 I set myself an exciting challenge today: a 60-minute AI-powered creative writing sprint. What you're about to read is the result of this experiment, generated using GenAI tools with minimal edits. While the process was AI-assisted, the content genuinely reflects my current thoughts and ideas. This entire journey—from initial concept to final publication—took less than an hour.
What you're about to read is the result of an hour-long iterative process. I've refined the content, ensured proper formatting, and verified overall coherence. To achieve a more natural, human-like tone, I utilized a combination of AI tools including Claude 3.5, Grok, and GPT-4o. This blend of AI assistance helped create a cohesive final product. The AI-Powered Product Engineer: Becoming an Invincible Force in Product DevelopmentThe role of product managers has always been multifaceted, demanding a blend of business acumen, technical knowledge, and creative problem-solving. Now, the rapid advancement of artificial intelligence is reshaping this landscape, offering product managers an unprecedented opportunity to enhance their capabilities across all aspects of product development. Generative AI emerges as a powerful 'exoskeleton' for product managers, augmenting human ingenuity rather than replacing it. This technology enables product managers to become a formidable combination of designer, software engineer, and strategic thinker, all in one. This article explores how generative AI empowers product managers to become versatile forces in design, development, testing, and marketing. We'll examine how this synergy of human expertise and AI capabilities is transforming the product creation and launch process, ushering in a new era of efficiency and innovation in product management. Design and Prototyping Design and prototyping are pivotal stages in product development, transforming abstract ideas into tangible models. Generative AI is now revolutionizing this process, offering product managers powerful new tools to approach design and prototyping. AI-enhanced platforms like Figma, Canva, Adobe Firefly (integrated throughout the Creative Suite), MidJourney, and Stable Diffusion are enabling PMs to create and iterate on prototypes at unprecedented speeds. This acceleration significantly reduces the time and resources needed for initial prototyping, while simultaneously expanding the range of design possibilities that can be explored. These advanced tools offer features such as auto-layouts, smart suggestions, and responsive resizing, dramatically shortening the journey from concept to model. For instance, Figma's AI-driven auto-layout feature dynamically adjusts components, ensuring impeccable alignment and spacing in designs. Tools like Adobe's Sensei, are empowering product managers to create high-fidelity mockups and prototypes with minimal technical design expertise, effectively bridging the gap between ideation and implementation. This democratization of design allows product managers to communicate their vision more effectively and iterate rapidly based on stakeholder feedback. Moreover, AI can generate design alternatives and predict user preferences, enabling data-driven design decisions. By analyzing extensive datasets of user interactions and preferences, AI can recommend design elements likely to resonate with target audiences. This not only accelerates the design process but also enhances the quality and user-friendliness of the final product. In essence, generative AI is transforming product managers into more versatile and efficient designers, allowing them to bring their visions to life with greater speed and precision than ever before. Coding and Development Is it just me or is it all moving to no-code slowly? Just look at all the recent examples of features added to notable LLM playgrounds:
I find all of these very useful for experimenting quickly with ideas. There is no ultimate playground yet but it's getting easier to experiment with LLMs. Generative AI is transforming the coding and development landscape, offering powerful tools that assist in writing and debugging code. While product managers aren't expected to replace software engineers, AI is enabling them to contribute more substantively to the development process. AI-powered code generation tools such as GitHub Copilot, Tabnine, and OpenAI's Codex are empowering product managers to create basic prototypes and proof-of-concepts without extensive coding expertise. These tools enhance coding efficiency and reduce errors, allowing PMs to engage more directly with the technical aspects of product development. A recent study suggests that AI coding assistants can potentially reduce development time by up to 30%, facilitating faster product cycles. By automating repetitive coding tasks and identifying potential bugs early in the development process, these tools free up product managers to focus on higher-level problem-solving and innovation. This AI-augmented approach to coding enables product managers to bridge the gap between conceptual design and technical implementation more effectively. It allows for rapid iteration and testing of ideas, potentially leading to more innovative and refined products. Product Management At the heart of product management is the ability to make informed decisions about product direction and feature prioritization. In this domain, AI is proving to be an invaluable ally. Machine learning