Workplace Trends

California's gas generation has fallen by more than 60% in just three years. The charts below compare the average daily generation profile in June 2023 and June 2026. They don't show the entire electricity system, the focus is on solar, batteries, imports and gas because this is where the major changes have occurred. Sources such as nuclear, geothermal, wind and bioenergy have been excluded because their contribution has remained relatively consistent. In 2023, gas was one of California's largest sources of electricity throughout the day. By 2026, its role had shrunk dramatically, while batteries are now a major source of evening supply after charging from abundant daytime solar. The negative values in the middle of the day represent battery charging - soaking up increasing amounts of solar generation. In 2023 they also included some electricity exports. Several changes have occurred at once: ✅ So far in 2026, gas generation is down 62% compared with 2023 ✅ Solar generation has increased by 54%, while battery output has increased by more than 300% ✅ California is increasingly shifting abundant daytime solar into the evening using batteries, reducing the need for gas But this isn't simply a story about building more solar or more batteries. It's about how those technologies increasingly work together. Solar generates abundant electricity during the day, while batteries shift more of that electricity into the evening when demand remains high. As solar generation grows, storage is becoming an increasingly important part of the electricity system – steadily reducing the role that gas once played.

Visa & Mastercard are moving faster into stablecoins than anyone expected. And the scale is already massive… Stablecoins are no longer a side experiment, they’re becoming a core payments rail for the card networks. Here are numbers that matter: ► Visa’s stablecoin-linked card spend is up 4x YoY ► 130+ stablecoin card programs live in 40+ countries ► Mastercard: 100+ crypto card programs globally ► Rain’s stablecoin cards → $2B+ annualized spend ► Mastercard is reportedly in talks to acquire Zerohash for $2B: https://lnkd.in/d9Txg3Eb ► Visa already invested in stablecoin startup BVNK and custodian Anchorage What’s driving this? In markets like LATAM and Africa, banks struggle to access USD liquidity. Stablecoins fix that — instantly. And stablecoin-backed prepaid cards let users hold dollars and spend locally, with fintech apps doing the behind-the-scenes conversion. For merchants, the value is even clearer: Funds settle faster while interchange remains the same. Both networks are also quietly enabling banks to issue their own stablecoins, rather than competing with them, a strategic move to stay indispensable. Credit cards won’t be replaced anytime soon (on-chain credit isn’t there yet), but Visa is already studying crypto-backed credit products for the next wave. What’s also interesting: Visa & Mastercard aren’t launching their own stablecoins, they’re becoming the infrastructure helping banks launch theirs. A strategic move to stay indispensable without competing directly with issuers. At the same time, merchants love stablecoin settlement because funds arrive faster, even if interchange stays the same. And while stablecoins won’t replace credit yet (no on-chain credit model exists at scale), Visa is openly exploring crypto-backed credit cards, which they believe could be a “massive opportunity.” My take: This is no longer “crypto payments.” This is a new USD distribution model for the developing world, and the card networks are positioning themselves as the global rails for it. Five years from now, stablecoin-backed cards may be one of Visa’s and Mastercard’s biggest growth engines. What do you think? Find this helpful? [ 𝗿𝗲𝗽𝗼𝘀𝘁 ] Anything to add about this subject? [𝗶𝗻𝘃𝗶𝘁𝗲𝗱 𝘁𝗼 𝗰𝗼𝗺𝗺𝗲𝗻𝘁] Nice story, Marcel. Next! [ 𝗹𝗶𝗸𝗲 ] (It’s free, but means a lot to me 👍)  

🚨 BREAKING: Revolut Just Got MiCA licence to Sell Crypto to 450 Million Europeans Revolut has announced it secured a MiCA license & launches 1:1 stablecoin redemption at zero spread Coinbase got their MiCA license in June. OKX, Bybit, Crypto.com all have theirs. But Revolut just did something none of them can. --- They're launching "Crypto 2.0" across 30 EEA countries, which includes: → 280+ tokens → Zero-fee staking up to 22% APY (you keep 100% of yields) → RevolutX—their pro trading platform with 0% maker / 0.09% taker fees → **Direct 1:1 stablecoin-to-USD conversion with zero spread** That last line is the killshot. Most platforms bury 0.5-2% in the spread when you exit stablecoins. It's invisible profit on every conversion. Revolut just made it free. $1 in = $1 out. Every single time. --- MiCA demands this: full 1:1 backing, transparent reserves, monthly audits, real redemption rights at par value. But here's what makes Revolut different from every crypto exchange scrambling for compliance: They have 65 million customers globally. 14 million already trade crypto on their platform. Their wealth division grew 298% YoY on crypto activity alone. --- And now they have infrastructure no pure-play exchange can match: → European banking license (via Lithuania, ECB-supervised) → SEPA rails for instant settlement → KYC already complete on tens of millions → Regulatory clearance to scale crypto like a bank product The competition explains why their stablecoin reserves sit in offshore entities with quarterly attestations from firms nobody's heard of. Revolut's reserves will be in European banks. Audited by top-tier firms. Disclosed monthly. With EU banking supervision. --- This is what the crypto product looks like when it's built by people who've been planning for this since 2017. 450 million Europeans need to move between fiat and stablecoins without friction, without spread, without regulatory risk. Revolut now is best placed to be that rail. --- MiCA was supposed to slow everyone down. Instead, it just separated the fintechs who were building compliance into their infrastructure from the exchanges who thought they could add it later. Revolut was ready.

