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Get ready for an AI earthquake! A team of UCLA researchers ( n, , , ) have dropped some major keys to AGI. It's not only the code to seriously human-sounding AI, but they've also gone and open-sourced the whole thing. @zxche @Yihe__Deng @HuizhuoY @Kaixuan_Ji_19, @QuanquanGu Now you can develop better LLMs without needing to feed it tons of new, human-annotated data. First, let's focus on the game-changer here: a self-teaching language model. This method lets a language model becoming better and better without massive amounts of new, externally curated data. teach itself, Introducing SPIN: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models I went full deep-dive mode – read their paper (" "),  scoured the insights on forums like , , and with Google Gemini Ultra and GPT-4 Turbo – and the core concept of SPIN knocked my tech-loving metaphorical socks off: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models HackerNews X Reddit The 'Conversation Partner' Trick Imagine starting with a language model that has mastered basic skills (let's say conversational etiquette). With SPIN, the model building a dataset from what it already knows. generates internal 'conversations,' Instant knowledge expansion! Step two involves unleashing a new model and giving it one task: spot the difference between and genuine human communication. This forces the original model to up its game, getting more and more with every response to avoid detection. machine-generated chats human-like Here's where things get interesting. They started with (already fine-tuned with ). SPIN unleashed an iterative training system with this base model, improving it exponentially without relying on tons of new externally created data. zephyr-7b-sft-full UltraChat corpus SPIN vs. Traditional AI Training (DPO): A New Champion? We usually think machine learning, particularly for these huge language models, requires boatloads of carefully curated and labeled data. methods involve humans painstakingly rating AI responses against each other for training. Not only is this labor-intensive, but it also balloons costs as a dataset grows. Direct Preference Optimization (DPO) Direct Preference Optimization (DTO) is a training method where a model is fine-tuned using a dataset of preferences, often involving human judgments that decide which of the model-generated responses are preferred. This method requires collecting new data where each piece is labeled based on these preferences, which can be resource-intensive. In contrast, SPIN utilizes , significantly reducing the need for new data. iterative self-play By the first iteration, , highlighting its efficiency and effectiveness in leveraging existing data to enhance model performance. SPIN's performance already exceeds that of DPO in most cases SPIN showcases its strength by achieving . The process of , methodically enhances the model's performance across multiple iterations, , especially on challenging benchmarks like TruthfulQA and GSM8k. on-par performance with models trained on more extensive datasets iterative training showcasing substantial improvements So, SPIN outperforms conventional training methods, including DPO, by efficiently leveraging synthetic datasets generated through self-play, without the need for additional human-annotated data. What are SPIN's Strengths and Costs? SPIN throws a curveball with its self-play dynamic. Think of it like a language model sparring with itself in a linguistic boxing ring, with each round teaching it new tricks. SPIN's data efficiency bypasses the need for new human-annotated datasets. But more importantly, it , . accelerates the improvement loop making the model increasingly adept at generating human-like text Not only does SPIN seem to match models trained on larger external datasets, but its iterative power means consistent gains as it essentially studies its own output. Mindblowing, right? Okay, Let's Talk the Elephant in the Room – COST Nous Research co-founder has a point. These big ol' language models don't get smarter for free. Iteratively re-training with SPIN involves the expensive process of Supervised Fine-Tuning (SFT) each time. @Teknium1 However, he also mentions that "I think its worth it!". Also, the long-term benefits of quicker evolution and potentially less dependency on human-annotated data outweigh the initial investment? That's the exciting question! BOOM! It's Open-Source AI Time Just yesterday, , associate professor of computer science at UCLA and director of AI research at ByteDance, announced that . This doesn't just mean code and datasets, but pre-trained models to kickstart your own AI journeys. Quanquan Gu anyone can now use the SPIN model and dataset SPIN mirrors human thought processes. By generating text that feels human, SPIN hints at the foundational elements of reasoning that future AI could do. You know how some LLM outputs feel robotic right? Well, SPIN is different. It actually mirrors the way humans think. The way it writes feels so natural, it's like a peek into how future AI might be able to reason for themselves. This isn't just about making chatbots sound nicer. It's about creating a kind of digital thinking that works like ours. That kind of AI would be so much more flexible and capable of real understanding. While SPIN is a big leap forward in making language models sound more natural, . it's easy to get excited and overestimate what it means The text it produces is impressive (you can take a look to the database), but it's important to remember that AI doesn't yet have the capacity for true independent reasoning. While SPIN isn't true , the way it mimics human-like writing demonstrates impressive advances in how AI could process and use language in the future. AGI Even so, it does suggest amazing possibilities for how AI and language might develop in the future (if you remember that we are at the beginning of the hockey stick, the future is not far from today...) The ripple effects will be huge and here's your access pass: Code: Available on : GitHub https://github.com/uclaml/SPIN Data: Hosted on , the dataset is readily accessible for those eager to apply SPIN methodologies: Hugging Face https://huggingface.co/collections/UCLA-AGI/datasets-spin-65c3624e98d4b589bbc76f3a… Models: Pre-trained models are also available, offering a head start for experimenting with SPIN-enhanced language models: https://huggingface.co/collections/UCLA-AGI/zephyr-7b-sft-full-spin-65c361dfca65637272a02c40… Project Page: For comprehensive insights and further information, the project page is an invaluable resource: https://uclaml.github.io/SPIN/ To sum up, its iterative, self-improving methodology is a significant advancement towards creating LLM that can engage in genuinely human-like communication. Originally shared on my . X account