algorithms can analyze vast amounts of market data, user behavior, and industry trends, providing product managers with actionable insights for strategic decision-making. Generative AI can assist in creating comprehensive product roadmaps by suggesting potential features based on market trends and user needs, and help prioritize these features by simulating different scenarios and predicting their potential impact on user satisfaction and business metrics. Product Testing and User Feedback. AI-driven user testing simulations are revolutionizing product validation before launch. These simulations can model thousands of user interactions, helping product managers identify potential usability issues or feature gaps that might be overlooked in traditional testing methods. Generative AI can also help prioritize bug fixes by analyzing the impact and frequency of issues, enabling PMs to allocate resources effectively and address critical bugs promptly, leading to improved product quality and faster iteration cycles. Post-launch, AI-powered sentiment analysis tools can process feedback from various channels to provide a holistic view of user satisfaction and pain points, enabling product managers to respond quickly to user needs and continuously improve the product. Product Launch and Marketing. Successful product launches require well-coordinated marketing efforts and strategies. AI-powered workflows can generate marketing copy, create visuals, and produce video content, allowing product managers to rapidly develop and test different marketing materials. AI tools like Copy.ai, Jasper, and AI-driven social media management platforms can automate content creation, campaign management, and customer engagement. AI can also analyze user data to create personalized launch strategies, ensuring that the right message reaches the right audience at the optimal time, a level of personalization previously unattainable without significant time and resource investment. Continuous Improvement and Iteration. The product manager's role extends beyond launch. AI-powered analytics tools can continuously monitor product performance, user engagement, and market trends, providing real-time insights for ongoing improvement. AI tools for sentiment analysis and user behavior tracking offer valuable insights into product interaction. Platforms like Qualtrics and Medallia analyze customer feedback from various channels, identifying trends and areas for improvement or emerging user needs. This enables product managers to stay ahead of the curve, continuously iterating and improving their product to maintain a competitive edge. Challenges and Considerations. While AI's potential in product management is immense, its implementation must be approached thoughtfully. Ethical concerns, particularly around data privacy and algorithmic bias, need careful consideration. Product managers must ensure responsible use of AI tools and maintain transparent and accountable decision-making processes. There's also the challenge of balancing AI assistance with human creativity and intuition. While AI can provide valuable insights and automate many tasks, the uniquely human qualities of empathy, creativity, and strategic thinking remain irreplaceable in product management Conclusion Generative AI is revolutionizing the role of product managers, enhancing their efficiency and versatility to unprecedented levels. This transformation represents a paradigm shift in product conception, development, and improvement. By leveraging AI as an 'exoskeleton,' product managers can transcend traditional role boundaries, becoming more adaptable, efficient, and effective in driving product success. However, this AI-augmented future of product management isn't about replacing human skills, but rather enhancing them. The most successful product managers will be those who can effectively collaborate with AI, using it to amplify their creativity, decision-making, and strategic thinking. Looking ahead, we anticipate a landscape where AI not only assists but also anticipates the needs of PMs, providing intelligent suggestions and automating routine tasks. This will enable PMs to focus more on strategic decision-making and innovation, leading to the creation of superior products and enhanced user experiences. As AI continues to evolve, product managers will need to continuously upskill to remain relevant. By embracing this AI-driven transformation, product managers can become the driving force behind the next wave of technological advancement. This future keeps human ingenuity at its core, amplified and empowered by artificial intelligence. For product managers willing to embrace this change, the possibilities are boundless. The synergy between human expertise and AI capabilities promises to unlock new levels of innovation and efficiency in product management, paving the way for groundbreaking advancements in the field.  