Business Ecosystem Development

How does Open Source benefit Big Tech companies in AI? Aren't they losing money by giving away their developments for free? They spend hundreds of millions of dollars, hiring the best researchers and building crazy AI Supercomputers with thousands of expensive GPUs. They develop the best AI models, and then... they give them away for everyone to use. For FREE. Mark Zuckerberg recently highlighted Meta's commitment to open-source AI during the Q4 and 2023 Year earnings call, emphasizing the strategic benefits this approach offers to Big Tech companies. Meta's strategy involves developing an open-source general infrastructure, exemplified by their Llama models, including the coming Llama 3, and industry-standard tools like PyTorch. This philosophy not only fosters innovation across the industry but also aligns with Meta's belief in the power of open-source development. Zuckerberg addressed common inquiries about the advantages of open-sourcing AI research: 𝟭. 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗠𝗼𝗱𝗲𝗹 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: Open sourcing enables improvements in AI models. Given the substantial work required to transform these models into market-ready products and the inevitable availability of other open-source models, leading to open source contributes more benefits than it detracts, maintaining product differentiation. 𝟮. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝗮𝗳𝗲𝘁𝘆: Open-source software tends to be more secure and safer, a crucial factor given the significance of safety in AI. 𝟯. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: The collective scrutiny and contributions from the community enhance operational efficiency and reduce computing costs, benefiting the entire ecosystem, including Meta. 𝟰. 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Open-source software often sets the industry standard. Adopting Meta's stack makes it simpler to incorporate new innovations into products, streamlining integration processes. 𝟱. 𝗔𝘁𝘁𝗿𝗮𝗰𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀 𝘁𝗼 𝗧𝗮𝗹𝗲𝗻𝘁: Open-source projects appeal to developers and researchers who prefer working on systems with widespread adoption. This strategy aids Meta in attracting top talent, essential for leading in new technological domains. Zuckerberg also noted that Meta's unique data and product integrations ensure that offering infrastructure like Llama as open source does not compromise the company's competitive edge. This strategy boosts tech innovation and positions Meta as a leader in AI, ensuring its relevance in the evolving tech industry. FOLKS: Open Source AI is Winning 🚀 Explore implementing Open Source for Enterprise here: https://lnkd.in/gUfbnAa9

If your website isn’t driving engagement, attracting clients, or positioning you as a trusted authority, chances are it’s missing one thing: valuable content. A static website is just an online brochure - it sits there, waiting to be found. But when you add useful, well-researched content, it transforms into a powerful business development tool. Here’s how to do it right: 1. Build a Strategy That Works: Great content doesn’t happen by accident. Your plan should align with your audience’s needs, your expertise, and your resources (time, people, and budget). A content calendar keeps you consistent, so you’re always top of mind. 2. Prioritize Research-Driven Content: Opinion pieces can be interesting, but data-backed insights and original research build credibility. If you want your content to get shared, bookmarked, and cited, focus on providing real value such as new information, deep expertise, and actionable takeaways. 3. Use Multiple Formats to Reach More People: Not everyone consumes content the same way. Some people prefer in-depth articles, while others engage with videos, podcasts, or infographics. Repurpose your best ideas across different formats to maximize reach and impact. 4. Curate, But Add Your Expertise: Sharing industry news, expert interviews, and event takeaways is a smart way to add value—but don’t just repost. Layer in your own insights to make it meaningful for your audience. Thoughtful curation strengthens your brand as a go-to resource. 5. Never Publish Without Editing: Typos and unclear messaging can hurt your credibility. Take the extra step to review your work (or have someone else do it) before publishing. Professionalism matters. 6. Publish With Purpose: A great piece of content means nothing if no one sees it. Optimize your posts with search-friendly URLs, embed videos strategically, and make sure everything is easy to find. Then, share it where your audience is - on LinkedIn, in email newsletters, and beyond. Content builds trust, and trust leads to business. If your website isn’t actively helping you attract opportunities, it’s time to rethink your content approach. Done right, it can position you as the go-to expert in your industry. Let me know what you think of these tips in the comments below! #contentmarketing #personalbranding #legalmarketing #bestadvice

