minimize token overhead that contributes to spending. None of these methods necessitates sacrificing productivity gains from AI, enabling firms to achieve widespread and low-friction access while staying within a controlled cost structure.

New infrastructure tools are emerging to assist organizations in managing expenses. At Databricks, we have made the key components of our cost management solutions open source or freely accessible: Unity AI Gateway for centralized oversight and Omnigent for developer tools. Thousands of organizations currently utilize these resources, and we encourage more companies to share experiences and refine strategies as the technology landscape continues to evolve.

Acknowledgments: Special thanks to infrastructure leaders at Uber, Stripe, Coinbase, and Ramp for their insights and reviews of this article. Our gratitude also goes to Thrive Capital for their feedback on an early draft.

Controlling AI Programming Expenses on a Large Scale

A bright, sunlit control room where a poised operations manager stands before a giant, old-fashioned balance scale: on one pan, neatly stacked gold coins and invoices, on the other, a tumbling heap of glowing green 1s and 0s and miniature cloud icons. Behind them, floor-to-ceiling server racks hum quietly under soft natural light through skylights, while a wall-mounted display charts soaring cost curves and optimization metrics.

Managing AI Coding Costs at Scale - Databricks

- AI coding tools have improved productivity metrics but have led to unsustainable cost growth.  
- Companies adopt strategies for broad AI access while managing user costs effectively.  
- New infrastructure like AI Gateways helps manage model access, budgets, and efficiency.

AI coding tools deliver immense value: at Databricks, agentic coding has measurably improved every velocity metric we track and, in some teams, driven an order-of-magnitude gains in output. But nearly every company deploying AI tools at scale has hit the same wall: exponentially growing costs. That curve is unsustainable - left unchecked it will eventually overtake revenue. The spend explosion has left enterprises in a paradoxical situation: on the one hand, desiring to maximally push AI transformation and put powerful tools in the hands of employees, and on the other hand, having to reconcile with an aggregate cost profile that threatens to undermine or even reverse the very efficiency gains AI provides.

Fortunately, several of the earliest large-scale adopters have converged on a set of approaches that solve this puzzle, achieving a “dual mandate”: (a) providing broad access to AI tooling, with minimal friction, and (b) keeping aggregate costs inside of a roughly fixed envelope per user. This post outlines proven cost management techniques, based on our experience at Databricks and conversations with several other digital-native companies, including Stripe, Coinbase, Uber, and Ramp. The table below summarizes current techniques and associated savings; the numbers are directional, based on an informal survey of development teams:

Some of these techniques can be easily implemented with software many companies already use. Others require new infrastructure, particularly techniques that modify end-user clients or shift traffic across models. At Databricks, we’ve open sourced or made freely available our key infrastructure components: an end user meta-harness (Omnigent) and our AI Gateway (Unity AI Gateway). For completeness, this post also covers software used by other companies we spoke with.

The “Efficiency Frontier” for Coding Models

The single greatest cost lever in moving coding spend to more efficient models as they are released. This point bears some discussion, as the simple explanation of "cheaper models” in fact hides a nuanced relationship between model cost and quality.

Colloquially, the term frontier model means “the highest intelligence model,” and frontier labs largely focus on advancing peak intelligence. Frontier models can now solve novel problems in math or cybersecurity. But when AI is deployed at scale, a different type of frontier matters more: the efficiency frontier. The efficiency frontier is defined by the set of models that have the best price point for a given level of intelligence. Most day-to-day coding doesn't require mathematical proofs or novel security insights, so what matters in aggregate is the cost of models that meet the quality bar for typical software engineering work. This "efficiency frontier” is advancing far faster than the intelligence frontier, with new models being released almost weekly that present better intelligence-per-unit-price than prior models.