Here is the Claude 3.5 result (1 single gen)The AI-Powered Product Manager: Becoming an Invincible Force in Product Development I. Introduction The role of product managers has always been multifaceted, requiring a blend of business acumen, technical knowledge, and creative problem-solving. However, the rapid advancement of artificial intelligence is reshaping this landscape, offering product managers an unprecedented opportunity to enhance their capabilities across all aspects of product development. Enter generative AI - the new "exoskeleton" for product managers. This powerful technology is not replacing human ingenuity but rather augmenting it, allowing product managers to become an invincible combination of designer, software engineer, and strategic thinker all rolled into one. II. Design and Prototyping Generative AI is revolutionizing the way product managers approach design and prototyping. By leveraging visual AI tools like DALL-E 2 and Midjourney, product managers can rapidly generate and iterate on design concepts, significantly reducing the time and resources required for initial prototyping. These AI-powered tools not only speed up the ideation process but also allow for the exploration of a wider range of design possibilities. AI-assisted design tools like Figma's AI features and Adobe's Sensei are empowering product managers to create high-fidelity mockups and prototypes with minimal technical design skills, effectively bridging the gap between ideation and implementation. This democratization of design enables product managers to communicate their vision more effectively and iterate faster based on stakeholder feedback. III. Software Engineering and Development While product managers aren't expected to replace software engineers, AI is enabling them to contribute more meaningfully to the development process. AI-powered code generation tools like GitHub Copilot and OpenAI's Codex are making it possible for product managers to create basic prototypes and proof-of-concepts without extensive coding knowledge. Furthermore, AI is revolutionizing testing and debugging processes. Automated testing tools powered by machine learning can identify potential bugs and performance issues early in the development cycle, allowing product managers to address problems proactively and ensure a higher quality product at launch. IV. Product Management At the core of product management lies the ability to make informed decisions about product direction and feature prioritization. AI is proving to be an invaluable ally in this domain. Machine learning algorithms can analyze vast amounts of market data, user behavior, and industry trends to provide product managers with actionable insights for strategic decision-making. Generative AI can assist in creating comprehensive product roadmaps by suggesting potential features based on market trends and user needs. It can also help prioritize these features by simulating different scenarios and predicting their potential impact on user satisfaction and business metrics. V. Product Testing and User Feedback AI-driven user testing simulations are transforming the way products are validated before launch. These simulations can model thousands of user interactions, helping product managers identify potential usability issues or feature gaps that might have been missed in traditional testing methods. Once a product is in users' hands, AI-powered sentiment analysis tools can process feedback from various channels - social media, support tickets, app store reviews - to provide a holistic view of user satisfaction and pain points. This enables product managers to respond quickly to user needs and continuously improve the product. VI. Product Launch and Marketing Launching a product successfully requires a well-coordinated marketing effort. Generative AI is proving to be a game-changer in this arena. AI tools can generate marketing copy, create visuals, and even produce video content, allowing product managers to rapidly develop and test different marketing materials. Moreover, AI can analyze user data to create personalized launch strategies, ensuring that the right message reaches the right audience at the right time. This level of personalization was previously unattainable without significant time and resource investment. VII. Continuous Improvement The product manager's job doesn't end at launch. AI-powered analytics tools can continuously monitor product performance, user engagement, and market trends, providing real-time insights for ongoing improvement Machine learning algorithms can analyze user behavior patterns to suggest feature improvements or identify emerging user needs. This enables product managers to stay ahead of the curve, continuously iterating and improving their product to maintain a competitive edge. VIII. Challenges and Considerations While the potential of AI in product management is immense, it's crucial to approach its implementation thoughtfully. Ethical concerns, particularly around data privacy and algorithmic bias, need to be carefully addressed. Product managers must ensure that AI tools are used responsibly and that decision-making processes remain transparent and accountable. There's also the challenge of balancing AI assistance with human creativity and intuition. While AI can provide valuable insights and automate many tasks, the uniquely human qualities of empathy, creativity, and strategic thinking remain irreplaceable in product management. IX. Conclusion The integration of AI into product management represents a paradigm shift in how products are conceived, developed, and improved. By leveraging AI as an "exoskeleton," product managers can transcend traditional role boundaries, becoming more versatile, efficient, and effective in driving product success. However, this AI-augmented future of product management isn't about replacing human skills but enhancing them. The most successful product managers