INDIA GOES OFFLINE, DIGITALLY! The Reserve Bank of India has launched the Offline Digital Rupee, a Central Bank Digital Currency that can move from one wallet to another even without internet or mobile network. Imagine paying for a cup of tea in the Himalayas or for groceries in a rural market where connectivity is zero and still completing the transaction in seconds. ✅ Digital trust has reached a new level. Money that works without the internet is not a product of convenience. It is the evolution of trust. When the value can move offline yet remain verified and authentic, we are witnessing the future of financial inclusion, not just technology. ✅ It solves the last-mile problem. For years, digital payments depended on networks, servers, and gateways. Rural India, remote areas, and even disaster zones were often left behind. The Offline Digital Rupee removes that dependency and gives digital money a physical character. This changes how we think of accessibility forever. ✅ It is faster, cheaper, and smarter. No third-party switches. No failed connections. No dependency on payment gateways. The value moves directly from one device to another, just like cash, but secured by blockchain-based architecture and backed by the central bank. The power of digital efficiency now exists without digital dependence. ✅ Programmable money means purposeful money. The RBI’s Programmable Central Bank Digital Currency model means money can be coded for a reason. Subsidies can be released only for their intended use. Corporate payouts can have specific validity. Social benefits can be tracked transparently. It adds responsibility to the currency itself. ✅ It redefines how economies will interact. Offline CBDC is not just a domestic innovation. It opens the door for new models of cross-border settlements, disaster-resilient financial systems, and new layers of fintech innovation. The world will look at this model as a live example of how technology can merge with human need, not just convenience. ✅ It reminds us what innovation truly means. The right innovation is not when a feature gets smarter, but when it becomes more inclusive. When a person in a no-network zone can transact as easily as someone in a metro city, that is when digital transformation turns into social transformation.

I’ve been headhunting in the CPG industry for the past decade, and I’ve never seen a post-inflation market like we’re in right now. For the past three years, customers have been capitulating to price hikes by extending their budgets. But now, they’re at a breaking point. American families, already tethering on edges of their budgets, do not have the ability or the desire to expand their budget in order to accommodate increased prices. I’m sure you’d agree with this, because my family certainly does. With grocery bills through the roof, we’d rather skip on groceries and essentials rather than paying a premium right now. A couple things led us here, starting the pandemic and the post-pandemic impact on spending and savings. Secondly, the wave of AI and tech developments that caught us off guard. So, where do the companies go now? Once the “price increase” playbook is done, CPG brands can only win in both value and volume by shifting gears. In my chats with executives, I’m sensing a change in tone. To stay competitive, they’re looking for ways to shift from the post-pandemic survival mindset to a growth-focused one that accommodates the customer as well. Rather than hiking prices, the focus is now on bringing down costs, and getting to terms with consumer’s limited budgets and increasing product choices. Layoffs aren’t the only way to bring down costs. In my view, CPG companies do have the leeway to embrace data-driven innovation and efficiency to cut costs. Here are some of the ways in which companies can use AI and ML to achieve targets in 2025 and beyond: 1/ Predicting the demand: Post-pandemic behavior is tough to predict, especially in CPG markets. With AI, the companies can now leverage real-time insights from sources like point-of-sale systems, social media, and even economic indicators to see future trends more clearly. PepsiCo, uses Tastewise to track what consumers are eating across 60+ million touchpoints and making decisions that align with local preference. 2/ Inventory management: With AI-powered predictive analytics, companies are now turning inventory management into a science. Procter & Gamble’s Supply Chain 3.0 initiative is one example of this shift. 3/ Increased personalization: Leaders are tapping into geographical intelligence to connect meaningfully with audiences. Estée Lauder has a voice-enabled makeup assistant for visually impaired customers, reaching a new market while boosting brand loyalty. Bottom line is: customers are no longer meeting brands where they’re at. It’s high time that companies start caring about customers and their shrinking bottom lines. Are you excited to see your grocery bill go down in the next few months? #CPG #AI #ML #fmcg #marketing #trending