Cost Lever #1: Moving to open source and lower cost models

Rapidly adopting newer, more efficient models delivers the largest cost wins of any technique. But to capture those gains, a company first needs to know which models actually beat its incumbents. This can be difficult because public benchmarks do a poor job of indicating real-world performance on coding tasks. To size up new models, many companies have built automated evaluations that they believe are more representative of their internal development mix. Databricks recently published an example of such a benchmark, in which we observed highly competitive price/performance for GLM models. That benchmark led us to roll GLM out to developers internally. Often, new models do not advance the efficiency frontier,and evaluations frequently produce negative results: Stripe found that Opus 4.7 did not meaningfully improve quality over Opus 4.6, while increasing cost. They therefore declined to make Opus 4.7 available internally. Databricks saw similar cost regressions when comparing Opus 5.0 to 4.8.

Harness and Model Flexibility

Since the biggest wins come from switching to new models, adopting end user tooling that allows for model flexibility is becoming a critical component of keeping costs down. The tool most commonly used in concern with a particular model is called harness. Proprietary frontier models are increasingly co-designed to work well with specific harnesses, meaning certain harnesses “work better” with certain models. If a company wants to preserve model independence there are roughly two approaches:

Ask users to switch harnesses. One approach is to provide developers with a set of harnesses (Claude Code, Codex, or Cursor) and then ask them to switch between harnesses when a company wants to migrate spend to lower cost models. This lets users work in their preferred harness when possible, but the downside of this approach is that switching costs for an individual developer can be high. If switching costs become too high, the harness itself becomes a de facto lock-in to a model family, limiting the ability to move spend to more competitive models.

Use a meta-harness. A new and increasingly popular approach is to use a meta-harness that surfaces a common user experience to developers while dispatching requests to underlying harnesses (both proprietary and open source). This approach allows both model/harness independence while also reducing developer switching costs. At Databricks, this is the default mode for developers who leverage Omnigent. Some companies we talked to have built custom internal meta-harnesses that integrate with their development toolchain.

Cost Lever #2: Dynamic Request and Task Routing

Instead of asking users to choose task-appropriate models themselves, a growing body of research suggests that automatic model and tool selection may further squeeze efficiency out of agentic coding workflows. Routing approaches roughly fall into three categories:

Request Level Routing: A stateful proxy sits in between a client (such as a coding harness) and the underlying foundation models. The proxy attempts to route requests to the lowest-cost model capable of answering each inference request. Routing for agentic use cases also needs to account for server-side caching, since a cold cache hit has a very high cost for large context workloads. A new wave of products is showing early, promising results for routing. Examples are: Cursor Router, OpenRouter’s AutoRouter, Ramps Router feature and Databricks own Smart Routing feature in Unity AI Gateway.

A stateful proxy sits in between a client (such as a coding harness) and the underlying foundation models. The proxy attempts to route requests to the lowest-cost model capable of answering each inference request. Routing for agentic use cases also needs to account for server-side caching, since a cold cache hit has a very high cost for large context workloads. A new wave of products is showing early, promising results for routing. Examples are: Cursor Router, OpenRouter’s AutoRouter, Ramps Router feature and Databricks own Smart Routing feature in Unity AI Gateway. Task Level Routing (Meta Harness): A client-side process dispatches user tasks to different harnesses based on the complexity of the task. A user task might be “rename this component from X to Y” (a simple task) or an open-ended task like “Explore design considerations that would reduce latency” (a complex task). The dispatcher, often called a Meta Harness, examines which level of underlying model is required for a task and then delegates that entire end-to-end task to the model. Omnigent is an example of a Meta Harness that supports this pattern.

A client-side process dispatches user tasks to different harnesses based on the complexity of the task. A user task might be “rename this component from X to Y” (a simple task) or an open-ended task like “Explore design considerations that would reduce latency” (a complex task). The dispatcher, often called a Meta Harness, examines which level of underlying model is required for a task and then delegates that entire end-to-end task to the model. Omnigent is an example of a Meta Harness that supports this pattern. Escalation/Delegation Patterns: A single harness pairs two models (an expensive, high-intelligence model and a cheap worker model). In some approaches, such as Claude’s Advisor Tool, the cheaper model runs the show and escalates when it thinks a task requires more horsepower. The inverse pattern also exists: In Cognition’s Devin Fusion, the higher cost model is the main loop, and it selectively outsources work to a cheaper model.