AGI Blueprint? UCLA Researchers Open-Source SPIN—A Self-Improving Language Model

এজিআই ব্লুপ্রিন্ট? UCLA গবেষকরা ওপেন-সোর্স স্পিন—একটি স্ব-উন্নতিশীল ভাষা মডেল

AGI-Blaupause? UCLA-Forscher Open-Source-SPIN – ein sich selbst verbesserndes Sprachmodell

¿Plan AGI? SPIN de código abierto de investigadores de UCLA: un modelo de lenguaje que se mejora a sí mismo

Plan AGI ? SPIN open source des chercheurs de l'UCLA : un modèle linguistique auto-améliorable

एजीआई ब्लूप्रिंट? यूसीएलए शोधकर्ता ओपन-सोर्स स्पिन-एक स्व-सुधार भाषा मॉडल

AGIブループリント? UCLA の研究者がオープンソースの SPIN — 自己改善型言語モデルを公開

AGI 청사진? UCLA 연구원 오픈 소스 SPIN - 자체 개선 언어 모델

Projeto AGI? SPIN de código aberto para pesquisadores da UCLA - um modelo de linguagem que se aprimora automaticamente

Проект AGI? Исследователи Калифорнийского университета в Лос-Анджелесе SPIN с открытым исходным кодом — самоусовершенствующаяся языковая модель

AGI Planı? UCLA Araştırmacıları Açık Kaynak SPIN—Kendini Geliştiren Bir Dil Modeli

Bản thiết kế AGI? Các nhà nghiên cứu của UCLA SPIN mã nguồn mở—Mô hình ngôn ngữ tự cải thiện