Being "everywhere at once" isn't a strategy —it's a trap.  And it might be why you're burnt out. The best way to escape it is to ❌ Ditch the disconnected channel approach ✅ Build a content flywheel Here's How to Build Your Own Flywheel in 5 Step: (Based on my convo with Nick Cegelski from 30 Minutes to President's Club) Key principle: Systems are about specific inputs, consistent outputs, and predictable outcomes. 1. Start With One Strong Primary Channel Choose a primary content type that: -Showcases your team's strengths -Delivers substantial value to your audience -Can be segmented and repurposed For 30MPC, podcasting was the obvious choice given Nick and Armand's sales expertise and speaking abilities. 2. Design for Repurposing from Day One When creating primary content, think about how it can feed other channels: ✔️ Record video of podcast interviews to enable clip creation ✔️ Structure long-form content with clear sections that can stand alone ✔️ Capture visually compelling moments that work for social media ✔️ Ask questions that generate quotable, shareable answers 3. Establish a Content Amplification System Create a standardized process for transforming primary content: 🟢 Clip Selection: Identify the most valuable 60-90 second segments 🟢 Newsletter Extraction: Pull actionable insights into structured takeaways 🟢 Follow-up: Flag opportunities for deeper exploration in other formats 🟢 Cross-Promote: Build CTAs that direct audiences between channels 4. Maintain Channel-Specific Identity While leveraging the same source material, respect each channel's unique characteristics: ▪️The podcast maintains full depth and context ▪️Social clips highlight emotional moments and quick insights ▪️Newsletters structure and organize the most actionable takeaways ▪️Webinars enable visual demonstration and live interaction 5. Use Audience Feedback to Refine the System All this output is going to give you a TON of valuable signals that will guide your future content plans. It's your new competitive advantage. Don't sleep on it! Nick and Armand discovered their audience wanted longer webinars despite their "30 minutes" brand identity. Listen to your audience and adjust your content format accordingly while maintaining your quality standards. The True Power of Your Flywheel When your content system begins spinning, growth is inevitable: 👉🏻 Podcast listeners discover your newsletter 👉🏻 Newsletter subscribers attend your webinars 👉🏻 Webinar attendees follow you on social 👉🏻 Social followers become podcast listeners This isn't just about efficiency—it's about creating space for creativity, strategic thinking, and actually enjoying your work again. And that's a great recipe for healthy growth. What's your take — are you building a flywheel? _ PS: If you want more growth tips, check out the full episodes of Reed Between The Lines - now on YouTube, Spotify and Apple.

Roblox x Shopify will now let any eligible creator sell in-game merch. At RDC last year, Roblox announced its Shopify partnership — letting select creators and brand experiences sell real items in-game. Today, that program has been opened up to the larger ecosystem. Here's what I expect to see: 🛍️ The rise of Shopping Games Games like Catalogue Avatar Creator — which let users try on digital items before purchasing — have always been a staple. Now, they can serve the same purpose physically. Currently, many fashion/shopping brands are coming to the platform and making their own experiences. I expect we'll see more integrations soon (and I suppose Walmart is the first to dip their toes in with "Dash Pass.") 👗 Creator Fashion Lines We've already seen top Roblox designers collab with physical brands, but will eventually see top games as the new major fashion shows. Dress to Impress was the #1 virtual fashion phenomenon last year, becoming the #7 top trending topic on YouTube & tripling Roblox engagement on Twitch, on top of its more than 1 billion hours of play. Why aren't major brands launching their lines with them — and working with digital designers in figuring out what users want? 💰 The first $1B dollar dev Roblox CEO Dave Baszucki has long predicted a developer's business will one day cross the 10-figure barrier. With in-game pay outs the last month just barely above $1B, and every dev competing for their share of it, it can seem far off. But recall, though most top franchises find their audience digitally — on a screen for Disney, in-game for Pokémon — toys, merch, and consumer products regularly make up 50-80% of their revenue. Which means though budding franchises on Roblox may have already created their share of value: now they'll see it. Happy shopping 💅

