Alexander Gildenberg

I find it hard to disagree with much in Andy Jassy's note here. I love the flexibility to not have to be in the office every day, but also thought most of us had that before covid. Modeling, learning, and building the shared language of teams is much easier to do in person. There are teams of high functioning individuals that can work effectively remotely (I've been part of several), but despite new technology, that's still the exception. The relevant portion: "To [better] invent, collaborate, and be connected enough to each other and our culture to deliver the absolute best for customers and the business, we’ve decided that we’re going to return to being in the office the way we were before the onset of COVID. When we look back over the last five years, we continue to believe that the advantages of being together in the office are significant. I’ve previously explained these benefits (February 2023 post), but in summary, we’ve observed that it’s easier for our teammates to learn, model, practice, and strengthen our culture; collaborating, brainstorming, and inventing are simpler and more effective; teaching and learning from one another are more seamless; and, teams tend to be better connected to one another. If anything, the last 15 months we’ve been back in the office at least three days a week has strengthened our conviction about the benefits. "Before the pandemic, not everybody was in the office five days a week, every week. If you or your child were sick, if you had some sort of house emergency, if you were on the road seeing customers or partners, if you needed a day or two to finish coding in a more isolated environment, people worked remotely. This was understood, and will be moving forward as well. But, before the pandemic, it was not a given that folks could work remotely two days a week, and that will also be true moving forward—our expectation is that people will be in the office outside of extenuating circumstances (like the ones mentioned above) or if you already have a Remote Work Exception approved through your s-team leader. "We are also going to bring back assigned desk arrangements in locations that were previously organized that way, including the U.S. headquarters locations (Puget Sound and Arlington). For locations that had agile desk arrangements before the pandemic, including much of Europe, we will continue to operate that way."

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I suspect that as AI becomes a real substitute for labor, a larger percentage of tasks that were previously bundled into 20th-century bureaucratic jobs will be rebundled into new jobs and a new type of software product. These new products will not quite resemble consumer software or enterprise SaaS. Instead of hiring someone for $100,000 a year, as a productive solopreneur, you might "hire" two to ten specialized software products that each cost hundreds or low thousands of dollars a month (digital alternatives for 1/100 to 1/10 the cost that are 50-80% as good...and work 24:7!) — software profit margins will converge with services, BUT fixed costs will fall, relatively (some might argue this is already the case as servicing enterprise SaaS customers is actually pretty labor intensive, and those costs are somewhat wrongly assigned to SGA, S&M, R&D, etc.). I think this is more likely than having one assistant to rule them all; that said, I am exited about Operator, et cetera (et al?). While some may choose to consolidate their digital interactions to one or two AI delegates, the more entrepreneurial among us will probably maintain a much more fragmented set of interactions. You may have your "AI executive team," with functional specialties and perhaps a "chief of staff" or even multiple. Additionally, there could be numerous levels where you're interfacing directly with external customers more frequently for tough problems, or even with their delegates, depending on your position in the capital stack — and taking action at different levels in a single day. More like being a central node in a fluid network than (exclusively) a chief executive atop a hierarchy. This future is neither dystopian nor utopian. (And much of how things were done in the 20th and early 21st centuries may not change significantly. I wouldn't be surprised if by the 22nd century, there were still people using fax machines or texting with their thumbs.) However, toward the expanding frontier, there will be a Cambrian explosion of configurations regarding how you participate in the global economy. This will involve a lot of creative experimentation with the information technology scaffolding around yourself and between yourself and collaborators and customers; this is happening now if you know where to look. I don't think it’s particularly useful to make predictions about the future with a "good" or "bad" valence. What's likely is the "weird and alien" dials will get cranked up for many folks, especially the younger Gen Zs & Alphas, as they start entering the unconventional workforce that exists aside tracked careers — some of those details are actually worth meditating on, but are a bit harder to think about + are uncharismatic, so posting about them gets ltd attention, because they share little context with the present ways of working. I'd like to see more of these posts, personally... What are some emerging norms you're seeing downstream of AI adoption?

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Consigning "boring meetings" to the artifact dustbin is a great idea. But not all good meetings are "decision meetings" and decision meetings as currently conceived can be just as deadly boring and ineffective as any poorly managed pre-meeting. Well-managed "discovery meetings" can precede a decision meeting and be invaluable: creative, energizing, engaging, and ultimately more collaborative -- resulting in higher probabilities of success for any subsequent decision meeting. It's not just about fewer meetings, it's about better meetings.

