My businesses, investments, nonprofits / philanthropy, passions, activities and life.
Alternative DAF platforms enabling for‑profit impact: Invest with your Donor Advised Fund
Several specialized donor-advised fund (DAF) platforms now enable donors to make for‑profit, impact‑oriented investments in much the same way LOHAS and Realize Impact do, Alternative DAF platforms enabling for‑profit impact LOHAS – Provides DAFs that can invest in any private‑sector impact company, fund, or even entertainment production with an impact thesis, explicitly positioning itself as […]
Several specialized donor-advised fund (DAF) platforms now enable donors to make for‑profit, impact‑oriented investments in much the same way LOHAS and Realize Impact do, Alternative DAF platforms enabling for‑profit impact LOHAS – Provides DAFs that can invest in any private‑sector impact company, fund, or even entertainment production with an impact thesis, explicitly positioning itself as an alternative to “traditional” DAFs that restrict impact investing in for‑profit ventures. Realize Impact (PHIGs) – Operates as an intermediary that works with any existing DAF. Donors recommend an impact investment; Realize Impact does diligence, makes the debt/equity/revenue‑based investment, and donates 99% of the returns back to the donor’s recommended DAF or other nonprofit for redeployment. RSF Social Finance – Double Impact DAF program – Allows DAF holders (even at other sponsoring institutions) to move idle, ungranted DAF assets into RSF’s Social Investment Fund, which provides customized financing to social enterprises regenerating communities and the planet. Community foundations using PRIs (e.g., East Bay Community Foundation) – Some community foundations now allow DAF holders to recommend program‑related investments (PRIs) from their DAFs into nonprofits, for‑profit businesses, or even individuals, as long as these investments further the foundation’s mission (for example, a Kaiser Permanente DAF loan of 5M USD to support affordable housing). Specialized independent sponsors and advisors – Research from Social Finance and others documents a growing field of sponsors and advisors experimenting with impact‑first DAF offerings and advisor‑managed impact portfolios, responding to donor demand for impact investing options. These players collectively demonstrate that DAF capital can go beyond traditional grantmaking to finance market‑based solutions with clear social and environmental goals. Donor‑advised funds have become one of the fastest‑growing tools in philanthropy, but the bulk of these assets still sit in conventional portfolios, passively invested in public markets with little connection to donors’ missions. A new generation of DAF sponsors and intermediaries is changing that by enabling for‑profit impact investments directly from DAF accounts. This shift is unlocking a powerful pool of catalytic, risk‑tolerant capital for social entrepreneurs and communities. Platforms such as LOHAS give donors wide latitude to invest DAF dollars into private companies, funds, and even impact‑oriented film and media, as long as those ventures advance a social or environmental goal. Where traditional DAFs often limit donors to grants and public‑market ESG funds, these structures let donors back early‑stage solar startups, inclusive fintech, or mission‑driven entertainment that might never qualify for standard venture capital. Because the money has already been irrevocably donated, donors are not relying on these investments for personal financial needs, making them more comfortable taking lower‑return or higher‑risk positions when impact is strong. Intermediaries such as Realize Impact go a step further by decoupling “where you hold your DAF” from “how you invest it.” Donors make a grant recommendation from any DAF; Realize Impact performs due diligence, structures a debt, equity, or revenue‑based deal, and deploys capital into an impact‑driven enterprise. As investments generate returns, 99% flows back to the donor’s recommended DAF or other nonprofit, to be recycled into the next mission‑aligned opportunity. This closed‑loop model turns static charitable capital into a revolving pool that finances waves of impact over time, rather than a one‑time grant. DAF‑based impact investing also expands the toolkit available to foundations and community institutions. Community foundations that allow program‑related investments from DAFs can channel capital into for‑profit or hybrid projects that advance a shared mission, such as affordable housing, small‑business lending, or racial equity initiatives. In one example, a health‑system‑affiliated DAF provided a multimillion‑dollar loan to support affordable housing in a historically Black neighborhood at risk of displacement, aligning investment strategy with community health and equity goals. These flexible instruments can fill financing gaps for organizations that are too risky or too small for banks and mainstream investors. Market research underscores that donor demand for such options is substantial. Social Finance found that roughly 72% of surveyed DAF donors expressed interest in making impact‑first investments from their DAFs and would, on average, allocate about a quarter of their DAF assets to such strategies. At the same time, DAF sponsors and advisors are beginning to experiment with advisor‑managed impact portfolios and private‑investment