AGI 蓝图？加州大学洛杉矶分校研究人员开源 SPIN——一种自我改进的语言模型

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The AI startup landscape has been dynamic, with numerous emerging players and innovative solutions in various industries, including healthcare, finance, transportation, e-commerce, and more, making it highly attractive for investors. From autonomous vehicles to virtual assistants, and the seemingly godlike ChatGPT, AI is transforming industries and disrupting traditional business models. Fueling the AI fire and investor interest is the sheer size and growth the sector has and will continue to enjoy. According to Precedence Research, the global artificial intelligence (AI) market size was estimated at $119.78 billion in 2022 and is . That's a compound annual growth rate (CAGR) of 38.1% from 2022 to 2030. expected to hit almost $1.6 trillion by 2030 However, not all AI startups are created equal, and investors need to exercise caution when deciding where to allocate their capital. To mitigate risk and make informed investment decisions, it's crucial to ask the right questions and thoroughly evaluate the potential of an AI startup. The fast-paced nature of AI development, regulatory uncertainties, ethical concerns, and the competitive landscape can impact the success and viability of AI startups. Therefore, it becomes crucial for investors to thoroughly assess and evaluate the risks and opportunities associated with investing in AI startups. I spoke with Daniil Kirikov, managing partner at , about some of the not-so-common questions, investors should be asking before they commit. Orbita VC Expand your understanding of the age-old "Is there a problem" qualifier One of the first questions all investors consider is whether any startup, AI or otherwise, is addressing a real problem that people are willing to pay for. "AI hype can sometimes overshadow the actual value proposition of a startup," Kirikov said. "Investors need to expand their understanding of whether there's a problem with better questions to critically assess if the startup is solving an old issue that people were already paying to solve, but to someone else. A good litmus test for this is to ask, 'Who were people paying to solve this problem 10 years ago?' If the answer is unclear or non-existent, it could be a sign that the problem being addressed is not significant or sustainable in the long run." Assuming the startup is addressing a genuine problem, the next question to ask is whether AI enables a significant increase in efficiency in terms of cost, speed, or quality. "AI should provide a clear competitive advantage over traditional methods, and the startup should have a compelling value proposition in this regard," Kirikov said. "If the startup's solution merely provides marginal improvements or does not result in a substantial increase in efficiency, it may not be a compelling investment opportunity. Investors should seek startups that have a clear edge over the competition and can deliver a step-change improvement through the use of AI." Problem defined, but does this need solving, and why now? Another critical factor to consider is the timing of the solution and whether it could have already been solved before the hockey-stick growth and interest in AI. After all, it's 2023 - at this point in history, it is nigh on impossible to come up with an idea that hasn't already been solved 700 times or find a gap in the market. "Investors should assess whether the same solution could have been implemented two, three, or five years ago," Kirikov said. "If the answer is yes, it may indicate that the startup is not leveraging new technologies or ideas and that others may have already attempted similar approaches without success. Learning from the failures of others is crucial in making investment decisions. If a solution has been attempted before without significant success, the likelihood of multiple increases in efficiency may be low. Investors should look for startups that are genuinely innovative and are leveraging cutting-edge technologies or ideas that are not readily available in the market." Dig deep on security risk, not just financials Security is also a vital consideration in the AI startup space. As AI technologies become more prevalent and powerful, the potential risks and ethical concerns associated with them increase. "Governments and regulatory bodies are paying close attention to the development and deployment of AI, and startups that do not prioritize security and ethical considerations may face regulatory hurdles and legal challenges," Kirikov said. "Investors should carefully evaluate the startup's approach to security and compliance with relevant regulations to mitigate potential risks and liabilities." Asking deeper and more nuanced questions is now critical given the sheer volume of AI startups being created daily. It is now not enough to understand at a surface level the problem the startup solves, its business model, traction and growth potential, team expertise, financials, risk mitigation, exit strategy, competitive advantage, funding requirements, and the timeline for achieving milestones when evaluating where to commit funds. Kirikov's approach and level of questioning in the assessment phase are now crucial for the industry and can help all investors make more informed decisions.

Assessing the Risk of Funding AI Startups: Deeper Questions Every Investor Should Ask