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When there is great uncertainty, there can't be predictability to any useful level. We have to reduce uncertainty to where it can be managed effectively, and we do this by running experiments... trying things. Does the stakeholder like what we've done? Does the customer like what we've done? Are we solving the problem that customers have? This puts us in the 'complex' quadrant of the Cynefin Model, where we have to try and (perhaps) fail, perhaps repeatedly, until we know enough. Yet, we still have a goal in mind and we at least know the first few things that need to get done... or the first thing we need to test to determine feasibility. Apple knew that unless it could source a ruggedized hard drive that would withstand being carried and occasionally dropped, the iPod would not be successful... so getting a commitment from Western Digital (who had been working on such a drive and was almost ready to ship) was the key decision point. The Wright Brothers knew that unless they could figure out a way to turn their airplane without 'skidding' and stalling a wing (a problem that had killed many of their inventive contemporaries) they couldn't build a safe airplane... oh, and find a lightweight, powerful-enough motor while you're at it. It all comes down to reaching decision points: do we know enough to know whether to continue, or whether to cancel our effort? In the example below (waterproofing a below-ground basement), we are going to have to continue. Therefore, we want to continue only at the pace at which we are certain of the decisions we're making. Is what we're doing working? If so, do more! If not, do something else. That is exactly what the contractor did in the post below, making 'MVP' experiments until the path to success was clear, until they had the 'Aha!' moment. So, when do we get predictability with useful accuracy? At the 'Aha!' moment. Figure out what 'Aha!' moments are needed, devise experiments to reach those moments and drive enough uncertainty out (you'll never get to zero) to where the range of uncertainty is acceptable. #predictability #forecasting #estimation #commitment #agile #planning #riskmanagement #projectmanagement

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Good article. I've started or been in 3 startup companies and I think we may have had just as many meetings, they just came in different forms. Constant meetings "after work" eating dinner with each other.. not always because we wanted to eat dinner together again. Pretty much an ongoing design meeting during coding sessions across the desk from co-workers. Meetings going to lunch when we had time. So many ad hoc meetings when something went well... or more often really bad. Celebratory meetings.... (we never called them meetings). In fact, we had more meetings when it was 5 of us and the number of meetings went down linearly as we grew larger. So, I'm not sure meetings or the number of meetings is the issue, it's the need, purpose, and content of the meetings if you want to act like a "startup".

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AI pricing is hard. Customers (esp enterprise) need to do annual budgeting. They won’t sign up for “who knows what it will cost???” But the AI provider (whether OpenAI or something built on it) have highly variable costs based on usage. In addition, if AI increases productivity, it may **reduce the number of user accounts a company buys**. As you provide more value, you (as the AI provider) can see less revenue. No one has entirely solved this.

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Being able to balance the polarities like clarity with complexity, change and stability, are key when driving change to turn organisations around. Common gaps in organisations looking to create shifts: 1. Lack of an in-depth understanding of the problem where the value is created - Talk to the people in a trusted and trusting manner 2. Rushing to solve an unclear problem - See #1 and map out the connects to see the disconnects 3. Clear framework for problem-solving to make the shift - Communicate this widely, not just in paragraphs of words in a mass email to staff, have the digestible visuals. Now, time to experiment with this 💫 #changemanagement #leadership

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Is it time to short Google? For the first time, Google's US market share in search has dipped below 50%. 📉 The big question is: Who's going to take a bigger bite out of that pie? Amazon is a major contender. OpenAI and Perplexity are changing the game with answers vs sponsored links and TikTok has turned into a discovery powerhouse, reshaping how Gen Z finds information. The Wall Street Journal recently reported on Google's struggles to maintain its dominance, it’s clear the ground is shifting beneath the tech giants. But here’s why I think this is the wrong question. We should ask: What will be the new portal to the web for Gen Alpha (born 2010-2024)? Just as Facebook and TikTok competed on different axes using social & display ads—the next evolution of search and interaction with the web might be radically different in the next 10-20 years. 👉 Will kids be typing into a search box? I doubt it Will it be through: 🗣️ Voice assistants like Alexa or Google Home? Simply talking to an AI agent? 🌐 Immersive AI/AR headsets like Apple’s Vision Pro or Meta’s Quest Pro? The future portal to information could look nothing like today’s search bar. What do you think? Who will define the next era of discovery?