will be those who can effectively collaborate with AI, using it to amplify their creativity, decision-making, and strategic thinking. X. Future Outlook Looking ahead, we can expect AI to become even more deeply integrated into product management workflows. We may see the emergence of AI assistants specifically designed for product managers, capable of providing real-time advice on product decisions, automatically generating reports, and even participating in brainstorming sessions. As AI continues to evolve, product managers will need to continuously upskill to stay relevant. This might involve developing a deeper understanding of AI and machine learning concepts, as well as honing uniquely human skills that complement AI capabilities. The future of product management is undoubtedly AI-powered, but it's a future where human ingenuity remains at the core, amplified and empowered by artificial intelligence. For product managers willing to embrace this change, the possibilities are truly limitless. Here is the GPT4-o result (2 gens)Harnessing Generative AI: The Ultimate Exoskeleton for Product Managers Introduction In today's fast-paced tech environment, product managers (PMs) are expected to wear multiple hats, seamlessly integrating roles of designers, developers, and marketers. Generative AI emerges as a powerful exoskeleton, enabling PMs to amplify their capabilities across these domains. This article explores how generative AI can empower product managers to become a multi-faceted force in design, development, testing, and marketing, transforming the way products are created and launched. The Role of Generative AI in Design and Prototyping Design and prototyping are critical stages in product development where ideas are transformed into tangible models. Generative AI tools like Figma and Adobe XD, enhanced with AI capabilities, allow PMs to create and iterate on prototypes swiftly. These tools offer features such as auto-layouts, smart suggestions, and responsive resizing, which significantly reduce the time from concept to model. For example, Figma's AI-driven auto-layout feature allows for dynamic adjustment of components, ensuring that designs are always perfectly aligned and spaced. Moreover, AI can assist in generating design alternatives and predicting user preferences, enabling PMs to make data-driven design decisions. By analyzing large datasets of user interactions and preferences, AI can suggest design elements that are likely to resonate with target audiences. This not only speeds up the design process but also enhances the quality and user-friendliness of the final product. Coding and Development with AI Assistance Generative AI is revolutionizing the coding and development landscape by providing powerful tools that assist in writing and debugging code. AI coding assistants such as GitHub Copilot, TabNine, and OpenAI Codex empower PMs to write code more efficiently and with fewer errors. These tools leverage large language models to understand and generate code, offering real-time suggestions and auto-completions. According to a recent study by McKinsey, AI coding assistants can reduce development time by up to 30%, enabling faster product cycles. By automating repetitive coding tasks and identifying potential bugs early in the development process, these tools help PMs focus on higher-level problem-solving and innovation. For instance, GitHub Copilot can suggest entire blocks of code based on simple comments, allowing PMs to quickly implement features and functionalities. This not only accelerates the development process but also ensures that the code is clean and consistent with industry best practices. AI in Product Testing and Quality Assurance Product testing and quality assurance are essential for delivering reliable and high-quality products. Generative AI tools like Testim.io and Applitools are transforming these stages by automating testing processes and enhancing bug detection capabilities. These tools use AI to create and execute test cases, identify anomalies, and predict potential issues. Automated testing tools can simulate thousands of user interactions, ensuring that the product performs well under various conditions. For example, Applitools uses AI-driven visual validation to detect visual bugs that traditional testing methods might miss. By comparing screenshots of the application under test with a baseline image, it can identify even the smallest visual discrepancies. Furthermore, generative AI can help prioritize bug fixes by analyzing the impact and frequency of issues. This enables PMs to allocate resources effectively and ensure that critical bugs are addressed promptly, leading to improved product quality and faster iteration cycles. Marketing and Launch Strategies Powered by AI Generative AI is also making significant strides in the realm of marketing and product launch strategies. AI tools like Copy.ai, Jasper, and AI-driven social media management platforms can automate content creation, campaign management, and customer engagement. These tools leverage natural language processing and machine learning to generate compelling marketing copy, create personalized email campaigns, and manage social media posts. For instance, Copy.ai can generate product descriptions, blog posts, and ad copy tailored to specific audiences, significantly reducing the time and effort required for content creation. By analyzing customer data