Raising money is one of the most challenging aspects of being a startup founder. After all, you're asking people to believe in your vision, invest money into your company and stay with you through ups and downs. Now more than ever, it’s -- which it makes it all the more impressive when it happens. extremely difficult to move from a seed round to a Series A One company that recently made the jump is , an all-in-one SaaS buying and management solution. They announced their Series A led by Prosus Ventures and Accel and comes only ten months after its seed round. Spendflo The company experienced a hockey-stick growth trajectory since its inception and has grown 30% month on month since the start of 2022, multiplying its revenue 15 times, growing its customer count by 5X, and expanding the team size fourfold. Talking to Siddharth Sridharan, CEO and Co-founder, let’d dig in more on their recent accomplishments. SE: Congratulations on your Series A. It’s never been more difficult for startups to move from the seed to Series A stage. What do you think sets you apart during this time? SS: Spendflo is an all-in-one solution that solves challenges at every stage of the SaaS procurement lifecycle including buying, management, and security. Every other alternative in the market remains a point solution focusing on just one of the three major challenges, resulting in three times the cost, effort, and resources involved for a business to tackle one problem. Moreover, juggling between three different systems to solve one problem is counterintuitive to the goal of saving time and money for fast-growing businesses. Spendflo remains the truly complete solution that helps modern businesses buy, manage and secure SaaS on autopilot. Seamless sharing of data between its SaaS buying, management, and security hubs streamlines SaaS buying, automates workflows, accelerates buying cycles, and saves up to 30% of a company’s annual SaaS expenses. As companies grapple with mounting SaaS expenses and try to be more efficient and productive, Spendflo addresses the need of the hour perfectly. : Saas spends are typically one of the top 5 expenses of any company. How did you identify this as an issue and recognize you could make an impact? SE SS: Spendflo was founded in 2021 while I was running business operations at the Californian electric vehicle charging company, Volta Charging. Every quarter, my CFO would complain to me that we were spending too much on SaaS solutions, but I didn’t know who owned the contracts or how they were using them. I got so fed up and realized I couldn’t be the only one with this problem. So I teamed up with co-founders Ajay Vardhan and Rajiv Ramanan to commercialize a solution. Spendflo essentially solves three problems: 1. The lack of visibility 2. The sheer chaos caused by multiple stakeholders involved in the buying, negotiation, and renewal processes 3. Price and usage opacity of their SaaS stack With Spendflo, companies can now track all of their SaaS tools and subscriptions in a single location. The solution provides crucial data on how much the business is spending on each subscription, as well as when contracts are due to be renewed; and more importantly, it provides usage data, so the business can see whether it’s getting the maximum possible ROI. SE: Tell me more about how Spendflo works, especially for audiences who could benefit, like: finance, procurement and IT, and security. SS: Spendflo helps companies save hundreds of hours on negotiating SaaS contracts and offers complete visibility while helping them evaluate, right-size and optimize their stack. This leads to faster approvals, easier onboarding, and more informed decision-making for a company's SaaS needs. For each audience, this translates into direct and impactful advantages, such as: CFOs and their finance teams now gain more visibility, get guaranteed savings (Spendflo provides 3x ROI consistently), and have streamlined approvals for their SaaS stack. Benchmark vendor data also helps them know areas where they can save or cut back. Procurement and IT departments receive benchmarked vendor data, enabling them to identify opportunities for cost savings and reduce procurement cycles. With Spendflo’s granular usage data and SaaS sentiment analysis, procurement and IT teams can make data-backed renewal/replacement decisions. Moreover, Spendflo provides access to skilled negotiation experts who are equipped to handle these tasks with proficiency. Security teams can now expect guaranteed better compliance with vendor questionnaires, streamlined onboarding processes, and seamless license renewals. SE: It looks like you’ve experienced tremendous growth in a relatively short amount of time. Tell me about that and what you think is impacting this. SS: Yes, we’ve experienced incredible growth at Spendflo, and we believe that it's due to a combination of factors. In today's economic climate, businesses are seeking innovative ways to optimize their operations, and Spendflo offers a streamlined, and efficient solution to SaaS buying and management. Additionally, our best-in-class, 24x7 multi-channel support, along with industry-leading service level agreements, have helped us gain the trust of leading global brands across industries. \ All of these efforts have helped us reach a wider audience and happier customers, which explains our growth trajectory. SE: How are you going to use the new funds? What are your most immediate and future plans? SS: The funds raised will be deployed towards strengthening the team, rapidly evolving the product offering, and expanding GTM efforts in the North American region. SE: Any tips, advice or best practices  for other founders and startups currently looking to raise a Series A? SS: These are the three things that I learned from Spendflo’s journey so far: Having loyal customers that truly believe in you, and would recommend you anywhere, anytime, can create a huge impact. Having reliable and proficient sales and marketing leaders within the organization makes the journey to Series A much smoother, and probable. Keeping past and potential investors in the loop about developments in the company and product is crucial—I highly recommend sending monthly investor updates.