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My boss Brad Wiskirchen used to say "10% of CEOs have a fraud problem, but 100% have a revenue problem". That was certainly my experience. Rarely was someone compelled by a claim we'd drop their fraud rate 30%. But if we could help them launch cross border sales or enter a risky market without losing their shirts, they were sold. It took me a while to figure out how to sell to Defense Advanced Research Projects Agency (DARPA). They only want generational improvements in the state of the art: advance it by 20 years. I'd lose a proposal because they thought it was too speculative, so I'd submit the next one with a clear path. They'd say it looked like I knew exactly how to go about it so I should talk to someone funding near term projects. Finally someone gave me a concrete description of what "strategic" meant to DARPA. When SHTF in the world, they put some options in front of the president. A strategic capability meant there was another folder on the Resolute Desk. When I saw the newspaper after the Bin Laden raid, that thought hit home. Once we found him, the president's options were 1) ask Pakistan to turn him over, knowing that he'd probably get tipped off and escape or 2) go in hot and suppress air defenses 35 miles from the capital of an allied country. Except - probably thanks to DARPA - there was 3) send in stealthy helicopters in a quiet raid with limited collateral damage. The #AI industry needs to take this lesson. No one is excited about cutting call center employees or making emails easier to write. There are other ways to save money, probably with better ROI, that your customer is already passing on because they would rather deploy their resources toward top line growth. Your pitch needs to explain how the product provides a capability that didn't exist before. Something you make possible that is impossible otherwise. The customer is not going to risk bleeding edge tech paying SaaS margins to try to save money. It's like buying a Ferrari because you'll save gas by getting to your destination faster. No one believes that. The value of the Concorde wasn't that it got you to Europe in an hour or two less. It was that you could suddenly make meetings the same day you traveled. This is why I'm unimpressed with all but a few GenAI pitches I've heard. Most of them take some marginal activity and claim it'll be cheaper to do with AI. Few of them actually put new options on the CEO's desk. And CEOs probably don't have the problem you are solving for. But they all want more revenue.

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My boss Brad Wiskirchen used to say "10% of CEOs have a fraud problem, but 100% have a revenue problem". That was certainly my experience. Rarely was someone compelled by a claim we'd drop their fraud rate 30%. But if we could help them launch cross border sales or enter a risky market without losing their shirts, they were sold. It took me a while to figure out how to sell to Defense Advanced Research Projects Agency (DARPA). They only want generational improvements in the state of the art: advance it by 20 years. I'd lose a proposal because they thought it was too speculative, so I'd submit the next one with a clear path. They'd say it looked like I knew exactly how to go about it so I should talk to someone funding near term projects. Finally someone gave me a concrete description of what "strategic" meant to DARPA. When SHTF in the world, they put some options in front of the president. A strategic capability meant there was another folder on the Resolute Desk. When I saw the newspaper after the Bin Laden raid, that thought hit home. Once we found him, the president's options were 1) ask Pakistan to turn him over, knowing that he'd probably get tipped off and escape or 2) go in hot and suppress air defenses 35 miles from the capital of an allied country. Except - probably thanks to DARPA - there was 3) send in stealthy helicopters in a quiet raid with limited collateral damage. The #AI industry needs to take this lesson. No one is excited about cutting call center employees or making emails easier to write. There are other ways to save money, probably with better ROI, that your customer is already passing on because they would rather deploy their resources toward top line growth. Your pitch needs to explain how the product provides a capability that didn't exist before. Something you make possible that is impossible otherwise. The customer is not going to risk bleeding edge tech paying SaaS margins to try to save money. It's like buying a Ferrari because you'll save gas by getting to your destination faster. No one believes that. The value of the Concorde wasn't that it got you to Europe in an hour or two less. It was that you could suddenly make meetings the same day you traveled. This is why I'm unimpressed with all but a few GenAI pitches I've heard. Most of them take some marginal activity and claim it'll be cheaper to do with AI. Few of them actually put new options on the CEO's desk. And CEOs probably don't have the problem you are solving for. But they all want more revenue. (Photo of tail section from stealth helicopter that someone sold not because it reduced metrics by 20%, but because it provided capability for counterterrorism operations without suppression that didn't exist before.)