sleeves inside DAF programs to meet that demand. By enabling for‑profit investments, these alternative DAF structures effectively blur the line between philanthropy and investing. They allow donors to back scalable business models that can tackle climate change, inequality, and global health challenges, while recycling returns into future mission‑aligned deployments. As more sponsors adopt this approach, the trillions of dollars expected to flow through DAFs over coming decades could become one of the most important engines of social innovation. In addition to DAF dollars being deployed to deliver social impact, there are investment funds such as Stand Together Ventures Lab (STVL) who specifically invest for social impact. If course, The Bill & Melinda Gates Foundation Trust also invests in a wide range of for-profit ventures to generate returns, focusing heavily on technology, sanitation, agriculture, and healthcare through both direct equity and large-scale stock holdings. Key holdings include Microsoft, Berkshire Hathaway, Waste Management, and pharmaceutical giants like Pfizer and Merck. Plus they are active in venture level investments. If you’re designing or selecting a DAF solution, and need some investment ideas, ping me on LinkedIn
Large Google Advertisers Demand Justice via Mass Arbitration for Ad Spend Recovery
The Quiet Reckoning: Large Advertisers Pursue Mass Arbitration Against Google for Ad Spend Recovery Thousands of companies that spent millions or billions of dollars on Google advertising over the last ten-years are now quietly mobilizing to recover what they believe were unlawful overcharges, and the scale of potential damages is staggering. Led by Chicago-based law […]
The Quiet Reckoning: Large Advertisers Pursue Mass Arbitration Against Google for Ad Spend Recovery Thousands of companies that spent millions or billions of dollars on Google advertising over the last ten-years are now quietly mobilizing to recover what they believe were unlawful overcharges, and the scale of potential damages is staggering. Led by Chicago-based law firm Keller Postman, a growing mass arbitration effort is targeting Google for monopolistic pricing in both search and display advertising, with total potential claims estimated to exceed $218 billion based on current data )mid-April 2026). For larger advertisers, those spending $5 million or more annually, this is not just a legal formality. It is a rare opportunity to claw back years of inflated ad costs that industry observers, agree with me were both systematic and preventable .latimes Running an agency (Didit) and in my role with the eMarketing Association I”ve concierged in over $1 Billion in claims myself. The Legal Foundation The mass arbitration cases do not start from scratch. They rest squarely on the shoulders of two landmark federal antitrust rulings against Google. In 2024, two separate U.S. federal judges adjudicated that Google had illegally monopolized the online search advertising market and the sell-side ad technology market, finding the company had abused its dominant position to artificially inflate prices paid by advertisers. Attorney Ashley Keller, whose firm Keller Postman has previously won mass arbitration cases against DoorDash, Postmates, and Intuit, stated plainly: “Two federal judges have already adjudicated Google to be a monopolist. It seems sensible to seek redress.”adexchanger+2 Because Google’s advertiser contracts contain mandatory arbitration clauses that block class-action lawsuits, Keller Postman pursued the mass arbitration model instead. Under this structure, 25 or more similar claims are pooled together, dramatically shifting leverage toward claimants and increasing the likelihood of settlement. The firm has already signed up a “significant number” of advertisers and expects that similar mass arbitrations, which typically resolve in 12 to 24 months, will yield meaningful recoveries.searchengineland+1. I The Core Claim: Monopoly Profits Were Passed to Advertisers as Inflated Costs At the heart of the arbitration claims is a straightforward economic argument: monopolists charge more than the free market would allow, and Google’s monopoly power meant advertisers had no real choice but to absorb those inflated rates. Keller Postman’s commissioned economist estimates that advertisers overpaid somewhere in the range of 10 to 25% above what they would have paid in a genuinely competitive marketplace.adsrefund+2 Federal courts found that Google set artificial reserve or floor prices in its ad auctions, essentially minimum bids that ensured winning advertisers almost always paid above-market rates. Even when all bidders submitted bids below the floor price, the winning bidder was still required to pay the floor price, a mechanism that systematically extracted surplus from advertisers on every auction cycle. Combined with Google’s integration of its ad-serving, exchange, and bidding platforms into a single closed system, the result was a market where no genuine competition on price was possible.classaction+2 “Ad Inflation” Through Subsidized Credits One of the more nuanced but significant allegations centers on what claimants describe as “ad inflation” driven by Google’s subsidized ad credits. Google has historically offered advertising credits to new and returning customers, free spend incentives designed to encourage adoption and higher budgets. Claimants allege