The future of SaaS is savings, Spendflo raises Series A

Heyo Growth Marketers - The round 5 results of the by and HackerNoon are now LIVE! Growth Marketing Writing Contest mParticle Here’s a quick reminder on how the contest works - You can win from $12,000 prize pool by writing a story on . Here are a few topic suggestions shared by mParticle: #growth-marketing Product Management Customer Data Platforms (CDP) Growth Marketing Data Engineering Data Management ! Submit your #growth-marketing story today The Growth Marketing Writing Contest: Round 5 Nominations & Winners For selecting the top 10 nominations, we picked all the stories with the #growth-marketingtag on HackerNoon, published in January 2022. Then we chose the top stories using 60:30:10 weightage respectively to: Number of hours read The number of people reached The freshness of the content Here are the top 10 nominations: by 6 More Growth Marketing Strategies to 10x Your Startup in 2023 @mrkrystallis by Press Mentions For Your Brand: A Growth Marketer's How-To @darragh by 10 Reasons Why I Sucked At Growth Marketing @nebojsa.todorovic by How to Use No-Code Machine Learning to Optimize your HackerNoon Articles @giorgiob by How to Unlock Product Market Fit and Rapid Growth for Startups @loringuyen by 7 Tips for Product Managers in Web3 @hunais by Behavioral Economics: Growth Hackers' Secret Weapon @aghafasih by How to Achieve 'Product of the Day' on Product Hunt Using a Trello Board @artkulakov by If Growth Marketing Is So Terrible, Why Don't Statistics Show It? @requiredcrx by Why Web3 and NFT Gaming Can Be the Biggest Trend of 2023 @sergey-baloyan In the first place, we have: https://hackernoon.com/press-mentions-for-your-brand-a-growth-marketers-how-to?embedable=true Quick Tips Building Relationships With Journalists: Don’t be self-promotional about ideas for a story; they hate that. Add-value: Follow them on socials and share and interact with content you like and can add value to. It’ll help you stand out. Engage online: Find out what they need for their story or story ideas; be helpful and add-value. Listen more than you talk. Listen: Congratulations, . You have won $1,000! @darragh In the second place, we have: https://hackernoon.com/6-more-growth-marketing-strategies-to-10x-your-startup-in-2023?embedable=true Lastly, here's an extra Growth Marketing Strategy you should never forget… Never stop testing! Constant testing is the backbone of any successful growth marketing strategy. Just make sure that your analytics are set up right so that you can analyze both your successes & failures equally and it won't be long till you discover a strategy with hockey stick growth potential that's worth doubling down on. Congratulations, . You have won $600!!! @mrkrystallis The story scoring the third position is: https://hackernoon.com/10-reasons-why-i-sucked-at-growth-marketing?embedable=true I used to be a humble and happy writer who one day had the Gordon Gekko “moment." You know what I'm talking about, don't you? I thought it was my revelation, but it turned out it was my damnation. I wanted more, plain and simple. It wasn't enough for me to be just a writer. Oh no, how about a writer-marketer?! More shiny titles, more clients. Sounds like a solid business growth plan. What could possibly go wrong? Well, it didn't take me too long to realize one simple growth marketing truth: Every marketer can be a writer; but not every writer can be a marketer. Marketing takes and asks so much more than just writing. So, I didn't get a dime for my rhyme. Well-deserved ! You have won $400. @nebojsa.todorovic Congratulations again to all the nominations and winners. Visit today to view all the current and upcoming contests! These contests are for everyone! Share your stories and win $$$! contests.hackernoon.com

The Growth Marketing Writing Contest: Round 5 Results Announced!

গ্রোথ মার্কেটিং রাইটিং কনটেস্ট: রাউন্ড 5 এর ফলাফল ঘোষণা করা হয়েছে!

Der Wachstumsmarketing-Schreibwettbewerb: Ergebnisse der fünften Runde bekannt gegeben!

Concurso de redacción sobre marketing de crecimiento: ¡se anuncian los resultados de la ronda 5!

Concours d'écriture sur le marketing de croissance : les résultats de la 5e ronde sont annoncés !

ग्रोथ मार्केटिंग राइटिंग कॉन्टेस्ट: राउंड 5 के परिणाम घोषित!

The Growth Marketing Writing Contest: 第 5 ラウンドの結果が発表されました!

그로스 마케팅 글쓰기 콘테스트: 5차 결과 발표!

O Concurso de Redação de Growth Marketing: Resultados da 5ª Rodada Anunciados!

Büyüme Pazarlaması Yazma Yarışması: 5. Tur Sonuçları Açıklandı!

Cuộc thi Viết về Tiếp thị Tăng trưởng: Đã công bố Kết quả Vòng 5!