Internal results at Databricks suggest that our AI Gateway Smart Router is able to consistently reduce average task cost by more than 30%, while roughly matching the quality of the most expensive model in the working set. Other companies we spoke with have seen similar results.

Cost Lever #3: Giving developers visibility, tripwires, and budgets

It may be surprising that this entire article did not start and end with “Give users a monthly budget and be done with it.” Hard budgets, where usage is entirely cut off at a specific spend threshold, are often used only as a last resort option in every company we spoke with. There are two reasons that hard token budgets are not particularly effective for AI spend management: First, if a developer hits their budget ceiling, cutting off further access to AI tools would be debilitating to productivity. Neither the company or employee actually wants that outcome. Second, at least some of the “high spending” users are in fact those who have achieved monumental efficiency gains with AI and are producing immense output. Discouraging those users is self-defeating.

Instead of a hard user spending cap, most companies are adopting a more nuanced and progressive approach that focuses on visibility for end users and increased degrees of friction as spend increases.

Visibility: Every company we spoke with had a mechanism to provide near-instantaneous feedback to users on their ongoing spend, with many also offering specific tips or insights on how to reduce spend by using less expensive models. It is important that users be able to see their spend across all tools, since they may want to influence their choice of tool where they get the highest ROI. A developer dashboard at Databricks showing active spend Spend Gates: Developers can be asked to take actions or seek approvals at increasing levels of spend. The simplest form of spend gate is one that can be self-cleared and serves as a warning that the spend rate is increasing above some threshold. At Databricks, we’ve found self-clearing gates a useful mechanism for preventing accidental or unintentional spend. Further gates can be introduced that require explicit budget approval (often through a management chain). Downshifting: If a developer has hit a spend gate, they can be downshifted to a lower-cost model rather than being entirely suspended from token access. Since the lowest-cost models are drastically less expensive than frontier-intelligence models, this technique allows developers to continue getting work done without incurring massive ongoing spend. Suspension: In the limit case, most systems do retain the ability to fully suspend users from all token access. As stated above, this is often a temporary measure only and the starting point for a conversation about how to efficiently leverage AI.

Cost Lever #4: Reducing Token Overhead

When a user types a relatively simple request into an AI coding agent (such as “Please investigate and fix this bug.”), that agent subsequently gathers massive amounts of relevant context, invokes a large number of tools, searches through the codebase, and integrates skills or system information provided by the company. By the time costly LLM inference occurs, the user's initial statement accounts for only a negligible fraction of the data fed into the AI system, meaning costs are dominated by context the user did not explicitly include. Techniques in reducing context bloat are still new, but several promising approaches are being explored, such as:

Coercing more frequent compaction (compression) of the active context.

Using harnesses that are “less chatty” (more token efficient), or tuning existing harnesses to generate less token overhead.

Auditing popular tools and decreasing their verbosity.

Encouraging developers to break tasks into smaller individual units of work, decreasing context scope.

When contexts get large, prompt caching also plays a meaningful role in overall performance. Both proprietary and open source LLMs have settings that allow you to enable prompt caching and tune how long the cache is stored. Cache writes cost money, but cached reads can drastically reduce per-inference cost. This trade-off is dependent on a company’s specific workload, so hand-tuning of default cache settings to increase overall cache hit rate can have drastic improvements to overall cost.

At Databricks, relatively simple tuning of our harness and caching settings led to an almost 50% reduction in the number of generated tokens and associated costs, with no observed quality degradation for developers. We continue to explore techniques in this area and think meaningful additional optimization remains possible.

A drastic reduction in tokens per session by eliminating extraneous inference calls and reducing cache writes.

