Luke Phillips @LukePh

Recently had a friend assaulted for being Jewish. Sad times. All hate is wrong. We’re all one.

@MarcLore Vision, Capital, and People! Thanks for sharing that framework

@JTLonsdale @thomasrorystone @brivael Something you won’t see in the news: magazine.columbia.edu/article/why-de…

This is the complete playbook for building a seed-strapped consumer/B2B SaaS in 2026. (Assembled from 25 years of operating, one near nine-figure exit, and 50+ investments at OpenSky Ventures)

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Chris Hohn did a 90-minute sit-down with Nicolai Tangen and then dropped an investor letter the FT got hold of last week. You’d think the guy who printed a record $18.9B last year would be doing victory laps. Instead he’s quietly rewiring his whole portfolio. My favorite takes from both: 1.The most important thing in investing isn’t growth. It’s barriers to entry. Growth without a moat is the airline industry: 5% volume growth for 100 years and basically zero cumulative profit. 2.There are only about 200 companies on earth he considers high-quality and investable. His fund holds 15. 3.Average holding period: 8 years. Some positions 13. “You have to hold the company forever, because the stock market may be at very bad prices when you want to sell.” 4.His real test for a moat: can the company price above inflation? A 20% margin business that prices 1% above inflation grows profits 5% faster than revenue. Forever. Almost no companies can do this. 5. Industries he won’t touch: banks, autos, retail, insurance, tobacco, asset managers, fossil fuel utilities, airlines, wireless telecom, media, advertising. On banks: “sooner or later someone without a lot of intelligence comes to run them, and then it can be toxic.” 6.On AI generally: call centers go bankrupt. Indian outsourcing coders are next. But for everyone else, AI lowers costs and raises productivity. Companies with real moats become MORE valuable. 7. Here’s the punchline. The FT got hold of his investor letter. He cut his Microsoft stake from 10% of the fund to 1%. Roughly $8B sold. He’d held it since 2017 through a 400% rally. His reason: AI could disrupt Office and Azure faster than the market thinks. 8.He moved that capital into Alphabet. Doubled it from 3% to 5%. Now his largest tech position. The world’s best quality investor sold Microsoft and bought Google because he thinks Google’s moat is more durable in an AI world. Not the consensus trade. 9.The underlying thesis: “AI eats software.” If AI agents do the work humans used to pay per-seat SaaS licenses for, the whole SaaS model gets re-rated. Oracle, Adobe, Salesforce all ~40% off highs. Microsoft 25% off. Market is starting to agree. 10.When to sell? Not when something gets expensive. When conviction drops. Valuation is one variable, conviction is the other. What kills you isn’t being wrong, it’s permanent loss of capital. 11.He admits hardcore activism doesn’t work anymore. Too much of the shareholder base is passive index funds. And even when activism wins, you usually win in a bad business. “The business always wins.” 12.Counterintuitive take: there are more good companies in public markets than in private equity. The best businesses are too big for PE to buy. And when public companies sell something to PE, they’re selling the assets they want to get rid of. 13.On intuition: “thinking without thinking.” Pattern recognition from 20 years of reps. It’s how he sniffed out Wirecard while the German establishment was defending it. “Most investors trust authority too much.” 14.He basically stopped shorting. “You’re going to be eventually right but not be able to fund the losses.” The first guy to short Wirecard had to cover 19 years before it hit zero. Buffett told him he and Charlie studied shorting and concluded it was too hard. 15.He gives almost everything away. ~$500M a year. $10 prevents an unwanted pregnancy in Africa. $40 saves a child from severe malnutrition. $50 prevents permanent blindness. 16.Tangen asks: advice to young people? Hohn, who runs the world’s most profitable hedge fund: “Go on a spiritual path.” The guy who made $18.9B last year ends the interview saying only purpose and meaning matter. The headline: the world’s best quality investor just sold his biggest tech compounder because he thinks AI is breaking the moat. Quietly, with conviction, on an 8-year horizon, while everyone else is still buying the AI winners of 2023.