增长营销写作比赛：第 5 轮结果公布！

Apple's Tim Cook is one of many metaverse detractors, but it is no surprise that he favors augmented reality (AR) as the next significant technological shift. "I always think it's important that people understand what something is," the Apple CEO told on Friday. "And I'm really not sure the average person can tell you what the metaverse is." Dutch publication Bright Other big names calling out the metaverse and promoting AR all have the same thing in common: their companies are expected to launch AR solutions soon, and they compete with the loudest kid in the metaverse playground, Meta. "In true technical style, there is no single universally accepted definition of the metaverse," Olga Vorobyeva, founder at Vox Consulting, wrote in . "Simply put, the metaverse is a vast network of 3D worlds and simulations rendered in real-time for cooperation and participation. This is not a virtual reality experience or a virtual economy with avatars sticking out, but a way to maintain identity, objects, payments, history, and ownership continuity." VentureBeat You don't have to be a budding Sherlock Holmes to see those speaking publicly about AR versus the metaverse tend to side with whichever horse their company decided to back. And the point made about people understanding a technology seems illogical at best. I don't need to understand Kotlin, Javascript, or AI algorithms to rent an e-scooter, order food, or watch an endless stream of people lip-syncing in front of a ring light. I simply You don't have to be a budding Sherlock Holmes to see those speaking publicly about AR versus the metaverse tend to side with whichever horse their company decided to back. And the point made about people understanding a technology seems illogical at best. I don't need to understand Kotlin, Javascript, or AI algorithms to rent an e-scooter, order food, or watch an endless stream of people lip-syncing in front of a ring light. I simply need to hear about a cool app that makes my life better, download it, and use it. Put a different way, Pokemon Go was the app that made AR a mainstream topic, and I don't need to understand AR to catch 'em all. But not only is AR versus the metaverse yet another example of binary thinking, causing polarization and division, but it's also missing the point. AR and the metaverse are best served together, like macaroni and cheese. While many analysts separate the two when preparing their market size reports and forecasts, Grand View Research understands that when looking at the future of the metaverse, it should include AR, VR, MR, and all associated technologies. , the global metaverse market size was estimated at $38.85 billion in 2021. It is expected to expand at a compound annual growth rate (CAGR) of 39.4% from 2022 to 2030. According to Grand View But all of this is moot unless we, the users, get apps that can make a positive difference in our daily lives. I looked at what the post-Pokemon AR world had to offer. There are some exciting and engaging offerings, including real-time , measuring body size and shape for , , and even . navigation in airports custom clothing designing and engineering buildings detecting and battling wildfires However, one new app combines AR with the metaverse/Web3 and is backed by some heavy firepower. is a street sports app designed for those who love skateboarding, rollerblading, BMX, parkour, snowboarding, and other similar activities. ShredSpots Founded by former pro skater Lev Filimonov and with an executive team that includes former YouTube, Snap, and Google execs, the app consists of features such as a community-driven street sports map and the ability to upload your tricks so others can see your skills in action. ShredSpots differs from other street sports map apps because it is soon to include AR features, and by building the app with Web3 in mind, the future possibilities go way beyond what you could offer by adding AR alone. The upcoming AR addition will allow users to see other users' tricks where they were performed, using your smartphone as the viewing device (and, one would assume, in AR smart glasses at some point). That's cool, but by combining AR and Web3, ShredSpots can offer users a whole raft of additional benefits. Imagine being able to monetize your skills and earn for sick tricks, or being able to immerse yourself in a world of street culture, with original street art and more presented to you as you look at the world through ShredSpots and your smartphone camera. I imagine the team has several ideas that combine all the latest technologies, including NFTs. ShredSpots is an excellent example of a team that understands there is no AR vs. metaverse - the two are complimentary, help both sides to gain mainstream acceptance and adoption, and - most importantly - should not even be highlighted in the first place. The most important thing is not the technology we build these experiences on but the experience itself. So if you want to take advantage of the hockey-stick growth analysts expect in Web3, AR, the metaverse, and other related technologies, be like ShredSpots. Design a fantastic app that works, appeal to a broad audience, and make it easy for them to use and understand. Tim Cook is right about that part. He's just wrong about making this a them vs. us thing.