The AI Gateway design pattern

The techniques above had many implicit technical requirements: To rapidly take advantage of new models, companies must have a central location where the “model menu” is managed, and end-users must have a toolchain that supports model mixing. To provide budget visibility across multiple AI tools, a unified cost observability capability must exist. To manage context bloat, companies need a way to observe typical toolcall outputs and enforce compression or compaction. These needs are collectively being solved by a new class of infrastructure software, best described as an AI Gateway. An AI gateway is a central location where all of the following occur:

Capacity management and proxying of access to underlying models (both proprietary and OSS models). Budget tracking and enforcement, including complex budget policies such as progressive friction levels and model downshifting. Configuration management for end-user tools, to enforce model allow-lists, compaction settings, and other locally mediated aspects. Logging of coding session traces for downstream efficiency analysis and benchmark.

At Databricks, we rely heavily on Unity AI Gateway for all of these capabilities.

Putting it all together

The exponential growth of AI coding costs is not an inevitability, it's a solvable engineering and governance problem. Companies that have tamed it share a common playbook: relentlessly chase the efficiency frontier rather than the intelligence frontier, adopt tooling that preserves model flexibility, route work intelligently to the cheapest capable model, replace hard budgets with visibility and progressive friction, and cut the token overhead that dominates real-world spend. None of these techniques requires sacrificing the productivity gains that made AI adoption worthwhile in the first place; together, they let organizations satisfy the dual mandate of broad, low-friction access within a predictable cost envelope.

A set of new infrastructure abstractions is emerging to give companies the tools to manage their costs. At Databricks, we’ve released the key components in our cost management stack as open source or free software products: Our Unity AI Gateway for central management and Omnigent for developer tooling. Thousands of companies use these components every day. We invite more companies to share findings and compare techniques as this technology landscape rapidly evolves.

Acknowledgements: Thank you to infrastructure leaders at Uber, Stripe, Coinbase, and Ramp who provided commentary and reviews of this article. Thank you to Thrive Capital for feedback on an early draft of this article.

Managing AI Coding Costs at Scale

Each year, the wellness sector introduces an array of new products and practices that claim to unveil the ultimate path to well-being. While some of these trends prove to be beneficial, others lack credibility, often falling into a gray area of mediocrity.

Here are some wellness trends sparking debates and intrigue in 2026.

AI

Artificial intelligence is infiltrating all facets of our lives, including personal wellness regimens. Increasingly, individuals are seeking advice from chatbots for nutrition plans, workout guidelines, and even personal development strategies.

In a recent survey, readers of The Guardian shared their experiences with AI-based fitness. Some found personalized suggestions helpful for staying committed to their routines and achieving their fitness objectives, while others noted its limitations. One user pointed out, “It can be confidently incorrect.”

AI also supports personal growth by making self-reflection easier, experts suggest; chatbot prompts can clarify one’s questions and ambitions. However, since AI leverages vast amounts of human-generated data, it may produce generic recommendations that align with mainstream narratives rather than catering to personal significance.

Parasite Cleanses

Supermodel Heidi Klum recently disclosed on social media that she and her partner were trying a parasite cleanse, attributing the trend to a surge of online content about worms and parasites. This concept has gained traction, with social media users advocating that such cleanses could enhance sleep, gut health, mood, and skin.

While parasitic infections do exist, particularly in areas lacking proper sanitation, experts note these occurrences are rare in well-maintained environments. Additionally, herbal supplements marketed online cannot effectively treat these infections, which are typically diagnosed via stool or blood tests and managed with appropriate medications.

Proteinmaxxing

The buzz around protein continues to grow, with traditional sources like meat, dairy, and eggs no longer meeting demand. Many consumers are now engaging in "proteinmaxxing," supplementing their diets with protein-rich foods such as fortified snacks, powders, and cereals.

This surge in protein consumption can be linked to the rise of GLP-1 medications, which cause individuals to eat less, making sufficient protein intake vital for muscle retention. However, experts warn that muscle-building effects plateau once protein intake exceeds 1.6 grams per kilogram of body weight, with the general recommendation for most adults being 0.8 grams.

