Technology

𝗧𝗵𝗲 𝗽𝗮𝗿𝗮𝗱𝗼𝘅 𝗼𝗳 𝗺𝗼𝗱𝗲𝗿𝗻 𝗵𝗲𝗮𝗹𝘁𝗵 𝘁𝗲𝗰𝗵: 𝗧𝗵𝗲 𝗺𝗼𝗿𝗲 𝘄𝗲 𝗺𝗼𝗻𝗶𝘁𝗼𝗿, 𝘁𝗵𝗲 𝗺𝗼𝗿𝗲 𝗮𝗻𝘅𝗶𝗼𝘂𝘀 𝘄𝗲 𝗯𝗲𝗰𝗼𝗺𝗲. We track our bodies 24/7. Count every calorie. Measure sleep, HRV, glucose, stress. From Apple Watch. To Oura Ring. To the latest “temple” device. Somewhere along the way, awareness turned into obsession. Here’s the paradox no one talks about: We have the best health-tracking tools in history, and some of the worst health outcomes. Something doesn’t add up. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝗵𝗼𝘄𝘀 𝗦𝗹𝗲𝗲𝗽 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗰𝗮𝗻 𝘄𝗼𝗿𝘀𝗲𝗻 𝘀𝗹𝗲𝗲𝗽 Studies on orthosomnia (an obsession with “perfect” sleep metrics) show that people who fixate on sleep scores experience more sleep anxiety, lighter sleep, and poorer recovery—even when objective sleep doesn’t improve. Trying to optimize sleep can literally break it. 𝗛𝗥𝗩 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝘀 𝘀𝘁𝗿𝗲𝘀𝘀 𝗳𝗼𝗿 𝗺𝗮𝗻𝘆 𝘂𝘀𝗲𝗿𝘀 HRV is a useful trend marker—but daily fluctuations are normal. Research shows that constant HRV checking can heighten health anxiety and perceived stress, especially when users don’t understand variability or context. Ironically, stressing about HRV often lowers HRV. 𝗠𝗼𝗿𝗲 𝗱𝗮𝘁𝗮 ≠ 𝗯𝗲𝘁𝘁𝗲𝗿 𝗵𝗲𝗮𝗹𝘁𝗵 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 Behavioral science research consistently finds that excessive self-monitoring leads to hypervigilance, loss of bodily trust, and decision fatigue. When every sensation becomes a data point, people stop listening to internal cues and start deferring to dashboards. In short: 𝗢𝘃𝗲𝗿-𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝘀 𝗮𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀 𝘄𝗶𝘁𝗵 𝗮𝗻𝘅𝗶𝗲𝘁𝘆. So what actually creates health? The same fundamentals that worked 5,000 years ago: • Deep, peaceful sleep • Regular sunlight • Real, nourishing food • Daily movement • Time with people you love These don’t need algorithms. They need presence. Use wearables if they serve you—I do, occasionally. But don’t let them become your master. Your life isn’t an algorithm waiting to be optimized. It’s a system meant to be felt, explored, and course-corrected. The best health coach you’ll ever have is already inside you. Trust it.

Every cloud provider faces the same AI infrastructure challenge: chips need to be positioned close together to exchange data quickly, but they generate intense heat, creating unprecedented cooling demands. We needed a strategic solution that allowed us to use our existing air-cooled data centers to do liquid cooling without waiting for new construction. And it needed to be rapidly deployed so we could bring customers these powerful AI capabilities while we transition towards facility-level liquid cooling. Think of a home where only one sunny room needs AC, while the rest stays naturally cool – that’s what we wanted to achieve, allowing us to efficiently land both liquid and air-cooled racks in the same facilities with complete flexibility. The available options weren't great. Either we could wait to build specialized liquid-cooled facilities or adopt off-the-shelf solutions that didn't scale or meet our unique needs. Neither worked for our customers, so we did what we often do at Amazon… we invented our own solution. Our teams designed and delivered our In-Row Heat Exchanger (IRHX), which uses a direct-to-chip approach with a "cold plate" on the chips. The liquid runs through this sealed plate in a closed loop, continuously removing heat without increasing water use. This enables us to support traditional workloads and demanding AI applications in the same facilities. By 2026, our liquid-cooled capacity will grow to over 20% of our ML capacity, which is at multi-gigawatt scale today. While liquid cooling technology itself isn't unique, our approach was. Creating something this effective that could be deployed across our 120 Availability Zones in 38 Regions was significant. Because this solution didn't exist in the market, we developed a system that enables greater liquid cooling capacity with a smaller physical footprint, while maintaining flexibility and efficiency. Our IRHX can support a wide range of racks requiring liquid cooling, uses 9% less water than fully-air cooled sites, and offers a 20% improvement in power efficiency compared to off-the-shelf solutions. And because we invented it in-house, we can deploy it within months in any of our data centers, creating a flexible foundation to serve our customers for decades to come. Reimagining and innovating at scale has been something Amazon has done for a long time and one of the reasons we’ve been the leader in technology infrastructure and data center invention, sustainability, and resilience. We're not done… there's still so much more to invent for customers.