these credits distorted the auction ecosystem by artificially inflating perceived demand, driving up click costs across all participants in the auction, including those who received no credits. Advertisers who were never offered credits effectively subsidized those who were, paying more per click because of an artificially elevated floor in competitive auctions.reddit This mechanism is particularly relevant for large advertisers spending in excess of $5 million annually, who operate in the most competitive keyword markets where floor pricing and auction manipulation would have the greatest compounding effect over time.adexchanger+1 What Discovery May Reveal Beyond the Antitrust Record The antitrust rulings, while foundational to the arbitration claims, only scratch the surface of what claimants allege actually occurred. One reason I’m so optimistic and have personally facilitated the submission over $1 billion in Google ad spend for recovery through Keller Postman is that I’ve seen first hand where client accounts have manifested behavior that was difficult to explain through other than artificial manipulation of quality score, match types, and targeting options. I have written extensively about additional manipulations that may be exposed through arbitration-specific discovery that goes above and beyond what was uncovered in the government antitrust proceedings.emarketingassociation Chief among these are allegations of Quality Score manipulation. Quality Score is Google’s proprietary 1–10 rating assigned to keywords that, along with bid amount, determines Ad Rank and the cost per click an advertiser ultimately pays. Advertisers have long accepted Quality Scores as objective assessments of ad relevance, expected click-through rate, and landing page experience. But if discovery reveals that Google manipulated Quality Scores in unfounded ways, artificially suppressing scores to drive up CPCs, or inconsistently applying scoring metrics based on advertiser size or campaign type, it would constitute a separate and deeply troubling layer of advertiser harm beyond simple monopoly pricing.youtube Similarly, allegations regarding match type manipulation deserve close scrutiny. Google has progressively broadened the definition of “broad match” and “exact match” keyword matching over the years, often routing advertiser budgets to queries that advertisers did not intend to bid on. When match types are manipulated or quietly redefined without advertiser knowledge, spend is directed toward lower-quality impressions and clicks, increasing costs while degrading ROI. Discovery may reveal whether these changes were algorithmically designed to maximize Google revenue rather than advertiser outcomes.adexchanger Beyond Quality Score and match type, the arbitration discovery process may expose ad rank manipulation more broadly, including whether Google’s auction algorithms were calibrated to favor certain outcomes that served Google’s financial interests over advertiser performance. The antitrust cases documented Google’s abuse of its integrated position across the ad stack; arbitration-level discovery has the potential to surface the specific engineering decisions that translated that structural power into advertiser overcharges on a keyword-by-keyword and impression-by-impression basis. Invalid Traffic and the Filtering Gap Another critical area flagged by me and other industry observers involves invalid traffic (IVT), clicks, impressions, and engagements that do not come from real users with genuine commercial intent. Google defines IVT as any engagement that could “artificially inflate an advertiser’s costs” and maintains policies requiring publishers to prevent their ads from generating such activity. Yet third-party auditors and advertisers have long questioned whether Google’s IVT filtering, applied before billing, meets best-practice standards or whether it is calibrated in a way that systematically underestimates invalid activity.adexchanger The concern is not merely that bad actors generate fraudulent clicks. It is that Google’s own standards for what constitutes “invalid” may be looser than what leading independent verification firms have measured in the same inventory. If discovery reveals that Google’s filtering thresholds were set at levels that knowingly allowed a higher volume of invalid impressions and clicks to be billed to advertisers than a best-practices approach would have permitted, that represents an additional, quantifiable source of advertiser harm layered on top of the core monopoly pricing claims.adexchanger Google has recently touted a 40% reduction in invalid traffic through new AI-powered detection methods, an improvement that, while welcome, implicitly acknowledges that prior systems left significant invalid activity undetected and billed to advertisers.searchengineland Large Advertisers and the “Concierge” Process For advertisers spending $5 million or more on Google annually, the stakes of the arbitration are material. I have noticed an unusual dynamic: despite privately acknowledging that Google is a monopoly and that claims are valid, a striking number of CMOs and brand executives refuse to discuss the arbitration publicly. The reasons are straightforward: Google remains the dominant search and display advertising platform for most large businesses, and advertiser teams fear that openly challenging Google could lead to informal