Divorce Rings

Also referred to as “F-you rings,” divorce rings embody the tradition of transforming engagement and wedding bands once a marriage dissolves. This trend has gained popularity among celebrities like Emily Ratajkowski and Rachel Zoe, who proudly showcase their new pieces.

One woman who remade her wedding ring explained, “It’s not necessarily aimed at my ex-husband; it’s more about pushing back against societal expectations that a woman's purpose is solely to be married, rather than to exist independently.”

Olive Oil and Lemon Juice

While the combination of olive oil and lemon juice might resemble a salad dressing, it has recently gained attention as a health elixir on social media. Proponents claim that consuming this mix can improve skin and digestion.

Truthfully, olive oil boasts several health benefits due to its monounsaturated fats that assist in lowering bad cholesterol levels and maintaining healthy arteries. Additionally, it contains beneficial antioxidants called polyphenols, which can help reduce inflammation. Although this trend is relatively safe to try, experts recommend integrating the mixture into meals rather than consuming it straight.

Hyrox

Every now and again, a new fitness craze captivates young professionals eager to push their limits, replacing trends like CrossFit. The latest is Hyrox, a competition that combines a series of grueling workouts with eight 1km runs.

Founded in 2017, Hyrox challenges participants with tasks ranging from sled pulls to rowing and burpee broad jumps. Participants eager to train at Hyrox often find themselves undertaking demanding workouts that mimic the competition, with some even expressing surprise at their physical limits.

Boy Kibble

"Boy kibble" is a term for a simple dish favored by fitness enthusiasts, typically made up of rice, ground protein, and vegetables. One nutritionist pointed out that this meal is a reincarnation of traditional bodybuilding diets, offering a nutritious option when balanced correctly with macronutrients.

However, relying solely on this meal structure is not advisable. Experts stress the importance of variety in diets, suggesting that individuals explore other nutritious options that might also be more exciting.

Functional Foods

In today’s wellness landscape, foods are being marketed with functional claims that go beyond their basic attributes—think chocolate that alleviates PMS and chips fortified with protein. These so-called "functional foods" are growing in popularity, primarily due to health-conscious consumers and online wellness influencers.

Despite the hype, experts urge caution regarding the health claims associated with these products. Many functional foods contain nutrients or probiotics in quantities that are significantly lower than those examined in rigorous scientific studies. Nutrition authority Dr. Marion Nestle has commented that much of the marketing surrounding these foods lacks scientific backing and instead focuses on appealing to consumers.

The Burned Haystack Dating Method

Introduced by Dr. Jennie Young, the burned haystack dating method outlines a blunt approach to finding potential partners. The concept centers on blocking unsuitable matches, with guidelines that dictate limiting app time and minimizing pre-date messaging.

Critics contend that this approach may be overly harsh towards men and may not promote sufficient self-reflection among users. However, adherents argue that it has led them to experience fewer, but more worthwhile, dating encounters.

Vaginal Wellness

The booming vaginal probiotic market sells numerous supplements and suppositories that claim to enhance vaginal health and combat infections. The industry has thrived partly due to the stagnation of scientific advancements for conditions related to the vaginal microbiome, like bacterial vaginosis.

Although the demand is evident, evidence supporting the efficacy of available vaginal probiotic products remains weak. Some may even provoke gastrointestinal side effects.

Spermmaxxing

Similar to many wellness trends, "spermmaxxing" starts with genuine concerns but often leads to questionable recommendations. Studies indicate that men’s average sperm count has significantly decreased since the 1970s, largely due to lifestyle factors such as inadequate sleep and increased stress.

To boost sperm quality and quantity, influencers have suggested various outlandish solutions, from dietary changes to quirky Remedies. However, experts advise that the most effective strategies for male reproductive health remain simple: prioritize sleep, maintain a nutritious diet, and stay active.