When Teenagers Beat Investment Bankers at Asset Monetization Welcome to the Roblox economy, where teenagers are accidentally building the most efficient M&A market in history. Seven of the platform's 15 top-grossing games in June were acquisitions. That's a 47% market consolidation rate that would make private equity blush. While Wall Street debates whether SPACs are dead, a 19-year-old just closed a $3 million exit in three months. No pitch deck. No roadshow. Just pixels and soccer balls. Roblox's top 10 developers earned $36 million each in the past 12 months. The platform is projected to pay out over $1 billion to creators this year. For context, that's approaching the annual revenue of many Fortune 500 companies, distributed to kids who probably still have curfews. What's fascinating from a corporate finance perspective is how this ecosystem mirrors traditional M&A dynamics, but with compressed timelines and adolescent efficiency. The anonymous creator of "Blue Lock: Rivals" generated $5 million monthly revenue for Roblox Corp. before selling to Do Big Studios. That's a 60% monthly revenue multiple in a market where public gaming companies trade at 3-5x annual revenue. The real shift happened in December when Roblox updated its terms to allow game ownership transfers. Previously, these transactions violated community guidelines. Now, over a dozen companies actively acquire, develop, and flip Roblox games through Discord channels, creating a shadow investment banking network run by teenagers. Consider the risk-return profile: Games can die overnight when trends shift. A paintball simulator might become worthless when someone clones Rainbow Six Siege. This volatility creates pricing inefficiencies where creators sell at 1-2 months' revenue multiples, while others command 12 months' worth. It's venture capital meets day trading, mediated by parents' credit cards. The strategic implications are profound. Companies like Voldex Entertainment are building portfolios of virtual assets with real cash flows. Their acquisition of Brookhaven RP reportedly exceeded the $100 million Embracer Group paid for Welcome to Bloxburg in 2022. We're witnessing the birth of a new asset class where intellectual property created by minors generates institutional-grade returns. CEO David Baszucki predicts a Roblox developer will be valued at $1 billion by 2028. Given current trajectory, that seems conservative. When 100 million daily users create demand for virtual goods, and teenagers prove more efficient at monetizing than traditional studios, we're not just seeing a gaming trend. We're observing the future of content creation economics. While we debate ESG premiums and SPAC structures, Generation Z is quietly building the next iteration of digital asset markets. Perhaps it's time to add Roblox to our comp tables. #CorporateFinance #DigitalAssets #Gaming #Valuations #Innovation #TechTrends #Investment #M&A #StartupValuation #FinTech

The Partnership Death Cycle is what every company should avoid Too many partnerships fail—not because the strategy was flawed—but due to unrealistic expectations from the outset. Here’s how the death cycle unfolds: 1. Unrealistic expectations are set Leadership expects partnerships to deliver immediate results—often demanding ROI in the same timeframe as direct sales. 2. Resources are cut or never fully committed When quick wins don't materialize, the company pulls back on crucial support like dedicated teams, integration resources, or marketing enablement. 3. Partnerships struggle in a compressed timeframe Without sufficient support, partnerships can’t drive the results expected, leading to more pressure and less time to succeed. 4. Blame is placed on the partnership, not the process Ultimately, the partnership is seen as a failure—when in reality, it was never given the right environment to thrive. Here’s how to break the cycle before it starts: 1. Set realistic expectations early Partnerships are long-term investments. Make sure your CEO, board, and cross-functional leaders understand that the ROI from partnerships doesn’t follow a typical sales cycle. Expect a 12-18 month runway to see real, measurable results. 2. Allocate proper resources from day one Partnerships need more than just a team lead—they require full commitment across the organization. This includes dedicated integration support, a trained sales team, and marketing resources to co-create demand. 3. Measure the right KPIs Instead of only tracking short-term revenue, focus on KPIs that reflect the true health of a partnership: joint pipeline creation, partner enablement progress, and the completion of key integrations. These are the milestones that drive long-term value. 4. Understand that partnerships need time to grow Partnerships need time to build trust, integrate offerings, and develop shared go-to-market strategies. It’s not about instant returns—it's about sustained, compounding growth. Break the cycle by committing upfront, supporting your partnerships with the right resources, and playing the long game. That’s how successful ecosystems are built.

Platform partnerships in AI just got more complex. 🤔 Windsurf's recent challenge with Anthropic limiting their direct access to Claude models highlights a critical lesson for any B2B SaaS company building on third-party AI platforms: your strategic partnerships can make or break your competitive position. Here's what's particularly interesting from a business strategy perspective: 🎯 Market positioning matters: While Cursor, Devin, and GitHub Copilot received direct Claude 4 access at launch, Windsurf didn't - despite being a major player in the AI coding space. This wasn't just a technical decision; it's a strategic one. 💡 Platform risk is real: Windsurf built their business to $100M ARR, but now faces the classic platform dependency challenge. When your core product relies on external APIs, you're essentially building on someone else's foundation. 🔄 Competition drives innovation: Anthropic launching Claude Code and investing in their own coding applications shows how platform providers increasingly compete with their own ecosystem partners. Having scaled SaaS by carefully managing platform relationships, I've learned that diversification isn't just smart—it's survival. The companies that thrive are those who: ✅ Build multiple vendor relationships ✅ Invest in platform-agnostic architectures ✅ Maintain direct user value beyond platform dependencies For founders in the AI space: How are you preparing for platform risk? Are your strategic partnerships diversified enough to weather changes in access or pricing? The AI landscape moves fast, but strategic thinking about platform dependencies moves even faster. 🚀 Read more: https://lnkd.in/gQdHZ9HR