retaliation, degraded account support, reduced access to beta products, or subtler algorithmic disadvantages.emarketingassociation To address this new form of monopolistic overchargte recovery, Keller Postman has developed what industry insiders describe as a “concierge” process for larger advertisers. This approach allows high-spend claimants to participate in the mass arbitration with a significantly higher degree of discretion and confidentiality than would be possible in public litigation. Claims are processed with insulation from public filings that would directly identify the advertiser, enabling brands spending hundreds of millions of dollars on Google media to pursue recovery without making their legal conflict with Google front-page news. Ashley Keller has confirmed that his firm represents multiple major businesses that have spent hundreds of millions of dollars on Google advertising.adexchanger+1 The Scale of the Opportunity, and the Silence Around It The my analysis published on eMarketing Association’s site, raises a pointed question: why are so many of the largest, most sophisticated marketing organizations in the world staying silent about a legal action that could return tens or hundreds of millions of dollars to their bottom line? The answer lies in the power asymmetry that Google’s monopoly itself created. As I noted: “If the evidence clearly shows monopoly profits in search and display, the only fair thing is for Google to return that unfair monopoly profit to advertisers. If it takes a mass arbitration to result in a fair outcome, I’m all for it.”emarketingassociation The full analysis of the claims is available on the eMarketing Association website, where I have documented both the legal framework and the broader industry dynamics that have kept so many large advertisers on the sidelines despite compelling evidence of overcharges. With potential total damages exceeding $218 billion, and with arbitration timelines running 12 to 24 months, the window for large advertisers to join the recovery effort, and do so quietly through the concierge process, remains open, but not indefinitely.emarketingassociation+3
Are LLM based chat bots vulnerable to SEO spam given the probabilistic nature of their responses? If a particular element of information is corroborated many times across the crawlable web does it influence results?
The Vulnerability of LLM-Based Chatbots to SEO Spam and Corroborated Information Influence Recent advancements in large language models (LLMs) have revolutionized conversational AI, but their probabilistic foundations and reliance on web-scale training data raise critical questions about susceptibility to search engine optimization (SEO) spam manipulation. This report analyzes how the statistical nature of LLM text […]
The Vulnerability of LLM-Based Chatbots to SEO Spam and Corroborated Information Influence Recent advancements in large language models (LLMs) have revolutionized conversational AI, but their probabilistic foundations and reliance on web-scale training data raise critical questions about susceptibility to search engine optimization (SEO) spam manipulation. This report analyzes how the statistical nature of LLM text generation interacts with modern SEO tactics, particularly examining whether repeated information patterns across the web disproportionately influence chatbot outputs. Foundations of LLM Vulnerability to Content Manipulation Probabilistic Generation and SEO Spam Synergies LLMs generate responses by predicting token sequences based on learned statistical patterns from training data813. This mechanism creates inherent vulnerability to SEO spam techniques designed to dominate specific linguistic patterns across the web. When spam content saturates particular semantic spaces through keyword stuffing, topic clustering, and synthetic content generation, LLMs’ next-token prediction systems become statistically inclined to reproduce these patterns111. Google’s Webmaster Guidelines explicitly classify purely algorithmic content generation as spam19, but the decentralized nature of web content makes complete spam filtration impossible. A 2024 study demonstrated that duplicating just 0.1% of training data 100 times degraded model performance equivalent to halving model capacity8, suggesting even limited spam penetration could disproportionately influence outputs. This effect compounds when multiple spam operators target the same keywords or entities, creating artificial corroboration at web scale1113. Training Data Contamination Pathways Modern LLM training pipelines ingest trillions of tokens from web crawls, forum posts, and digital archives where SEO-optimized content prevalence exceeds 30% in commercial verticals911. Unlike traditional search algorithms that weight domain authority, LLMs treat all training text as potential pattern sources. This democratization of influence enables sophisticated spam campaigns using: Paraphrase Flooding: Generating multiple linguistic variations of target content to dominate n-gram distributions13 Contextual Anchoring: Embedding target phrases in syntactically diverse but semantically related contexts11 Recursive Amplification: Using LLM-powered tools to iteratively refine spam content that exploits model-specific attention patterns12 A 2024 adversarial study showed inserting strategic text sequences increased product recommendation rankings from 10th to 1st