AI, pet food for boys, and parasite detoxes: the 11 major wellness trends for 2026

A sunlit modern wellness kitchen flooded with warm natural light. On the left, a sleek tablet stands upright on the marble countertop, its screen showing “AI Wellness Coach” with simple nutritional icons. In the center, a minimalist ceramic bowl brims with colorful, kibble-like pellets labeled “Human Formula.” On the right, a light wooden shelf holds amber glass bottles and vials stamped “Parasite Cleanse.” In the background, potted greenery and a small chalkboard sign overhead announce “Top 11 Trends of 2026.”

AI, boy kibble and parasite cleanses: the 11 biggest wellness trends of 2026 - The Guardian

- AI is increasingly used in wellness, offering personalized guidance but often lacking accuracy.  
- Parasite cleanses gained popularity via social media, despite expert warnings about their ineffectiveness.  
- Functional foods are trendy, but their health claims may be exaggerated and lack scientific support.  

Every year, the wellness industry churns out new products and practices that it claims are finally – finally! – the real secret to being well. Sometimes, these trends are legitimate. Sometimes they’re utter nonsense. Often they’re somewhere in between.

These are the wellness trends that have provoked controversy and interest in 2026.

AI

AI has rapidly become part of every area of life, including wellness routines. More and more people are turning to chatbots for nutrition advice, fitness routines and even guidance on how to navigate relationships and grow as a person.

In May, Guardian readers shared how they have incorporated AI into their fitness routines. Some said the personalized information helped them stick to a routine and meet their fitness goals, but others acknowledged it was an imperfect tool. “It can be wrong, and confidently so,” said one AI user.

As for personal growth, AI can lower the barrier to self-reflection, experts say; chatbot prompts may help you more clearly define your questions and goals. But because these technologies are trained on large-scale, human-generated data, their recommendations risk being over-generic, and reinforcing “dominant cultural narratives, rather than what is meaningful for an individual”.

Read more on AI fitness

Parasite cleanses

In an interview last year, supermodel Heidi Klum revealed that she and her husband were undertaking a parasite cleanse because, as she put it, “everything on my Instagram feed at the moment is about worms and parasites.” The idea has trickled down through social media, where users have been making bold claims that deworming cleanses could help with everything from sleep, gut health, mood and skin.

While parasitic infections are real, and common in communities where people lack access to clean water and sanitation, experts say they are far less common in areas with good sanitation. What’s more, such infections cannot be cleared up by the herbal supplements hawked on TikTok. Parasitic infections are diagnosed with stool samples or blood tests, and often treated with antibiotic or antiparasitic medications.

Read more on parasite cleanses

Proteinmaxxing

Protein has been having a moment, and the usual sources – meat, eggs, dairy – aren’t cutting it any more. Increasingly, consumers are supplementing their regular intake – proteinmaxxing – with protein-fortified foods: powders, cereal and even snacks like popcorn and pretzels.

What’s the reason behind this seemingly bottomless appetite for a single macronutrient? Some credit it to the boom in GLP-1 medications: when people eat less, consuming an adequate amount of protein to maintain existing muscle becomes especially important.

Experts add that there is a ceiling of how much protein can help with building muscle – it’s generally recommended that most adults aim for 0.8g of protein per kg of body weight. And even for those serious about weightlifting and building muscle, protein intake and muscle synthesis seems to plateau above 1.6g per kg of body weight.

Read more on proteinmaxxing

Divorce rings

Sometimes known as “F-you rings”, divorce rings are the result of refashioning engagement and wedding rings after a marriage ends. The trend took off among celebrities too, with people like Emily Ratajkowski and Rachel Zoe flaunting their divorce rings.

“It’s not an F-you to an ex-husband, necessarily,” said one woman who reworked her wedding ring into new jewelry. “It’s an F-you to the expectations, to the norms, to this belief that a woman’s sole purpose is to be a man’s wife, not their own person.”

Read more on divorce jewelry

Olive oil and lemon juice

Though this combination sounds like the base of a nice salad dressing, social media influencers started drinking it as a shot, claiming it improves skin and digestion.