Why Tim Cook has got it wrong on augmented reality vs. the metaverse

During an economic downturn, everything is impacted, from the cost of food and fuel to consumers' discretionary spending budgets. As discretionary spending patterns shift, app monetization will be affected. The apps that can succeed during an economic downturn differ from those that thrive in upswings. As frivolous spending from users and investors dries up, . cockroaches become valuable, not unicorns Subscription apps that provide high value to a committed user base are hard to kill. Adjusting your app's mobile monetization to match the economic conditions will help you and your users weather the storm. While there is talk of subscription fatigue, data shows a continued evolution towards use-based subscriptions. Venture capitalists are now looking for revenue and coveted hockey stick growth. A solid and elegant mobile monetization strategy is key to providing the required payment to continue to woo investors while you seek long-term growth. Mobile Monetization Strategies Mobile monetization strategies need to shift to account for economic conditions. Think like your users as you calculate the impact of inflation. Can you be generous with your offering and promotions to drive a higher sales volume? Consider how hiring freezes and the most recent jobs report will impact your user base. Will this decrease their budget? Could it increase their session time? Can you offer a steeper discount on volume purchases? Is it possible to provide an introductory rate for your subscription offering? Answers to these questions will vary based on the unique user bases of individual apps. Additionally, unique cohorts within a single app's user base can answer these questions differently. Verticals Expected to Perform Well Subscriptions in verticals that help people navigate tough times will have staying power and perform better than verticals with other focuses. Apps with subscriptions focused on education, the gig economy, FinTech, HealthTech, and entertainment will experience better performance in this market. Let's examine each vertical and why it's expected to perform well. Education becomes a more significant focus as people are trying to level up or change the role or industry they work in. Subscription apps help people find new positions or grow their networks, like , , and . There will also be a higher demand for online education apps that help children K-12 to learn core STEM and college prep skills as parents look for cheaper alternatives to offline tutoring centers. Apps like , and are well positioned in this market. LinkedIn Learning Coursera BumbleBizz Tynker, Quizlet Elite While budgets are tight, more people are looking to increase money coming in via gigs and community-based monetization. Gig apps with subscriptions that provide work like DoorDash's , , and will be positioned well as desired by consumers and people looking to make extra money. DashPass Lyft Pink Instacart+ Community-based apps that help creators monetize, like and , help users create their revenue streams. As more and more people become content creators, community-based are becoming a widespread monetization strategy. Twitch recently increased the generosity of its offering by Patreon Twitch business models allowing multi-platform streaming. We will also see the increased popularity of fintech apps. These apps help people save money with deals and discounts and budget better. Apps that can ensure users better control their income and expenses, such as , will see growing success in this market. You Need A Budget (YNAB) Healthtech apps help people get more access to content and services that help them with mental health and wellness. Businesses that aim to help reduce stress and anxiety during uncertain times will see an influx of users. Apps that help connect patients with medical providers, such as and , will see more downloads. Meditation apps like and offering subscriptions to on-demand guided meditations will see an increase in demand as people look to navigate uncertain times. Lyra One Medical Calm Headspace Finally, we will see success for apps and services that help people escape and forget about inflation, such as Nintendo Online or Apple Arcade. The still holds in gaming. As seen by Netflix's first global subscriber loss in over ten years, there is room for new and scrappy video streaming services to enter the video entertainment space. power of subscription models Netflix's recent dip in earnings shows that users in the market are hungry for new content, and there is space for scrappy competitors to take subscribers away from the established streaming giants. Streaming services are attempting to step into this space with . various strategies Strategies for All Verticals The first cut made by many consumers when faced with budgetary constraints is their subscriptions. The subscription services most likely to survive this climate are the ones with low price points and high-value offerings. Adding a lower price point to your subscription can help retain users who would have otherwise churned. Increasing your app's value will incentivize users to stick around. When adjusting pricing, use the from First Round. What was optimal at 10,000 users won't be optimal at 100,000. Identifying a key user segment and applicable levers will help you make sustainable changes as you scale. Be generous when your unit economics allow for it. 4-Step Pricing Framework Subscription-based businesses that have additional monetization through microtransactions are a strong bet as it's easier for users to justify smaller purchases. Digiday reports that podcasters provide alongside subscriptions to help boost subscriber retention and joins. Offering microtransactions or exclusive content alongside your subscription will help capture more revenue. This is a best practice for mobile app monetization, no matter the economic conditions. exclusive and early access content Freemium business models that include a free version and paid upgrades will perform well. As users have more time, they will test out free apps and eventually convert to paying users as they become immersed in the app. Securing users will set your app up for success as the economy recovers and discretionary budgets return. Apps should lean on advertising options to keep entry price points low. Advertising will allow you to provide value to your users at a low cost while bringing in that much-needed revenue. is exploring at the $7 per month price point. Keep in mind that the mobile advertising landscape has changed in recent years. Be sure to . Netflix ad-supported subscriptions Update Your Subscription Monetization Strategy to Account for IDFA Changes Adjusting the mobile monetization strategy for your app during an economic downturn will help you retain payers and increase your staying power. Think like your users and work with your internal teams to make swift adjustments to match your monetization strategy to the economic conditions. Be ready to make adjustments again when the economic conditions improve. This article was co-authored by Lomit Patel and . Jenny Pollock