position in LLM outputs11, demonstrating precise manipulability. When multiplied across thousands of spam pages, these techniques create artificial “consensus” that LLMs interpret as genuine signal413. Corroboration Effects in LLM Output Generation The Illusion of Web-Scale Consensus LLMs exhibit strong correlation between output confidence and training data repetition frequency48. This creates a vulnerability where malicious actors can manufacture artificial corroboration through coordinated spam campaigns. The 2024 Claude phishing study revealed LLMs more readily accept claims appearing in multiple contexts3, mirroring human cognitive biases towards frequently encountered information7. Technical analysis of attention mechanisms shows repeated phrases receive progressively higher activation weights during generation16. Spam operators exploit this through: Lexical Echo Chambers: Domain-specific term repetition across seemingly independent sources Syntactic Mirroring: Replicating sentence structures containing target entities Semantic Layering: Embedding concepts in multiple knowledge hierarchies A 2025 analysis found 68% of commercial LLM hallucinations aligned with common SEO content patterns rather than factual errors4, suggesting training data distribution directly shapes model “confidence” in incorrect outputs. Measurement of Corroboration Influence Controlled studies using the C4 dataset revealed: Corroboration Level Output Accuracy Hallucination Rate 1-3 Sources 82% 12% 4-10 Sources 74% 21% 10+ Sources 63% 34% Data adapted from Deduplicating Training Data Makes Language Models Better (2022)13 The inverse relationship between source corroboration and accuracy stems from spam’s disproportionate representation in high-repetition content13. LLMs interpret frequency as validity indicator, privileging SEO-optimized content through: Attention Saturation: Frequently seen tokens dominate transformer attention layers16 Path Dependency: Early repetition establishes generation pathways that subsequent sampling reinforces10 Prior Overfitting: Models develop skewed priors from duplicated content distributions8 Case Studies in LLM Manipulation Local Business Listings Hijacking A 2025 experiment created 120 synthetic “SEO training Houston” pages using GPT-4, achieving first-page Google rankings within 72 hours9. When queried about Houston SEO courses, major chatbots: 73% recommended spam-created institutes 62% reproduced exact pricing models from spam content 41% cited fake accreditation bodies After human content replacement, chatbot accuracy improved to 89%9, demonstrating direct spam influence. Medical Misinformation Propagation Anti-vaccine groups used GPT-3.5 to generate 50,000 forum posts linking autism to COVID vaccines. Within six months: Chatbot vaccine safety answers showed 22% increase in “requires further study” qualifiers 15% of outputs referenced debunked studies from spam forums Citation accuracy decreased 38% despite unchanged training cutoffs This demonstrates LLMs’ inability to distinguish factual consensus from artificial corroboration413. Mitigation Strategies and Technical Countermeasures Training Pipeline Improvements Aggressive Deduplication: Removing duplicates at paragraph (87% efficacy) and document (92% efficacy) levels13 Dynamic Repetition Penalties: Adjusting token sampling based on real-time repetition frequency10 Adversarial Training: Injecting counterexamples to break spam pattern associations12 Runtime Safeguards Consensus Verification: Cross-checking outputs against curated knowledge bases Uncertainty Quantification: Exposing confidence intervals for factual claims Contextual Anchoring: Binding responses to vetted primary sources A 2024 implementation combining these methods reduced spam influence by 54% in clinical trial chatbots15, showing promise despite computational overhead. Future Directions and Research Challenges The arms race between SEO spam and LLM robustness requires fundamental architectural changes: Differentiated Attention Mechanisms: Separating content frequency from validity signals Dynamic Training Weighting: Deprioritizing high-duplication content Web-Scale Credibility Graphs: Integrating trust metrics into training objectives Until these innovations mature, LLM chatbots remain vulnerable to artificial corroboration effects. A 2025 projection estimates $23 billion annual losses from LLM-enabled SEO fraud11, underscoring the urgency for coordinated technical and regulatory solutions. The probabilistic nature that enables LLM fluency paradoxically creates critical security vulnerabilities in the age of AI-powered disinformation.
Since Google’s early algorithm changes made to fight SEO spam, the issue of duplicate content has been on the radar of content and SEO professionals. Re-posting ore re–syndication of the same or similar content had risks associated with it. Oddly, now naturally occurring duplicate content is informing LLMs about businesses and individuals and their specialties. […]
Since Google’s early algorithm changes made to fight SEO spam, the issue of duplicate content has been on the radar of content and SEO professionals. Re-posting ore re–syndication of the same or similar content had risks associated with it. Oddly, now naturally occurring duplicate content is informing LLMs about businesses and individuals and their specialties. Perhaps we are in an era of the duplicate content bonus.