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When you strip away the complexity of code, the stack choices, and the never-ending pressure to innovate, leadership in tech comes down to one simple truth: it’s not about the technology—it’s about the people. I've been in those rooms, when the real challenge isn’t a technical hurdle, but how to steer a team through tension, uncertainty, or even burnout. It’s those moments—where trust, empathy, and clear leadership make or break the situation—that define whether a team succeeds or falters. This journey has taught me something most dashboards don’t capture: real leadership isn’t about pushing harder, it’s about knowing when to pull back, to make sure your foundation is strong. Because talent alone isn’t enough—trust is what carries teams through the hardest days, and relationships are what keep clients by your side when the going gets tough. It’s not just code we’re building. We’re building trust, partnerships, and environments where people can excel. That’s what leadership is. And it’s not easy—but it’s everything. #Leadership #Technology #Innovation #Growth #CTOChallenges #SoftwareDevelopment #SoftwareEngineeringLeadership #TechEcosystem https://lnkd.in/eK8rNQcu

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I love contract data because it tells me that one failed contract renewal can create 11 million angry customers. When the DirecTV-Disney carriage agreement expired on September 1st, a whole bunch of Disney-owned channels went dark for DirecTV’s 11 million customers. This is especially bad news for tennis fans, who can no longer watch the US Open finals currently broadcasting via ESPN. With good contract data, I can assess all expiring deals ahead of time, making sure to focus special effort on the most disruptive and damaging non-renewals. This is reason 096 why #ilovecontractdata. Follow me to discover many more. #contracts #contractdata #contractrenewal #renewals #nonrenewal #expiry #stalemate #broadcasting #TVrights #datadriven #dataquality #contractmanagement #contractintelligence #contractanalysis #contractreview #contractanalytics #contractAI #ContractAIthatscales #datanerd #legaldata #legaltech #legalops #data #AI 

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GenAI is making leaps and bounds in accelerating coding — but here's the catch… …you only see benefit if actual coding is the bottleneck. In a world of decision, approval, and prioritization bottlenecks, GenAI isn't a magic fix. This is why **Value Stream Optimization** becomes *more* critical with the rise of AI. My Takeaways: - DORA metrics are great, but they don’t provide an end-to-end view. Understanding your flow is essential. - End-to-end visibility empowers you to prioritize improvements. Without the data, you’re just guessing. - Optimizing flow means nesting and cascading value streams down to the team level. - Shifting to a product-centric organization? It’s a team effort! Strategic Portfolio Management (SPM) and Lean Portfolio Management (LPM) are your guiding lights. Hear more insights from Keith Buehlman and Mik Kersten on a fascinating episode of Catalyst, a podcast from Launch by NTT DATA, Inc. 🎧 [Listen here!] https://lnkd.in/dizSPJ3j #ProjectToProduct #ValueStream #AI #GenAI #ProductManagement #DigitalTransformation #CatalystPodcast

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NVIDIA tops the Axios Harris Poll 100, which ranks company reputations. FYI, this poll is done using this three-step process: 1. January 2024 Survey: 6,273 Americans identified the 100 most visible companies. 2. March 2024 Survey: 16,500 Americans rated these companies on nine reputation dimensions, creating a Reputational Quotient (RQ®) score. 3. May 2024 Surveys: Two waves of surveys provided additional context on brands and politics.

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Incisive analysis by Sequoia partner David Cahn on the missing $600 billion in AI revenue... Basically, given what companies are spending on Nvidia's chips and infrastructure build out, a certain amount of resulting revenue is implied. There is a $600 Billion gap. This week's The Economist arrived at a similar conclusion. The ROI will likely be realized after 2032! Links below. #ai #chatgpt #nvidia #venturecapital #startups