👁 Imagine losing your sight for 10 years… and then, the very first thing you do is recognize the faces of your loved ones again. That’s what happened to Jamal Furani, 78, thanks to a breakthrough in medical innovation: a fully synthetic cornea implant. No donor tissue. No immune rejection. A device that integrates directly with the eye’s own tissue. 💡 The deeper insight: The true revolution here isn’t only technological. It’s structural. Today, corneal blindness affects millions worldwide, but most can’t be treated because there simply aren’t enough donor corneas. A synthetic cornea changes the equation. It turns a scarce resource (donations) into a potentially unlimited one (innovation). And here’s what few realize: this implant doesn’t just restore vision. It restores autonomy, dignity, and human connection. Those are the “side effects” that make technology truly transformative. 👉 My take: The future of medicine won’t just be about “healing.” It will be about reinventing our organs — sometimes with solutions even better than the originals. If you could enhance or replace one organ with technology, which would you choose first? #Healthcare #Innovation #Biotech #FutureOfMedicine

Website traffic was a valuable metric correlated to growth. Now it may be a vanity metric, not correlated to growth. Search has been disrupted. Visits to your website are declining. So, marketers - what now? The search landscape was already shifting (I talked about this at INBOUND last year). Now, the change is accelerating dramatically: - AI Overviews appear in 43% of Google searches – when they do, organic CTR drops by nearly 35%. - Google’s AI Mode and audio AI overviews are coming – they will cause clicks to collapse further. - More buyers are using LLMs to find information, ChatGPT search in Europe grew 3.7x in six months. So, what should marketers do? And how can AI help? 1. Be everywhere and diversify your channels The days of relying solely on Google search are way over. You need to show up on YouTube, LinkedIn, Instagram, podcasts, and in niche communities. The good news? AI makes multi-channel, multi-format content creation scalable – even for small teams. 2. Be specific with context In the past, broad informational content was the way to rank in Google. Today, buyers expect results deeply relevant to them, whether they’re on Google, LLMs, or Reddit. You need specific content that reflects your expertise and resonates with your buyers. 3. Optimize for conversion, not clicks Traffic was once the lever you could pull. Now, conversion is where the opportunity lies. AI enables you to deliver personal messages that drive better conversion. Don’t ask, “How do we get more blog visits?” Ask, “How do we convert more prospects into customers across all channels?” The changes in search are sending shockwaves across marketing teams and media companies everywhere. The era of traffic-based marketing is ending. But a new era full of opportunity is just beginning. Super exciting times for marketers to reinvent the playbook!