Most SaaS founders fail because they scale acquisition too late. We did the opposite. We focused on distribution before product perfection. That’s how we went from $0 to 2,000+ paying customers in 9 months with 0 outside funding. Here’s the exact growth system we used at GojiberryAI: STEP 1: Validate demand before building Most founders spend months building features nobody asked for. Instead: we started selling before the product even existed. We created a simple 7-slide deck: • the problem • the workflow • the expected result Then we started outbound immediately. Using GojiberryAI + manual sourcing, we targeted high intent leads through LinkedIn and cold email. Weekly targets: • 180 LinkedIn invites • 1,000 cold emails • 4-8 demos per day That was enough to validate demand. The signal we looked for was simple: people asking: “How much does it cost?” Before the SaaS existed, we were manually selling curated high intent lead lists. Once enough companies bought repeatedly, we automated the workflow and turned it into software. The goal at this stage is not scale. It’s getting your first 100 customers as fast as possible. STEP 2: Turn customer results into growth Once customers started getting results, distribution became easier. Our biggest unlock: Reddit. Not ads. Not SEO. Not partnerships. Just giving massive value publicly. What worked: • commenting on viral posts • sharing tactical breakdowns • posting real customer wins • explaining exactly how we generated pipeline No corporate branding. No polished marketing language. Just actionable content. At the same time: we kept outbound running aggressively every single day. First hires: 3 Customer Success Managers. Retention and customer wins became growth loops. STEP 3: Build an inbound engine Once we crossed ~$25k MRR, we doubled down on LinkedIn. Every person on the team: • posted daily • handled outbound daily We only published lead magnet style content: • frameworks • templates • playbooks • case studies • experiments And every post had the same CTA: “Comment X and I’ll send it.” That single mechanic generated thousands of inbound leads. Every week, each team member created: • 1 new lead magnet • multiple distribution angles from it We also added: • free tools • customer stories • motion design videos • better landing pages New hire: 1 Product Manager. STEP 4: Layer distribution channels At this point, the system already worked. Now the goal became: add more attention sources. We expanded into: • X/Twitter • LinkedIn influencers • newsletter sponsorships • partnerships But we never stopped the channels that already worked: • outbound • Reddit • LinkedIn content Most founders abandon winning channels too early. We scaled them harder instead. STEP 5: Scale paid acquisition Once the organic engine was stable, we added: • Meta Ads • Google Ads • UGC creators • B2B influencer campaigns Then we redesigned the entire website around conversion. At the same time: we scaled hiring across: • growth • engineering • sales And massively increased outbound volume with GojiberryAI. Our philosophy is simple: More targeted attention → more conversations → more demos → more customers Most startups die from lack of distribution, not lack of product. Build distribution earlier than everyone else. That changed everything for us. If you want to try GojiberryAI with a 14-day free trial instead of 7 days: Comment “GOJI” and I’ll send you access.

talked to a YC company that scaled from $0 → $2m ARR in their first 6 months with their ENTIRE GTM built off going to conferences. Here's the playbook they cracked (step by step): ~4 weeks before: > Post abt the conference and tell attendees exactly how to reach you > Send personal DMs to the right ppl on LinkedIn and X > Reply within the hour & lock in 10 top targets to close. > Send everyone else to your drip email campaign. Then, set a meeting block of 1-3 days during the conference: > make shared booking link for the team > Reserve a quiet café / private dining room > Pack in 12 meetings per day, 30 min each, with buffer time built in While you're there: >Hand every prospect a thoughtful small gift and a personal card >Single out 5 standout customers whose pain ur product actually solves >Pull them aside for a casual on-camera Q&A in a solid film spot >Don't pitch hard. >Let the conversation breathe and weave your product in naturally. The 4 weeks after >Hand the raw footage to a freelance editor + ask for ~15-20 punchy clips with captions. >Drop a new clip every couple of days on LI / X > use these clips when you post online about the next conference to keep the momentum This is the formula, costs less than a few thousand dollars to execute. They’re on track to end the Y1 at ~$6m ARR (B2B, targeting large enterprises) + STILL not using any other channels for customer acquisition

Most people think smart people are better at solving problems. A lot of the time they’re just better at finding the real problem in the first place. “A perfect formulation of a problem is already half its solution.” This matters way more now because AI is making execution cheap. Almost anyone can now generate decent code, designs, research, presentations, automations, strategy docs etc. But lots of people are now just moving faster in the wrong direction. Scaling things nobody truly wants. Polishing weak ideas. Optimising low value work. Making bad decisions more efficiently. AI amplifies direction more than intelligence. If the underlying problem is wrong, speed doesnt help that much. Sometimes it actually makes things worse faster. That’s why clarity is quietly becoming more valuable than raw execution. The hard part increasingly isnt producing answers. It’s knowing what question is actually worth asking. What the real bottleneck is. And what problem is actually worth solving to begin with