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As the technology staff layoffs continue, there has been an increase in the number of projects trying to combine roles to reduce costs. It's a concerning trend to see jobs with conflicting responsibilities being combined to save money. However, this short-term saving could lead to significantly higher costs in the medium and longer term, weakening the business and setting the stage for future problems. Today, while speaking with a client, it became apparent that while what they needed to hire at this point was an engineer who could create a tool to scrape data, they were going to dress this up as three roles in one post, one part of the role being the need for a database designer, the second part being a senior developer and the third person being a security expert but sadly this is where the issues began. Humans being human means that allowing them to design, populate and monitor the data quality and legality of using the populated data following best practices (both technically and GDPR) is messy. The role needs to splitting into at least two roles, the first being technical. I always argue against combining Database Design and DevOps as both are specialist roles in their own right. However, at least they are both technical and, in a small team, can report to either the lead developer or, more likely, the CTO (Chief Technology Officer). The second role is a non-technical role; the data management is a compliance role and, as such, should be managed by a data controller, with the data controller reporting to the CIO (Chief Information Officer). This underscores the need for technical expertise in data management. While reading this, I am sure you have spotted the flaw in this model; you are asking the person who is responsible for the quality of the data to be the same person who is designing the system to house the data and to build the tools to collect and collate the data. This vulnerability is enormous; any GDPR breaches are unlikely to be spotted as it would require the person who checked the data to whistle-blow on the person who collected the data or built the database. If this is the same person, they are unlikely to whistle blow on themselves, risk a considerable fine or their job role at the company. So, how do we mitigate these risks on a tight budget? In short, by sharing some of the roles with relevant people with industry experience, using advisors / non-exec directors (like myself) helps. It demonstrates to investors and customers that you have mitigated some of the risks in handling their personal data and money. I am amazed at the number of projects that do not consider these issues or bury their heads in the sand, hoping things will be okay. If you have any questions or would like help in preparing your business for capital raising please book a call using the link below.

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Creating templates and guardrails for your team is one of the most important elements if you ever want to scale. When everyone is doing their own thing it’s hard to make sense of the data and makes it even harder to revisit months later. That being said, you still need to be open to changing those templates and rails when appropriate. Don’t get to stuck in your ways or you’ll miss out on big opportunities.

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I'm a big fan of how Drew Houston (Founder & CEO, Dropbox) compares running a tech business (💻) to playing StarCraft (🎮), specifically the "micro", "macro", and "meta" games that are all happening at once ... I'll add my own perspective on how these 3 "games" show up when playing basketball (🏀) and running a sports business (🏟️): 1. "MICRO" game: 🏀 Basketball - Your skill at ball handling, your shooting technique 🎮 StarCraft - What is your click rate, how efficiently can you complete tasks 💻 Tech business - How quickly are you shipping new products. How engaging is your design 🏟️ Sports business - How engaging is your in-stadium experience, how efficient is your customer service 2. "MACRO" game: 🏀 Basketball - Do you have solid schemes and plays to get open looks and to defend appropriately 🎮 StarCraft - Are you managing your resources and expanding your area in the most effective way 💻 Tech Business - Are you positioning yourself to reach the right new clients, are you building products that have stickiness and defensibility 🏟️ Sports business - Does your marketing and outreach reach the right segments of your local community, are you positioning the sport for strong media distribution 2. "META" game: 🏀 Basketball - How do analytics and rule changes dictate more effective team composition (e.g., focus on "3-and-D") 🎮 StarCraft - How are they "weights" and "dominant strategies" changing within the game mechanics 💻 Tech Business - What trends within the technology ecosystem can you engage in order to gain market share and strengthen your position (e.g., AI trend) 🏟️ Sports business - How does your team/league/sport engage younger audiences and develop storylines that resonate for fans __ On a personal level, I have very much enjoyed playing Activision Blizzard's StarCraft, though not nearly at the level of mastery for any of these 3 "games"! I enjoyed the comparison to broader business themes. While the metaphor can be seen as silly on the surface, I enjoy the humility that comes with comparing large and unwieldy business challenge with much more approachable "games" that millions of people engage with daily. 💡 The "meta" game in the sports industry has many exciting trends and themes that are changing the landscape - new leagues, media rights changes, growth in sports betting, and more. __ 🎙️ Link to the full episode of Lenny Rachitsky podcast - a great listen: https://lnkd.in/eSffAnG8 Lenny, if you're reading this, count me as a supporter of the show! Would be thrilled to hear your unique and insightful questions to Founders, CEOs, and Presidents who are building in the sports ecosystem. __ What other areas can you apply this framework of "micro", "macro", and "meta" games? ⬇️ 💬

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