By 2030, these 11 abilities will decide who gets hired Most don’t show up on resumes yet. The World Economic Forum just revealed the top skills for 2030 in the Future of Jobs Report 2025. And it’s a wake-up call. Today's celebrated tech skills? AI will do those better by 2026. Those certifications? Outdated in 18 months. But here's the good news: The skills that matter most in 2030? Technology can't replace them. Start mastering these skills to stay relevant and be recognized: 1. AI and Big Data 🤖 ❌ Passively watch AI replace jobs ✅ Make AI your competitive edge → Use AI to automate weekly reports → Build self-updating dashboards and summaries 2. Analytical Thinking 🧠 ❌ Drown in opinions and noise ✅ Let data drive key decisions → Identify root causes before reacting → Monitor metrics that reveal blind spots 3. Resilience, Flexibility and Agility 🐆 ❌ Break down under shifting priorities ✅ Adapt fast and lead through change → Stay steady during messy execution → Pause, breathe, ask: “What’s the next best move now?” 4. Motivation and Self-Awareness 👤 ❌ Burn out chasing urgency ✅ Work in sync with your energy → Track your energy every 3 hours for a week → Schedule focus work when your mind feels sharp 5. Curiosity and Lifelong Learning 🔍 ❌ Stick to your job description ✅ Learn a complementary skill to your role → If you're in marketing, study basic product design → If you're in finance, explore storytelling with data 6. Leadership and Social Influence 🌟 ❌ Rely on your title for respect ✅ Build trust by how you think, speak and act → Explain why you made a tough call, not just what you decided → Share a client insight that helped your team level up 7. Technological Literacy 💻 ❌ Run to the IT helpdesk for every issue ✅ Build and adapt your own stack → Automate one repetitive workflow today using AI → Use familiar tools more efficiently (Excel, Slack) 8. Systems Thinking 🔧 ❌ React to broken processes ✅ Design workflows that scale → Improve one repeated but inefficient process this week → Ask: “Can this run without me?” 9. Empathy and Active Listening 🎧 ❌ Talk to be heard ✅ Listen to support, inspire and lead → Listen without needing to speak more in 1:1s → Decode what’s really being said 10. Creative Thinking 🎨 ❌ Wait for inspiration ✅ Build innovation into routine → Ask: “What’s another way to solve this?” → Try a small change to test a new idea 11. Talent Management 👥 ❌ Try to do it all ✅ Delegate and develop future leaders → List 3 tasks to delegate now → Improve hiring processes to onboard the right talent 💡 It’s not about doing more. It’s about evolving how you think, lead, and grow. Because the future expects you to. Which one are you focusing on this month? -- ♻ Share this with someone you’d want on your 2030 team. ➕ Follow me (Meera Remani) for future-ready leadership strategies. 🔔 My best insights for transforming your leadership career? Join my exclusive email list. Link below.

𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆, 𝘆𝗼𝘂 𝗳𝗶𝗿𝘀𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝗼𝗹𝗶𝗱 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗲𝗻𝗳𝗼𝗿𝗰𝗲 𝘀𝘁𝗿𝗶𝗰𝘁 𝗱𝗮𝘁𝗮 𝗵𝘆𝗴𝗶𝗲𝗻𝗲. Getting your house in order is the foundation for delivering on any AI ambition. The MIT Technology Review — based on insights from 205 C-level executives and data leaders — lays it out clearly: 𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗱𝗼 𝗻𝗼𝘁 𝗳𝗮𝗰𝗲 𝗮𝗻 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘆 𝗳𝗮𝗰𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗿𝗶𝘀𝗸 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Therefore, many firms are still stuck in pilots, not production. Changing that requires strong data foundations, scalable architectures, trusted partners, and a shift in how companies think about creating real value with AI. Because pilots are easy, BUT scaling AI across the enterprise is hard. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: ⬇️ 1. 95% 𝗼𝗳 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 — 𝗯𝘂𝘁 76% 𝗮𝗿𝗲 𝘀𝘁𝘂𝗰𝗸 𝗮𝘁 𝗷𝘂𝘀𝘁 1–3 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀:   ➜ The gap between ambition and execution is huge. Scaling AI across the full business will define competitive advantage over the next 24 months. 2. 𝗗𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗹𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀: ➜ Without curated, accessible, and trusted data, no AI strategy can succeed — no matter how powerful the models are. 3. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗮𝗿𝗲 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 — 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗴𝗼𝗼𝗱 𝘁𝗵𝗶𝗻𝗴:   ➜ 98% of executives say they would rather be safe than first. Trust, not speed, will win in the next AI wave. 4. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱, 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝘄𝗶𝗹𝗹 𝗱𝗿𝗶𝘃𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗲:  ➜ Generic generative AI (chatbots, text generation) is table stakes. True differentiation will come from custom, domain-specific applications. 5. 𝗟𝗲𝗴𝗮𝗰𝘆 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗱𝗿𝗮𝗴 𝗼𝗻 𝗔𝗜 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻𝘀:  ➜ Firms sitting on fragmented, outdated infrastructure are finding that retrofitting AI into legacy systems is often more costly than building new foundations. 6. 𝗖𝗼𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗿𝗲 𝗵𝗶𝘁𝘁𝗶𝗻𝗴 𝗵𝗮𝗿𝗱: ➜ From GPUs to energy bills, AI is not cheap — and mid-sized companies face the biggest barriers. Smart firms are building realistic ROI models that go beyond hype. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗳𝘂𝘁𝘂𝗿𝗲-𝗿𝗲𝗮𝗱𝘆 𝗔𝗜 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗵𝗮𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗺𝗼𝗱𝗲𝗹 𝗿𝗲𝗹𝗲𝗮𝘀𝗲.   𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 — 𝗱𝗮𝘁𝗮, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗥𝗢𝗜 — 𝘁𝗼𝗱𝗮𝘆.