Data teams are becoming software engineering teams. On December 14th we welcomed Philip Zelitchenko, VP of Data from ZoomInfo, to talk about how he has built this discipline within his team & it was fascinating. The video is here : https://lnkd.in/gBFwkTqq Like the Devops movement, the Dataops movement aims to scale the use of data within companies without increasing the headcount of the data team. To do that, Philip defines data products using DPRDs, structures his data team with five key roles, & defines clear roles between the data team & others in the company. DPRDs, or Data Product Requirements Documents, contain the key information about a data product: what it will provide, how it will produce value, how the data will be governed including data quality alerting. Unlike code, data is stochastic or unpredictable. Data may change in size, shape, distribution, or format. This adds an additional dimension of complexity to the DPRDs. In addition to the DPRD, the ZoomInfo data team employs TEP or technical execution plan that aligns the internal technical teams on architecture & governance. The data team has five key roles: 1. Data PMs : quarterback the DPRDs. They gather feedback from users, define the value, solicit feedback from the rest of the team, then manage the execution of the plan. 2. Business logic : the data engineering team build the ETL pipelines while the data science team researches & implements machine learning algorithms for ML\DS driven data products. 3. Data analysts : embedded/seconded to the different operating teams, analysts analyze the data each team needs using the infrastructure provided by the data platform. 4. Data governance : ensures data quality/accuracy, defines the access control policies for security, sets the operating procedure for alerting & monitoring, and help define data contracts between producers, processors, and consumers. 5. Data platform : builds the universal data infrastructure for the company. Last, the ZoomInfo team is building an internal product called Heartbeat that measures usage across the main data products, evaluate the priority, SOPs for impact on SLAs and communication with data practinioers across the org in an automated way. For Philip, leading the data team is about focusing on the data products that drive meaningful value to the company. I learned a tremendous amount about the way modern data teams, who leverage software engineering disciplines, operate. Thank you, Philip!

How AWS Built an Army without a Sales Team Doing All the Work In the early days, AWS wasn’t just selling cloud services. It was building an entire ecosystem of partners who would sell for them. The goal? Let every consultant, tech firm, and SI (System Integrator) become an extension of their BD team. Here’s how they did it: Launched the AWS Partner Network (APN), giving 1000s of firms access to certifications, deal registration, and co-selling. Provided shared marketing assets, so partners could pitch AWS like insiders. Created co-branded case studies and joint wins, building credibility fast. Offered pre-sale support, so partners never felt alone in the room. Rewarded loyalty with better tiers, leads, and incentives. The result? In less than a decade, AWS didn’t just grow; it created an industry of AWS-first businesses. The Impact? 100K+ partners worldwide $80B+ annual revenue U.S. public sector dominance through trusted partner-led delivery BD Lesson: Partnerships aren’t “extra”, they’re a BD multiplier. Have you closed deals through a partner or built a partner program that worked? Let’s hear your approach in the Comments #BusinessDevelopment #Partnerships #AWS #BDStrategy #Alliances #CoSelling #CloudGrowth #GovCon #Consulting #LinkedInSeries #GoToMarket

You must approach a data platform in three layers: 1.) Code - What engineers care about, 2.) Data - What analysts care about, 3.) Business Logic - what business users care about. If you do not have a multi-tiered approach that captures the RIGHT information from each layer, your data platform and strategy will be an incoherent mess that struggles to gain adoption. Each persona in your audience will only have a partial amount of the information they need, and your stakeholders will constantly demand new things that feel disjointed. Cataloging, Monitoring, Lineage, and Data Contract are not TOOLS. They are patterns. And each pattern has a different application and use case depending on the persona. For example, most Software Engineers do NOT care about the data itself. They work with software and code. What they are willing to own is the code that produces data, and systems/software that allows them to manage their data code in a more structured way. Therefore, a catalog that only focuses on data is less than useless for a software engineer because they have no context on how to apply this to their day-to-day work and adds significant overhead. This is where Data DevOps is critical: - Data Contracts are enforced in CI/CD and prevent backward incompatible changes like integration tests and unit tests for data code - The Catalog captures code owners. Engineers who manage repos that produce ultimately produce data, the repo list, who has made changes over time, events, and other sources - Code-based lineage focuses on how code moves data across services as a dependency graph - Monitors exist to help teams understand when new data code is being created, if its following the expected patterns, and how data code is being changed If engineers do not have a Data DevOps system, they will NEVER adopt (or push back strongly) against a system that requires they take ownership of the data itself. Asking an engineer to own data, without first helping them own the code that produces the data is totally backwards. So in short - don't buy a cataloging tool, or a data contract tool, or a monitoring tool. These are features that enable a particular workflow for a certain group of people within your business. Once your platform begins to view this functionality as layers that must work together cohesively, your platform initiative will explode in terms of adoption and value. Good luck!