HUGE AI LEGAL NEWS! The European Data Protection Board (EDPB) has published its much anticipated Opinion on AI and data protection. The opinion looks at 1) when and how AI models can be considered anonymous, 2) whether and how legitimate interest can be used as a legal basis for developing or using AI models, and 3) what happens if an AI model is developed using personal data that was processed unlawfully. It also considers the use of first and third-party data. The opinion also addresses the consequences of developing AI models with unlawfully processed personal data, an area of particular concern for both developers and users. The EDPB clarifies that supervisory authorities are empowered to impose corrective measures, including the deletion of unlawfully processed data, retraining of the model, or even requiring its destruction in severe cases. On the issue of anonymity, the opinion grapples with the question of whether AI models trained on personal data can ever fully transcend their origins to be considered anonymous. The EDPB highlights that merely asserting that an AI model does not process personal data is insufficient. Supervisory authorities (SAs) must assess claims of anonymity rigorously, considering whether personal data has been effectively anonymised in the model and whether risks such as re-identification or membership inference attacks have been mitigated. For AI developers, this means that claims of anonymity should be substantiated with evidence, including the implementation of technical and organisational measures to prevent re-identification. On legitimate interest as a legal basis for AI, the opinion offers detailed guidance for both development and deployment phases. Legitimate interest under Article 6(1)(f) GDPR requires meeting three cumulative conditions: pursuing a legitimate interest, demonstrating that processing is necessary to achieve that interest, and ensuring the processing does not override the fundamental rights and freedoms of data subjects. For third-party data, the opinion emphasises that the absence of a direct relationship with the data subjects necessitates stronger safeguards, including enhanced transparency, opt-out mechanisms, and robust risk assessments. The opinion’s findings stress that the balancing test under legitimate interest must consider the unique risks posed by AI. These include discriminatory outcomes, regurgitation of personal data by generative AI models, and the broader societal risks of misuse, such as through deepfakes or misinformation campaigns. The opinion also provides examples of mitigating measures that could tip the balance in favour of controllers, such as pseudonymisation, output filters, and voluntary transparency initiatives like model cards and annual reports. The implications for developers are significant: compliance failures in the development phase can render an entire AI system non-compliant, leading to legal and operational challenges.

Data Integration Revolution: ETL, ELT, Reverse ETL, and the AI Paradigm Shift In recents years, we've witnessed a seismic shift in how we handle data integration. Let's break down this evolution and explore where AI is taking us: 1. ETL: The Reliable Workhorse      Extract, Transform, Load - the backbone of data integration for decades. Why it's still relevant: • Critical for complex transformations and data cleansing • Essential for compliance (GDPR, CCPA) - scrubbing sensitive data pre-warehouse • Often the go-to for legacy system integration 2. ELT: The Cloud-Era Innovator Extract, Load, Transform - born from the cloud revolution. Key advantages: • Preserves data granularity - transform only what you need, when you need it • Leverages cheap cloud storage and powerful cloud compute • Enables agile analytics - transform data on-the-fly for various use cases Personal experience: Migrating a financial services data pipeline from ETL to ELT cut processing time by 60% and opened up new analytics possibilities. 3. Reverse ETL: The Insights Activator The missing link in many data strategies. Why it's game-changing: • Operationalizes data insights - pushes warehouse data to front-line tools • Enables data democracy - right data, right place, right time • Closes the analytics loop - from raw data to actionable intelligence Use case: E-commerce company using Reverse ETL to sync customer segments from their data warehouse directly to their marketing platforms, supercharging personalization. 4. AI: The Force Multiplier AI isn't just enhancing these processes; it's redefining them: • Automated data discovery and mapping • Intelligent data quality management and anomaly detection • Self-optimizing data pipelines • Predictive maintenance and capacity planning Emerging trend: AI-driven data fabric architectures that dynamically integrate and manage data across complex environments. The Pragmatic Approach: In reality, most organizations need a mix of these approaches. The key is knowing when to use each: • ETL for sensitive data and complex transformations • ELT for large-scale, cloud-based analytics • Reverse ETL for activating insights in operational systems AI should be seen as an enabler across all these processes, not a replacement. Looking Ahead: The future of data integration lies in seamless, AI-driven orchestration of these techniques, creating a unified data fabric that adapts to business needs in real-time. How are you balancing these approaches in your data stack? What challenges are you facing in adopting AI-driven data integration?