@dara_venture 😂😂😂

Very underrated video. Felt like a fly on the wall. Went past your place @fredwilson when I went strolling. Love to say hi next time when I’m in NY. Agreed with Fred’s point around AI expanding creativity rather than reducing it. The shift isn’t just that existing creatives become more productive, but that more people become capable of creating in the first place. Feels similar with entrepreneurship and talent more broadly. Indeed, AI will get better at mapping what is already legible: sectors, founder backgrounds, patterns, networks, categories. But some of the most interesting people emerge before there’s enough structured data around them to make them obvious. This probably means the edge increasingly shifts toward recognising pre-legible signals early: obsession, originality, agency, curiosity, fast learning curves, and salesmanship to name a few. The same way AI may enable great creativity from people who were never previously seen as “artists”, I think we’ll increasingly see highly capable founders emerge from outside the usual filters too. Already starting to see some of this through Phareyes. Interesting times!

I sold The Hustle's first ads. About $100k worth. Here's the 1st cold email I sent that got a sale. It was Wealthfront. Maybe sent emails to 20 diff brands, 5-8 showed interest. We had 93,535 subscribers. I charged them $26 per 1,000 sends.

Hiring wrong can be costly. @bchesky delivers great tips here. m.youtube.com/watch?v=xpp2ha… Tldr: The best companies don’t “recruit.” They continuously map exceptional people long before there’s an open role. A few principles: • Don’t start from CVs. Start from results, then work backwards to the people behind them. • Great people know other great people. Every conversation should expand your network. • In interviews, the first answer is rarely the truth. The signal is in the follow-up questions and specifics. • References matter more than interviews. Exceptional operators can sell themselves better than most founders can assess them. Best reference question: - “Who’s the best person you’ve ever worked with?” If the candidate never comes up naturally, that itself is signal. Big takeaway: Hiring is less about processing applicants and more about building a long-term intelligence network around talent

@LinkedInHelp @hem1305 Hi, my account was suspended. I appealed but it’s still restricted. Please help. uk.linkedin.com/in/lukeph

@LinkedInHelp My account is: uk.linkedin.com/in/lukeph

@LinkedInHelp @fkfarhankhan148 Hi my account was suspended unjustly. I appealed but account remain restricted. Can you please help

@LinkedInHelp can you help me get access to my Linkedin account. Been several days! Very important

Been thinking about Ring as a pattern breaker. I keep coming back to how cleanly it fits the inflection → insight → idea sequence, even though it didn’t look that way at the time. @m2jr lays out a good framework. By inflection, I mean a real shift in the world that makes new behaviour possible. A few lined up at once. Phones became always on. Cameras got cheap. Cloud video became normal. At the same time, people felt less sure about what was happening around their homes, especially at the front door. That created possibility, but it didn’t tell anyone what to build. By insight, I mean the non obvious truth about what people will actually care about once the inflection hits. Most incumbents looked at the same moment and concluded people wanted better security. Ring seemed to notice something smaller and more human. People were asking “who was that”, “what just happened”, “did I miss something”. Not protection. Clarity. Reassurance. And by idea, I mean the specific product you build to deliver that insight. Once you see the job as clarity, the idea almost designs itself. A doorbell with a camera that sends you a video the moment something happens. One tap. Instant context. No installers. No monitoring contracts. No dashboards. What’s interesting is that this looked like a bad idea early on. A video doorbell sounds like a toy if you think the job is security. It only makes sense if you’re evaluating it against the right insight. That framing also explains why incumbents like ADT didn’t lead the shift. They could have built it, but doing so would have made their best customers question why they were paying for monitoring at all. The right move for users was the wrong move for the business. Most Ring clones missed it in a different way. They copied the object instead of the insight. Better cameras. More features. Smarter apps. But they aimed at rare disasters instead of everyday uncertainty. Ring didn’t really sell security. It sold clarity. A feeling of being present when you’re not. Curious how others read this, or if there’s a better counterexample. @JamieSiminoff m.youtube.com/watch?v=um-iVX…