#Batteries are starting to dominate the evening peak in California's grid, charging up with daytime solar then discharging as solar ramps down. On 5th April they set another new record for share of supply, peaking at over 34% at 7pm. This represents a rapid progression - two years ago the record was just 13%. And they remained the largest source of supply on the grid from 6:35pm until 9:40pm. As more and more battery storage enters the mix, batteries will continue to play an increasing role in the state's grid, and continue to break more records. They are flexible and extremely quick to respond. By charging in the middle of the day they are soaking up excess solar and are then putting this to good use later, reducing the need for gas and imports in the nighttime hours. From just 0.5 GW in 2018, by late 2024 California already had over 13 GW of battery storage capacity, with more on the way. While that may sound like a lot, there is still some way to go with the California Energy Commission estimating the state will need around 52 GW of battery storage to meet it's 2045 target of getting all its power from carbon-free sources. Batteries will play an important role in the decarbonised grid of the future. As prices continue to fall we will see more and more batteries deployed, and are certainly seeing this happen in Australia - especially Western Australia. We are just on the cusp of much more widespread adoption. Onwards and upwards! #energy #sustainability #renewables #energytransition

A market map with 10,000 companies is impossible to prioritize. These are the 300 to know. I was a VP of Product in sales tech. And I was frustrated with the maps I found. So I've been studying the space and speaking with experts. Here's the players you need to know: — ONE - Core: Revenue Operating System This is your CRM, your system of record - where your sales operation begins. I break this into 3 segments: Enterprise Platforms → Built for large organizations with complex workflows and high-volume deals → Salesforce, Oracle, Microsoft Dynamics 365, SAP Growth-Stage Solutions → Designed for growing businesses that need scalable tools but with flexibility to adapt → HubSpot, Pipedrive, Zoho CRM, SugarCRM Modern CRMs → Startups and fast-scaling companies looking to move fast without rigid systems rely on modern CRMs. → Attio, Affinity, Close.io, Copper, Freshsales. — LAYER TWO - Engagement & Intelligence These tools power outbound outreach, automate sequences, and provide real-time data on prospects: → Outreach, Salesloft, VanillaSoft, Groove Engagement tools ensure your team hits the right prospect at the right time. — LAYER THREE - Revenue Acceleration These platforms shorten deal cycles: → Gong, Salesloft, Chorus.ai, Ebsta With real-time feedback and actionable insights... — LAYER FOUR - Data & Enrichment Your outreach is only as good as the data backing it. These platforms ensure you’re reaching out to right prospects. → ZoomInfo, Apollo.io, Clearbit, Lusha, Hunter io, Cognism — SATELLITE CLUSTERS - Modern GTM Stack These tools enhance parts of the GTM journey. AI-Enhanced Tools → Automate and personalize content creation at scale. → Writer, Grammarly, CopyAI, Jasper Product-Led Motion → Identify sales-ready leads through product engagement. → Pocus, Intercom, Breyta Sales Enablement → Equip sales teams with training, resources, and playbooks to perform at their best. → Seismic, Spekit, Allego Conversational GTM → Convert prospects directly through real-time chat. → Drift (now part of Salesloft) — SATELLITE CLUSTERS- Emerging Categories These are adjacent categories sales teams often still use. Product Analytics → Track user behaviors post-sale for better upsell and retention opportunities. → Amplitude, Mixpanel Customer Success → Ensure long-term customer retention and success beyond the initial sale. → Gainsight, Catalyst, Totango Workspace Integration → Enable seamless collaboration across sales and operations. → Notion, Slack, Airtable, monday.com Revenue Orchestration → Connect workflows across different systems to streamline revenue operations. → NektarAI, Tray.io, Workato, Boomi — This took a lot of time. Reshare ♻️ if you loved this post. What tools would you add?