and behavior, these tools can also suggest optimal times for posting on social media, target specific audience segments, and track campaign performance in real-time. AI-driven marketing strategies have been shown to increase engagement and conversion rates. For example, a recent campaign by a leading e-commerce company used AI to personalize email content based on user preferences, resulting in a 20% increase in click-through rates and a 15% increase in sales. Continuous Improvement and Iteration One of the most powerful aspects of generative AI is its ability to facilitate continuous improvement and iteration based on user feedback. AI tools for sentiment analysis and user behavior tracking can provide valuable insights into how users interact with a product. Tools like Qualtrics and Medallia analyze customer feedback from various channels, identifying trends and areas for improvement. By leveraging AI-driven insights, PMs can make data-informed decisions to enhance the product incrementally. For example, sentiment analysis can reveal common pain points and feature requests, guiding the development of new functionalities. Additionally, AI can help prioritize improvements based on their potential impact on user satisfaction and retention. Companies excelling in continuous improvement through AI include Spotify and Netflix, which use AI to analyze user behavior and preferences, constantly refining their algorithms and user interfaces to enhance user experience. Conclusion Generative AI is transforming the role of product managers, making them more efficient and versatile than ever before. By leveraging AI in design, development, testing, and marketing, PMs can drive faster product cycles, improve quality, and create more effective marketing campaigns. Over the next 12 months, we can expect AI tools to become even more integrated, offering seamless workflows that further enhance productivity and innovation. Next Steps:
Vision for the Next 12 Months: The future of product management lies in the seamless integration of AI across all stages of product development and lifecycle management. We envision a landscape where AI not only assists but also anticipates the needs of PMs, providing intelligent suggestions and automating repetitive tasks. This will allow PMs to focus more on strategic decision-making and innovation, leading to the creation of superior products and enhanced user experiences. By embracing this AI-driven transformation, product managers can truly become the invincible force driving the next wave of technological advancement. By following these steps and embracing the potential of generative AI, product managers can ensure they stay ahead of the curve, leading their teams and products to greater success in an increasingly competitive market. 
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 {my current notes on a few things I have been following this month: web3, digital health, and a changed workforce}
New directions for digital healthcare In the past two years we have seen rapid cycles of innovation, deployment and deliveries of health care technologies that would have been difficult imagine even 5 years ago – but is 2022 going to be the big year during which we learn about what actually works in digital health? For example, there has been a rapid expansion of insurance coverage for remote patient monitoring, like the remote collection (use connected sensors) of patient blood pressure, weight, or blood glucose levels measurements in both public and private insurance systems – for example my healthcare provider offered me a full year of access to both apple fitness plus and peloton. This is a really great step forward using digital technology and tools to help improve the life and the experience of those who engage with the health care systems …. But now is perhaps the time to look at all of this data to find out, learn and reflect on how well these tools work and for whom? I think success in health tech will be measured in how the existing health care system improves in quality, patient-centricity, and convenience using these insights and driving the vision of a digital health future. NFTs in the mainstream (finally) driving crypto adoption? It turns out that 2021 saw a huge spike in the creation and sale of non-fungible tokens (NFTs) as People bought and traded NFTs of everything from contemporary art to images of apes and a giant tungsten cube . In many ways, I think there’s nothing new here since NFTs provides just a sort of digital deed to establish the ownership of a certain good. They make it possible to assign and transfer ownership of digital assets like digital art and music, they make clear who the owners are. In addition, NFTs are programmable, which makes it possible to expand their features, as a result, their value over time. As more mainstream brands and artists continue to popularize NFTs in 2022 and with marketplaces like OpenSea and Coinbase NFT coming up in 2022, these products will become significantly more accessible to consumers. Most importantly, NFTs have already proven that they can bootstrap communities of consumers and capture the public’s imagination in a way that cryptocurrency mostly hasn’t. The demand for NFTs is already driving innovation to make crypto wallet technology to more intuitive and better protected for consumers, which makes it more likely that NFTs will finally help take crypto mainstream. A new