Louder for the people at the back 🎤 Many organisations today seem to have shifted from being institutions that develop great talent to those that primarily seek ready-made talent. This trend overlooks the immense value of individuals who, despite lacking experience, possess a great attitude, commitment, and a team-oriented mindset. These qualities often outweigh the drawbacks of hiring experienced individuals with a fixed and toxic mindset. The best organisations attract talent with their best years ahead of them, focusing on potential rather than past achievements. Let’s be clear this is more about mindset and willingness to learn and unlearn as apposed to age. To realise the incredible potential return, organisations must commit to creating an environment where continuous development is possible. This requires a multi-faceted approach: 1. Robust Training Programmes: Employers should invest in comprehensive training programmes that equip employees with the necessary skills for their roles. This includes on-the-job training, mentorship programmes, online courses, and workshops. 2. Redefining Hiring Criteria: Organisations should revise their hiring criteria to focus more on candidates’ potential and willingness to learn rather than solely on prior experience or formal qualifications. Behavioural interviews, aptitude tests, and probationary periods can help assess a candidate's ability to learn and adapt. 3. Partnerships with Educational Institutions: Companies can collaborate with educational institutions to design curricula that align with industry needs. Apprenticeship programmes, internships, and cooperative education can bridge the gap between academic learning and practical job skills. 4. Lifelong Learning Culture: Encouraging a culture of lifelong learning within organisations is crucial. Employers should provide ongoing education opportunities and support for professional development. This includes continuous skills assessment and access to resources for upskilling and reskilling. 5. Inclusive Recruitment Practices: Employers should implement inclusive recruitment practices that remove biases and barriers. Blind recruitment, diversity quotas, and targeted outreach programmes can help ensure that diverse candidates are given a fair chance. By implementing these measures, organisations can develop a workforce that is adaptable, innovative, and resilient, ensuring sustainable success and growth.

Should you try Google’s famous “20% time” experiment to encourage innovation? We tried this at Duolingo years ago. It didn’t work. It wasn’t enough time for people to start meaningful projects, and very few people took advantage of it because the framework was pretty vague. I knew there had to be other ways to drive innovation at the company. So, here are 3 other initiatives we’ve tried, what we’ve learned from each, and what we're going to try next. 💡 Innovation Awards: Annual recognition for those who move the needle with boundary-pushing projects. The upside: These awards make our commitment to innovation clear, and offer a well-deserved incentive to those who have done remarkable work. The downside: It’s given to individuals, but we want to incentivize team work. What’s more, it’s not necessarily a framework for coming up with the next big thing. 💻 Hackathon: This is a good framework, and lots of companies do it. Everyone (not just engineers) can take two days to collaborate on and present anything that excites them, as long as it advances our mission or addresses a key business need. The upside: Some of our biggest features grew out of hackathon projects, from the Duolingo English Test (born at our first hackathon in 2013) to our avatar builder. The downside: Other than the time/resource constraint, projects rarely align with our current priorities. The ones that take off hit the elusive combo of right time + a problem that no other team could tackle. 💥 Special Projects: Knowing that ideal equation, we started a new program for fostering innovation, playfully dubbed DARPA (Duolingo Advanced Research Project Agency). The idea: anyone can pitch an idea at any time. If they get consensus on it and if it’s not in the purview of another team, a cross-functional group is formed to bring the project to fruition. The most creative work tends to happen when a problem is not in the clear purview of a particular team; this program creates a path for bringing these kinds of interdisciplinary ideas to life. Our Duo and Lily mascot suits (featured often on our social accounts) came from this, as did our Duo plushie and the merch store. (And if this photo doesn't show why we needed to innovate for new suits, I don't know what will!) The biggest challenge: figuring out how to transition ownership of a successful project after the strike team’s work is done. 👀 What’s next? We’re working on a program that proactively identifies big picture, unassigned problems that we haven’t figured out yet and then incentivizes people to create proposals for solving them. How that will work is still to be determined, but we know there is a lot of fertile ground for it to take root. How does your company create an environment of creativity that encourages true innovation? I'm interested to hear what's worked for you, so please feel free to share in the comments! #duolingo #innovation #hackathon #creativity #bigideas

Friday marked exceptional volatility in precious metals markets, with gold falling ~12% and silver ~38% intraday — moves we rarely see outside of periods of acute market stress. The immediate catalyst was the nomination of Kevin Warsh as prospective #Fed Chair, which triggered broad profit-taking and renewed concerns around a potentially more hawkish policy path. Beyond the headlines, positioning and liquidity dynamics played a meaningful role in amplifying the sell-off. Our current view: #Gold: We do not see this as the end of the bull market. The broader monetary backdrop and structural demand drivers remain intact, and we do not expect a material shift in the Fed’s overall trajectory. In the near term, we believe consolidation in the USD 4,500–4,800/oz range is possible as positioning resets, but fundamentals remain supportive. Our mid-year target remains USD 6,200/oz. #Silver: Momentum had already begun to soften ahead of Friday’s repricing, despite strong ETF inflows and speculative interest that helped fuel last year’s rally. With volatility elevated and industrial demand facing potential headwinds, we believe it is still premature to establish long-term strategic exposure. For readers interested in the deeper analysis, my colleagues Wayne Gordon, Giovanni Staunovo, and Dominic Schnider expand on these themes in the detailed reports below.