Monetization Strategies to Help Your App Weather the Economic Downturn

As I listened to the latest Tesla shareholders' meeting, with Musk on the stage. A few things left me blown away. And I wanted to share them with you. To be sure, first, I'm not a Tesla investor. Second, this is not investment advice. Third, independently of whether you like or not Musk, there are a few incredible facts here. And I, as well, have a conflicting relationship with Musk's public figure. On the one hand, I respect him as an entrepreneur, and I believe he's one of the greatest entrepreneurs of our time. On the flip side, I do not agree with many things he says. And as he is one of the most powerful people in the world today, I think it's critical to have him in check, more than anyone else. Having premised that, let me dive into a few interesting findings from Tesla's latest shareholders' meeting. Tipping points! (it takes years to fully roll out a business strategy, and when it does roll out, it looks like the tip of an iceberg!) Three years ago (pre-pandemic), Tesla was worth $41.8 billion. Today Tesla is worth over $900 billion; Ford is valued at $61.5 billion. It took 14 years for Tesla to pass Ford's market cap. In the next five years since then, Tesla's valuation 20xed. In 2017, when Tesla passed Ford, it made $11.8 million in revenues, losing over $2.2 million. In 2018, Tesla went through what Musk dubbed "production hell" (the attempt of the company to ramp up manufacturing), which brought it very close to bankruptcy (a few days away). Analysts all called up Tesla for that (I was among them). Yet when things turn around, those same analysts disappear. I want instead to give you a deep dive into the current state! Production Hell! (one thing is to prototype, another thing is to scale) For this quarter, Lucid motor delivered just 679 vehicles on 37000+ reservations. For some context, in the same period, Tesla delivered 254,695 vehicles. This reminds us that one thing is prototyping, and another thing is scaling. For Tesla to be where it is today, it took years of going through what Elon Musk has dubbed “production hell.” That flirt with the production hell made Tesla very close to bankruptcy, not once but at least three times. The latest - a few days away from it - was 2018. Just ten years before, in August 2012, Tesla had barely made three thousand cars. Thus, the most difficult part (as Musk confirmed over and over) was not to make a viable EV but to go from production to mass production! The profitability paradox (a single year makes up for all the lost years) Above are Tesla’s cumulative losses over the years. After losing money pretty much for all its history. Suddenly, profits in 2021 made up for the losses throughout the years. For new developing industries. Once the new industry becomes viable. And if you’re taking advantage of economies of scale. In a single year, you make up for all the losses produced over the company's history. This is the best way to measure scalability. And perhaps an index that the company dominating that industry has reached/or is reaching economies of scale. That’s a powerful combination. Indeed, it doesn’t matter whether you create a new industry. What matters is that you dominate it. A new paradigm (what we call EV will have a different name and a clear set of features in the consumer's mind) Each new industry comes with its paradigm. In the EV case, we’ll learn to call it in a way that comprises these primary components: self-driving for consumers to make demand scale robotics in manufacturing to make supply scale energy storage and distribution to solve the “range anxiety.” Like today, the iPhone wouldn’t make sense without touch, apps, and cameras. EVs won’t make sense without self-driving on the consumer side and robotics on the manufacturing side. And, of course, the energy infrastructure that solves the “range anxiety” of people owning EVs. On the robotics side, it’s interesting to notice that robots make other robots obsolete. In the picture, you find the “body shop robot count” per unit produced at Tesla’s factories. In other words, at Tesla, the same Model Y takes much fewer robots to build in Berlin than in Fremont. Non-linear growth (slowly, then suddenly - the snowball effect) When new industries develop, they initially move painfully slow. And to be fair, in many cases, they collapse on themselves. That is why it’s so risky to develop a whole new market. And that is why it’s so hard to predict what new industries will be able to pass the embryonic development. Indeed, there is a threshold before which growth seems painfully slow, and no matter how much you push, things don’t move faster. Yet, as you pass through that threshold, things start to move so quickly that growth becomes exponential! That is what a hockey stick's growth trajectory looks like! This is a dream for those surfing that trajectory and a nightmare for competitors that have to deal with a rising tsunami coming against. And that’s what the EV industry, headed by Tesla, looks like, as of now. And Elon Musk explained it well; when commenting on the chart above, he said: This is one of the cleanest exponentials…it looks like one of those fake projections you see in VC meetings, but it's actually true! The quote above explains the snowball effect of creating a new industry while dominating it! Also published . here

Tesla's Snowball Effect 

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