and changed workforce In 2021, most companies have viewed the evolutions caused by the Pandemic as an anomaly. They were wrong. Covid actually has accelerated many underlying trends that were latent in the labor market before the onset of the pandemic. Companies will now have to account for them in their human resource strategy. I think the disruption is far beyond what most imagine. The workforce cultural makeup post-pandemic and the supply-demand imbalance is becoming a permanent feature on recruiting which is going to make companies have to re-engineer basically everything in recruiting and retention, in particular working much harder in reducing turnover as many people reconsider the role of work in their lives. Most importantly, firms depending on creative and knowledge workers will be better served to consider how they can reshape their job descriptions to retain talent fed up with fulfilling various corporate process “requirements” and “culture compliance” to make themselves attractive to a new and powerful class of gig workers with world-class skills.  In my previous note, I have been discussing that we’re in 2022 at an inflection point with the future of work, digital nomadism combined with the rapid maturing of web3-associated technologies, that is most likely already altering the fabric of society. I made the personal choice to sit at the confluence this web3, DeFi and blockchain rapidly evolving technology wave – simply because the developments in web3, including NFTs, decentralized storage, computing power, digital currencies, in other words distributed ledger tech definitely help creators to opt and enjoy the benefits of ownership, empowerment and overall, 360 degrees decentralization – away from Silicon Valley, California and its trappings (at least for me and my fam). I'm super proud to be part of the @usebraintrust movement where we are building a better future of work for everyone! And this $100M of $BTRST is for all of us. Come and build with us! $BTRST #web3 1. (Finally) an unparalleled explosion of AI From generative pre-trained transformer models and digital twins to synthetic data, mini machine learning models run at the edge of specialized AI embedded silicon, in 2022 I sense that AI is finally maturing – we’re finally moving with general deep learning and neural networks to the benefit of no- and low-code platforms with advanced robotic process automation, for example GPT-3 is already used to write code faster, which in turn makes jobs less monotonous and more focused on the fun part which is creation. 2. Decentralized finance is here to disrupt a lot more industries! Thanks to the current popular focus on NFTs, DeFi is already creating a lot more opportunities for people to take their financial lives back into their own hands: because non-fungible tokens make it possible to prove ownership of a digital asset, regardless of what that asset is, and once we can prove ownership, that asset become tradable and exchangeable to drive economic growth. This characteristic in itself, amongst others specific to NFTs, are redefining our economic system in 2022:
NFTs have the potential to redefine the existing financial infrastructure by displacing the existing incumbents. For example, today banks make a lot of profit by selling, reselling and insuring real estate mortgages linked to land properties. Now imagine that you can replace any real-world asset (real estate, car, art, etc.) with any digital asset linked to an NFT and replace the bank with smart contracts: this is decentralized finance (simplified). In addition to this, NFTs bring completely new ways of earning an income: once you have earned, minted, or bought an NFT, you can rent it out, borrow against it, license it, sell it, package it, insure it …. In other words, make money with it. So, it’s fair game to play to earn (no pun), read to earn, think to earn, stream to earn, in fact, whatever-you-come-up-with to earn. As a result, DAOs will enable any community to come together - empowering content creators, crypto owners and innovators to make money without the involvement of traditional financial institutions. 3. Enter the metaverse and the future of (mostly remote) work Let’s say that 2025 is the new 1995 and that the metaverse is going to have the same impact on society that the first and second internets have had for the past two decades. As the required tech to move from a centralized to decentralized web is getting closer to be fully ready (I’ve already talked about the software, now the hardware like AR and VR headsets is making leaps forward, see what Sony announced at CES 2022 for PS5 or the MR headsets from Apple). This is going to completely change the way with socialize, live and work. As an example, as people have reflected during the pandemic on their lives, valuing more family time and having seen the benefits of remote work, reduced commute time and the ability to focus more on projects they’re passionate about, are no longer geographically bound to work where they live. This is good, as now more than ever, the world is your oyster. And the same applies to those startups and companies fully embracing remote work. They, too, are able to fish in a much bigger talent pool. And as remote working technologies are becoming more advanced and intuitive, because of the metaverse, where collaboration across the digital highway can become the norm. Let me know what you think!