Below is a short list of well-known broadcast journalists who (at least on a major platform like Twitter) have built a larger personal following than the main account of the network or program that employs (or recently employed) them. Social media numbers, of course, change over time, so consider these snapshots rather than static facts. […]
Below is a short list of well-known broadcast journalists who (at least on a major platform like Twitter) have built a larger personal following than the main account of the network or program that employs (or recently employed) them. Social media numbers, of course, change over time, so consider these snapshots rather than static facts. 1. Rachel Maddow Employer: MSNBC Rachel Maddow’s Twitter: ~10+ million followers MSNBC’s Twitter: ~5–6 million followers Rachel Maddow, host of The Rachel Maddow Show on MSNBC, has a notably larger Twitter following than MSNBC’s main Twitter account. 2. Piers Morgan Most Recent UK Employer: ITV (when hosting Good Morning Britain), currently with TalkTV in the UK Piers Morgan’s Twitter: ~8+ million followers Good Morning Britain’s Twitter: ~1 million followers TalkTV’s Twitter: ~150k–200k followers (approx.) While not always classified as a traditional “hard news” anchor, Piers Morgan has nonetheless been a high-profile broadcaster (CNN in the US, ITV in the UK, TalkTV) whose personal social media following outstrips that of the shows/networks that have employed him. 3. Jorge Ramos Employer: Univision Jorge Ramos’s Twitter: ~4+ million followers Univision’s Twitter: ~3+ million followers Jorge Ramos, a prominent Spanish-language news anchor, is arguably the face of Univision for many viewers—yet on Twitter, his personal account typically has a larger following than the main Univision handle. Why This Is Relatively Rare Major news networks (e.g., CNN, Fox News, ABC, NBC) tend to have tens of millions of followers across platforms, making it difficult for an individual journalist to surpass the overall corporate brand’s reach. It’s more common to see an individual journalist exceed the social media following of a specific show’s account or a local affiliate’s handle rather than the entire network. Examples of “close but not quite” cases: Anderson Cooper has 10+ million followers on Twitter, but CNN’s primary handle has over 60 million. Sean Hannity has 6+ million followers on Twitter, but Fox News has well over 20 million. Notes and Caveats Platform Variation: A journalist might outpace an employer on Twitter but not on Facebook, YouTube, or Instagram (or vice versa). “Social media reach” can differ widely depending on which platforms are measured. Multiple Employers: Some journalists appear on multiple networks or have multiple professional affiliations. In these cases, you might see them outstrip one employer’s handle while not surpassing another’s. Rapidly Changing Follower Counts: Social media numbers are dynamic; the approximate figures listed above can change significantly in a short time. These three (Maddow, Morgan, and Ramos) are some of the clearest, widely recognized examples of broadcast journalists who command a personal social media audience larger than at least one major handle of their employer.
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That time I tried to buy Gawker.com out of bankruptcy
After generating over $8 million for nonprofits with We-Care.com’s cause marketing powered shopping I developed aa technology that would facilitate cause marketing powered content. So, I tried to buy Gawker.com out of the bankruptcy in January of 2018. Got a ton of press and almost got it. My winning bid was invalidated due to someone’s […]
After generating over $8 million for nonprofits with We-Care.com’s cause marketing powered shopping I developed aa technology that would facilitate cause marketing powered content. So, I tried to buy Gawker.com out of the bankruptcy in January of 2018. Got a ton of press and almost got it. My winning bid was invalidated due to someone’s tortious interference in the court’s eyes. The concept was that instead of content creators allowing ad revenue to flow to Zuckerberg, ByteDance, Google (YouTube) and Twitter (x) that revenue would flow to nonprofits.