This isn’t complete nonsense. Olive oil in particular has a number of health benefits – it is a monounsaturated fat, meaning it helps lower bad cholesterol and keeps artery walls flexible and functional. It also contains polyphenols, a type of antioxidant that helps reduce inflammation.

It’s a relatively low-risk trend to try. But experts agree you’d probably be better off eating the mixture with a salad.

Read more on lemon shots

Hyrox

Every once in a while, a new fitness method comes along that completely consumes young professionals with free time and disposable income. Before, it was CrossFit. Now, there’s Hyrox.

Founded in 2017, Hyrox is a competition that occurs over several days. It consists of eight 1km runs and eight different workouts, including pulling and pushing sleds, farmer’s carry, burpee broad jumps and rowing. Training classes include sets of exercises that closely resemble these – and they’re hard. “I had hoped my baseline fitness and adrenaline would carry me through,” wrote Wilson Wong. “They did not.”

Read more on hyrox

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Boy kibble

“Boy kibble” describes a meal that has long been popular among fitness enthusiasts: rice, ground protein and some kind of vegetable.

“This is essentially old-school bodybuilding nutrition repackaged,” said one nutritionist. It is a fairly nutritious meal option when it combines the right amounts of protein, fat, carbohydrates and fiber. Simple meal structures like this one can be helpful for some people trying to meet their nutritional needs.

But it shouldn’t be the only meal structure one uses. Experts emphasized that other meals should provide a wider range of foods. Also, there are plenty of other meals that are equally nutritious, and a little more interesting.

Read more on boy kibble

Functional foods

It’s not enough any more for chocolate to be chocolate, or chips to be chips – these days, chocolate now offers PMS relief, and chips now have added protein.

So-called “functional foods” – foods that provide specific health benefits or enhance the body’s processes – are not new, but they have risen in popularity with the ascent of wellness influencers and increasingly health-conscious consumers. But experts agree functional food claims should be taken with a grain of salt. The amount of nutrients or probiotics added to functional foods tends to be far below what has been studied in clinical trials.

Nutrition expert Dr Marion Nestle says most of the claims made by these foods have “nothing to do with science. It’s about marketing.”

Read more on functional foods

The burned haystack dating method

Developed by Dr Jennie Young, a professor of rhetoric, the burned haystack dating method offers an answer to the question, “How do you find a needle in a haystack?”: You burn it down.

Applied to dating, this means blocking any person who doesn’t seem like a match. There are also 10 “rules of engagement” (for example, don’t spend too much time on the apps; don’t message for too long before meeting in person) and 33 red flag rhetorical patterns (for example, sending an inappropriately sexual first message). Critics argue the method is too unforgiving towards men, and that it doesn’t encourage enough self-reflection on the part of the person doing the burning. Followers of the method say it has helped them go on fewer but more promising dates.

Read more on burned haystack

Vaginal wellness

The billion-dollar vaginal probiotic industry sells supplements and suppository capsules which it claims can improve vaginal health and odor, and prevent infections.

It’s clear why the industry is booming. For decades, little scientific progress has been made in treatments for vaginal microbiome-related conditions such as bacterial vaginosis and urinary tract infections. But there is little evidence the vaginal probiotic supplements on the market now help, and some may cause gastrointestinal side-effects.

Read more on vaginal wellness

Spermmaxxing

Like many wellness trends, this one starts from a place of truth and then quickly goes off the rails.

Research has shown that men’s average sperm count and concentration has dropped significantly since the 1970s. Experts attribute this largely to modern lifestyle factors like poor sleep, stress and air pollution. To improve the quantity and quality of one’s sperm, online influencers have peddled a range of dubious solutions which they sometimes call “spermmaxxing”. These include eating more garlic and dipping your testicles in ice water.

But according to experts, the best thing men can do for their reproductive health is to keep it simple: get enough sleep, eat healthy and move your body.

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