My name's phil mora and I write about the things I love fitness, hacking work, tech and anything holistic. Head of Product thinker, doer, designer, coder, leader.  Last month I started my next chapter in helping change in ag, and I briefly talked about my thoughts on the future of ag and regenerative agriculture practices. I am adding a few more thoughts in this note, along with a very cool little youtube vid I found when doing some cursory research last weekend. And with more recent mainstream press coverage for sure discovery work is in progress! In this note, I am trying to get my head around understanding a little bit more, at a very high level, the differences and challenges to the conversion. It’s amazing to see that regenerative farmers in the EU, North America, Brazil, Australia, and India (mainly) are working actively to change the way we farm in this decade and as a result increase biodiversity, enrich soils, improve watersheds, and enhancing the health of livestock and wildlife. And by thinking more holistically, they in turn increase the resilience of their farms to weather events and help boost the sustainability of their communities as well. And with government policies and the food industry looking for solutions that will improve and secure food supplies in the post-covid era, orgs in the US, Brazil, India and the EU are planning to have millions of acres converted to regenerative farming methods (for example, Patagonia, Danone, General Mills and Mc Cain) and invested multi-million-dollar budgets. Recap: 5 principles of regenerative agriculture: Regenerative Ag has five main principles,
These are put into practice under a general, guiding principle of integrating all the farm’s operations as far as possible. In today’s conventional farming approach, crops and livestock production are typically kept separate. Regenerative agriculture combines them in circular ecosystems; essentially, the animals feed the plants, and the plants feed the animals. The regulated grazing of sheep or cows, for example, encourages plant growth, and distributes natural nutrients back over the land in the form of dung. Poultry also fertilizes land, as well as eating unwelcome bugs and weeds. The focus of regenerative farming is most commonly to be the quality and performance of the soil, and regenerative farmers use growing practices that improve the health of their land (by the way there is now evidence that this approach can enrich soil and improve watersheds, which reduces topsoil runoff) , with the more common regenerative farming methods including:
For the last century industrial farming has prioritized increasing production. Regenerative Agriculture addresses cost and regards profit for farmers as more important than production: if we can produce the same output with half the input, the farmer makes more money. Some see this as farming the way it used to be, before the shit to heavy mechanization and intense chemical us in the 1950s and 60s, which encouraged monocultures and ever-larger farms The advantages of regenerative farming in infiltration and biodiversity Improving the soil not only increases fertility in a sustainable way, but also tends to improve water infiltration. Better infiltration means less runoff, and also less erosion and pollution from soil being carried away in the runoff water. In some areas, water springs that dried up several years ago have begun to flow again due to new regenerative farming approaches. In conclusion, the COVID-19 pandemic has disrupted supply chains and demand, and increased the amount of food waste in farms and fields while threatening food security for many. As agriculture gradually regains its footing, participants and stakeholders are casting an eye ahead, to safeguarding food supplies against the potentially greater and more disruptive effects and once again, innovation and advanced technologies are making a powerful contribution to secure and sustainable food production. And as a new agricultural ecosystem rapidly emerges, I am convinced that regenerative ag is very well positioned to replace 20th century conventional farming. Let me know what you think here.
My name's phil mora and I blog about the things I love fitness, hacking work, tech and anything holistic. New Chapter! Head of Product at Vayda Vayda is advancing regenerative outcomes in agriculture. By combining regenerative principles and a high-tech approach, we are focused on facilitating the reversal of climate change, while rebuilding natural ecosystems and feeding people with healthier food. thinker, doer, designer, coder, leader |
Head of Product in Colorado. travel 🚀 work 🌵 weights 🍔 music 💪🏻 rocky mountains, tech and dogs 🐾
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