Using light as a neural network, as this viral video depicts, is actually closer than you think. In 5-10yrs, we could have matrix multiplications in constant time O(1) with 95% less energy. This is the next era of Moore's Law. Let's talk about Silicon Photonics... The core concept: Replace electrical signals with photons. While current processors push electrons through metal pathways, photonic systems use light beams, operating at fundamentally higher speeds (electronic signals in copper are 3x slower) with minimal heat generation. It's way faster. While traditional chips operate at 3-5 GHz, photonic devices can achieve >100 GHz switching speeds. Current interconnects max out at ~100 Gb/s. Photonic links have demonstrated 2+ Tb/s on a single channel. A single optical path can carry 64+ signals. It's way more energy efficient. Current chip-to-chip communication costs ~1-10pJ/bit. Photonic interconnects demonstrate 0.01-0.1pJ/bit. For data centers processing exabytes, this 200x improvement means the difference between megawatt and kilowatt power requirements. The AI acceleration potential is revolutionary. Matrix operations, fundamental to deep learning, become near-instantaneous: Traditional chips: O(n²) operations. Photonic chips: O(1) - parallel processing through optical interference. 1000×1000 matmuls in picoseconds. Where are we today? Real products are shipping: — Intel's 400G transceivers use silicon photonics. — Ayar Labs demonstrates 2Tb/s chip-to-chip links with AMD EPYC processors. Performance scales with wavelength count, not just frequency like traditional electronics. The manufacturing challenges are immense. — Current yield is ~30%. Silicon's terrible at emitting light and bonding III-V materials to it lowers yield — Temp control is a barrier. A 1°C change shifts frequencies by ~10GHz. — Cost/device is $1000s To reach mass production we need: 90%+ yield rates, sub-$100 per device costs, automated testing solutions, and reliable packaging techniques. Current packaging alone can cost more than the chip itself. We're 5+ years from hitting these targets. Companies to watch: ASML (manufacturing), Intel (data center), Lightmatter (AI), Ayar Labs (chip interconnects). The technology requires major investment, but the potential returns are enormous as we hit traditional electronics' physical limits.

We're moving away from charging for *access* to software and toward a model of charging for the *work delivered* by a combination of software and AI agents. Let’s dive into what’s happening and what it means for you ⤵️ 1. The rise of disruptive AI pricing models Tech companies are realizing they can't solely rely on seat-based subscriptions in an age of AI, automation and APIs where value is disconnected with how many people are logging in. Perhaps Salesforce going all-in on Agentforce (and charging $2 per conversation) was the push the industry needed. Each product category has its own flavor of disruptive pricing. - Legal AI products might charge for a demand package generated by AI or an AI-generated summary. - Creator AI products might charge for the content that gets produced such as a video generation or amount of video created. - GTM products might charge for specific tasks completed or workflows executed by the AI. 2. Selling work, not necessarily success As a customer, I wish I only had to pay for software when it delivered results. But the reality is that true success-based billing won’t work for the vast majority of today’s products. Most products should charge for work output instead. The issue is attribution. You want the customer to get a fantastic outcome — and you want them to recognize that your product powered that outcome. As soon as you start charging for success, the customer begins to rethink the results. 3. Goodbye ARR as we know it? Shifting to these newer value-based pricing models isn't a simple pricing change you can just announce in a press release. It's a business model evolution that looks a lot like the shift from on-prem to SaaS in the first place. These new AI pricing models might mean greater volatility in both usage and spend. Variable margin profiles across products and customers. Seasonal revenue fluctuations. The potential for project-based, non-recurring use cases. Put simply, annual recurring revenue (ARR) continues to get dethroned. — Full post in today’s Growth Unhinged newsletter: https://lnkd.in/ea5eTrVD Things are about to get interesting 🍿 #ai #pricing #saas