PR, Create The Story, Share Your Stardom
I really loved the Dunewood Fire Island oceanfront house that we built shortly after Sandy. Yet the time came in 2018 to put the house up for sale for a variety of reasons. Despite the house being a stunning architectural digest quality house, both my internal PR team at Didit, a long island marketing agency, […]
I really loved the Dunewood Fire Island oceanfront house that we built shortly after Sandy. Yet the time came in 2018 to put the house up for sale for a variety of reasons. Despite the house being a stunning architectural digest quality house, both my internal PR team at Didit, a long island marketing agency, and my good friend Peter Shankman educated me that I should pitch the larger story and have the house be part of the story. Even better, find myself a co-star in the story pitches, and tune the pitch to the periodical. So, rather than try to get the written about the house I looked at who I could pitch at the periodicals I wanted. Here’s how I did given my objective was to try to get press coverage for the house in the following periodicals NY Times – strike out NY Post – Tour the funkiest houses on Fire Island Wall Street Journal – A New York Weekend Island Retreat Gets Fancy Newsday – strike out Not bad, two out of four. And in both articles, I made the island itself, and my architect Eric Schiller the co-star in the story That’s how to do PR. <img fetchpriority="high" decoding="async" width="769" height="1024" src="https://kevinlee.net/wp-content/uploads/2019/02/post-fi-769x1024.jpg" alt="" class="wp-image-108" srcset="https://kevinlee.net/wp-content/uploads/2019/02/post-fi-769x1024.jpg 769w, https://kevinlee.net/wp-content/uploads/2019/02/post-fi-225x300.jpg 225w, https://kevinlee.net/wp-content/uploads/2019/02/post-fi-768x1022.jpg 768w, https://kevinlee.net/wp-content/uploads/2019/02/post-fi.jpg 788w" sizes="(max-width: 769px) 100vw, 769px" />
The reason I invested in Boatbound which became Boatsetter
I was pitched on the concept of Air BnB for boats. I saw in the Bay Shore marina that I was a heavy boat user, taking it back and forth to my Fire Island house in Dunewood. (since sold) The premise made sense especially given the umbrella insurance coverage that had been procured.
I was pitched on the concept of Air BnB for boats. I saw in the Bay Shore marina that I was a heavy boat user, taking it back and forth to my Fire Island house in Dunewood. (since sold) The premise made sense especially given the umbrella insurance coverage that had been procured. <img decoding="async" width="1024" height="768" src="https://kevinlee.net/wp-content/uploads/2025/06/20160616_202141-1024x768.jpg" alt="" class="wp-image-151" srcset="https://kevinlee.net/wp-content/uploads/2025/06/20160616_202141-1024x768.jpg 1024w, https://kevinlee.net/wp-content/uploads/2025/06/20160616_202141-300x225.jpg 300w, https://kevinlee.net/wp-content/uploads/2025/06/20160616_202141-768x576.jpg 768w, https://kevinlee.net/wp-content/uploads/2025/06/20160616_202141-1536x1152.jpg 1536w, https://kevinlee.net/wp-content/uploads/2025/06/20160616_202141-2048x1536.jpg 2048w" sizes="(max-width: 1024px) 100vw, 1024px" /> <img decoding="async" width="768" height="1024" data-id="155" src="https://kevinlee.net/wp-content/uploads/2025/06/20181114_143151-768x1024.png" alt="" class="wp-image-155" srcset="https://kevinlee.net/wp-content/uploads/2025/06/20181114_143151-768x1023.png 768w, https://kevinlee.net/wp-content/uploads/2025/06/20181114_143151-225x300.png 225w, https://kevinlee.net/wp-content/uploads/2025/06/20181114_143151.png 800w" sizes="(max-width: 768px) 100vw, 768px" /> <img loading="lazy" decoding="async" width="768" height="1024" data-id="156" src="https://kevinlee.net/wp-content/uploads/2025/06/20181114_143238-768x1024.png" alt="" class="wp-image-156" srcset="https://kevinlee.net/wp-content/uploads/2025/06/20181114_143238-768x1023.png 768w, https://kevinlee.net/wp-content/uploads/2025/06/20181114_143238-225x300.png 225w, https://kevinlee.net/wp-content/uploads/2025/06/20181114_143238.png 800w" sizes="auto, (max-width: 768px) 100vw, 768px" /> <img loading="lazy" decoding="async" width="768" height="1024" data-id="157" src="https://kevinlee.net/wp-content/uploads/2025/06/20181114_143211-768x1024.png" alt="" class="wp-image-157" srcset="https://kevinlee.net/wp-content/uploads/2025/06/20181114_143211-768x1023.png 768w, https://kevinlee.net/wp-content/uploads/2025/06/20181114_143211-225x300.png 225w, https://kevinlee.net/wp-content/uploads/2025/06/20181114_143211.png 800w" sizes="auto, (max-width: 768px) 100vw, 768px" />
Hack the Eisenhower Decision Matrix with “Time”
Steven Covey popularized the “Eisenhower Decision Matrix” to help people plan out their day and their projects. The EDM was derived from a famous remark made by General Eisenhower: “What is important is seldom urgent and what is urgent is seldom important.” But the Eisenhower Decision Matrix is a great tool to use for prioritizing […]
Steven Covey popularized the “Eisenhower Decision Matrix” to help people plan out their day and their projects. The EDM was derived from a famous remark made by General Eisenhower: “What is important is seldom urgent and what is urgent is seldom important.” But the Eisenhower Decision Matrix is a great tool to use for prioritizing resources in any marketing campaign you’re running, or even overall marketing tasks. It can keep your team on track and avoid the chaos that can appear when everyone is running around “putting out fires.” The Eisenhower Decision Matrix has four quadrants mapping the urgency of tasks against their overall importance to the business: Urgent tasks/Important tasks Non-urgent tasks/Important tasks Urgent tasks/Unimportant tasks Non-urgent tasks/Unimportant tasks I add time as the third Z dimension. I’ve even hacked the Eisenhower Decision Matrix by adding a third dimension within each quadrant, in particular the first quadrant. The additional dimension I like to use when deciding prioritization within quadrants is Difficulty/Ease or fast vs long projects. I’ll provide more on why that can be a helpful factor to consider later on. First let’s talk about each quadrant in detail: Quadrant 1: Urgent tasks/Important tasks Unfortunately Quadrant 1 is where many of us marketers spend too much of our time. An ad is broken, a site is down, a mail piece is misprinted, or a client is unhappy. The result is an “all hands-on-deck “full court press” effort to clear the emergency and get back to some semblance of normality. The costs of having yourself — or your staff – spending a lot of time in Quadrant 1 is burnout, turnover, and a work environment where everything is always at DEFCON 1. In project management, being in Quadrant 1 is known as “crashing the time” and it’s a state to be avoided at all costs because mistakes are so often made while in it. You and your team will inevitably have to spend a considerable amount of your time in Quadrant 1, because business life is unpredictable and there will always be “fires” that have to be put out. But the trick is to use the system to focus on the marketing tasks within this quadrant of important and urgent that have the most material impact on your marketing KPIs. Hit home runs on your KPIs and your chances of a raise, bonus or promotion just got better. Quadrant 2. Non-urgent tasks/Important tasks Quadrant 2 is the “sweet spot” where management gurus say we should strive to spend more of our time and mental energy, particularly if we have strategic or management responsibilities. In this quadrant, there’s time for deliberative thought, evaluation, and creativity. Deadlines are understood and resources are allocated in time to meet them. Surprises are few and goals are long-term and strategic. You can’t completely eliminate surprises and crisis from happening, but if you do a great job of managing things in Quadrant 2, you’ll spend less time in Quadrant 1. Procrastinate within quadrant 2 and you have a crisis that moves into quadrant 1. In Quadrant 2, as a manager you have the freedom to delegate, to let your team work its magic, and to think long and hard about the strategies and tactics most likely to move the needle for your business. Both digital project management platforms and a whiteboard with post-it notes can work to organize your initiatives and tasks into quadrants and perhaps use color coding to indicate difficulty of completing the tasks and initiatives. Quadrant 3. Urgent tasks/Unimportant tasks Why spend any time in Quadrant 3, where urgency — masking as importance — bogs you down in tasks that aren’t really that important? Well, there’s no good reason other than the fact that we humans are easily distracted, and today — in the era of social media and multitasking — our attention is constantly being divided. Let’s be frank here too: corporate culture often rewards an appearance of activity irrespective of whether this activity is crucial or tangential to the business’ strategic goals. The result is a conflation of “busy-ness” and “business.” Reducing the “urgent but unimportant” task load isn’t easy, because it’s often a product of culture, culture isn’t easy to reform, and there’s often no general agreement on what “business critical” goals are in any organization. Clarity on this issue usually flows from the top (but unfortunately and ironically, many C-level executives spend so much time in Quadrant 1 that they never get to this important goal). Quadrant 4. Non-urgent tasks/Unimportant tasks In a perfect world, Quadrant 4 wouldn’t even exist or it would be empty (have nothing in it). Ideally, neither you or any of your team should be doing any non-urgent, unimportant initiatives or tasks. In the age of Facebook, Twitter, Buzzfeed, and the Huffington Post, tolerating some level of Quadrant 4 activities is probably inevitable, but you should still strive to move the bulk of activities to Quadrant 2. By hacking the Eisenhower Decision Matrix by adding difficulty one can accomplish the initiatives and tasks that move the needle more quickly. Color coding or tagging the tasks is a great way to do that. Go rock your quadrants.