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You’ve worked together before. You trust each other. You know how the other person thinks under pressure. On paper, it’s the safest move. In many ways, it is. Shared history creates speedfaster decisions, candid conversations, less time decoding intent. When CEOs bring former colleagues into senior roles, baseline trust feels like rocket fuel. But familiarity also introduces a hidden risk that undermines executive teams far more often than leaders anticipate. What I see repeatedly in executive teams built on shared history is the quiet formation of inner circles. Leaders who go way back share shorthand, context, and trust earned elsewhere. Others, often equally capable with deep institutional knowledge, find themselves outside that orbit. I coached a CEO whod brought three former colleagues into a 10-person executive team. Within months, critical decisions were being pre-discussed among “The Four” before formal meetings. The other six leaders became increasingly passive, not because they lacked capability, but because challenging pre-baked decisions felt politically risky. The damage isnt caused by intent. Its caused by relationships that were never recalibrated for a new reality, and by new relationships that were never deliberately cultivated. The most effective executive teams consistently apply these four practices to prevent individual excellence from turning into organizational friction. 1. Connection High-performing teams invest time getting to know one another, all members, not just familiar faces, before diving into business results. Career paths, pivotal decisions, what energizes and frustrates them. This doesnt take much time. A small investment upfront pays dividends for months. When facilitating executive off-sites, I often begin with a simple my journey exercise using images rather than words to reflect career highs, lows, decision points, and at least one non-work passion. The impact is often immediate. Leaders whove worked together for decades consistently say they learn something new about their colleagues, newly hired executives feel less like outsiders from day one, and everyone gains a clearer sense of how to tap into the talent around the table. 2. Clarify contributions Each executive understands not only what they own but also how their contribution creates value for the enterprise. Where do I lead? Where do I support others? Where might my strengths unintentionally create drag? Without this clarity, leaders default to optimizing their own function. Silos arent a cultural failure; theyre the natural outcome of unexamined individual priorities. 3. Intentional relationship recalibration For leaders with shared history, relationships must be explicitly reset for the new context. What worked at the last company doesn’t automatically apply here. Assumptions from past roles, how we made decisions, how we disagreed, who deferred to whom, need to be examined, not inherited. Strong teams explicitly revisit: How decisions are made now (not how they used to be) How disagreement is expected to show up in this company What information must travel across functions, not just within them How tension and conflict will be surfaced early within this team This isn’t about questioning trust; it’s about updating the operating system. The CFO who was your peer at the last company now reports to you. The CMO you brought in was brilliant at a scrappy startup, but this is a public company with regulatory constraints. Same people, different game, different rules. Without recalibration, old patterns quietly reassert themselves, even when they no longer serve the business, or the team. 4. Accountability over loyalty Loyalty protects people. Accountability protects performance. In cohesive executive teams, leaders dont avoid difficult conversations or cover for one another in the name of trust. They hold peers to shared standards, especially when its uncomfortable. Many capable teams stall here. Loyalty gets mistaken for cohesion when, in reality, unchecked loyalty is what allows silos and turf wars to persist. What great executive teams look like in practice In the strongest executive teams, something subtle but powerful happens in meetings. Side conversations are surfaced in the room. Misalignment is named before decisions are finalized. When one voice dominates, others step in, not to challenge authority, but to protect cohesion. Decision rights are referenced rather than assumed. The CEO doesnt act as referee. The team self-corrects. That’s the signal of a deliberately designed team. And it’s why these teams execute faster in a crisis. Trust wasn’t inherited from the past; it was engineered for the present. The CEO who’d created “The Four”? Once we surfaced the pattern and reset expectations, decision quality improved, and the full team reengaged. But it required deliberate intervention, not hope. The CEOs real work If youre building a leadership team from people you already know, your job isnt to rely on trust, its to reengineer it. Think of it this way: you wouldn’t move into a new office and keep the same floor plan from your last building. Why would you import relationship patterns from a different company, different roles, different stakes? The best leadership teams dont suppress individuality. They harness it through intentional relationship design. Shared history may get you started. Only deliberate cohesion sustains performance.
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E-Commerce
The meme coin boom has made some Web3 bros incredibly rich. But a new study published on Cornell Universitys arXiv suggests the ecosystem is better understood as a place of extreme churn, flimsy infrastructure, and a surprising number of scammy projects that disappear quickly. Researchers Alberto Maria Mongardini at the Technical University of Denmark and Alessandro Mei at the Sapienza University of Rome built MemeChain, an open-source, cross-chain dataset of 34,988 meme coins across Ethereum, BNB Smart Chain (BSC), Solana, and Base. The system combines on-chain records with off-chain legitimacy signals such as token logos, social links, and archived website HTML. MemeChain found that through mid-January 2025, 1,801 tokens, or around 5% of all the coins tracked, stopped trading within 24 hours of launch. Nearly half showed zero transaction activity from mid-October to mid-December 2024, suggesting many projects burn out within weeks. Around 10% of meme coins on the BNB Smart Chain lasted only a single day, compared with roughly 0.1% on Solana. Mongardini said the team began by watching this wave of new created meme coins across chains in 2024 and asking who was trying to exploit the hype and FOMO. While he expected some level of alleged impropriety, he was surprised by the scale of scammy rug pulls and short-lived meme coins. Its shocking to me, he said. Some indicators helped signal whether a coin was likely to rug pull. While 74.8% of tokens claimed an associated website, only 32.1% of those sites returned a working HTTP 200 response when tested. The researchers also found widespread use of cheap registrars and short-lived hosting, which Mongardini described as very, very fragile infrastructure built with very, very low effort because creators want to capitalize as soon as possible. The aim of developing tools that can warn investors about high-risk trades is to make a safer environment for people that [use] the crypto markets, Mei said, though he added that doing so in real time is very expensive. Buyer beware.
Category:
E-Commerce
The distance between a world-changing innovation and its funding often comes down to four minutesthe average time a human reviewer tends to spend on an initial grant application. In those four minutes, reviewers must assess alignment, eligibility, innovation potential, and team capacity, all while maintaining consistency across thousands of applications. It’s an impossible ask that leads to an impossible choice: either slow down and review fewer ideas or speed up and risk missing transformative ones. At MIT Solve, we’ve spent a year exploring a third option: teaching AI to handle the repetitive parts of review so humans can invest real time where judgment matters most. WHY AI, AND WHY NOW In 2025, Solve received nearly 3,000 applications to our Global Challenges. Even a cursory four-minute review per application would add up to 25 full working days. Like many mission-driven organizations, we dont want to trade rigor for speed. We want both. That led us to a core question many funders are now asking: How can AI help us evaluate more opportunities, more fairly and more efficiently, without compromising judgment or values? To answer this question, we partnered with researchers from Harvard Business School, the University of Washington, and ESSEC Business School to study how AI could support early-stage grant review, one of the most time-intensive and high-volume stages of the funding lifecycle. WHAT WE TESTED AND WHAT WE LEARNED The research team developed an AI system (based on GPT-4o mini) to support application screening and tested it across reviewers with varying levels of experience. The goal was to understand where AI adds value and where it doesnt. Three insights stood out: 1. AI performs best on objective criteria. The system reliably assessed baseline eligibility and alignment with funding priorities, identifying whether applications met requirements or fit clearly defined geographic or programmatic focus areas. 2. AI is more helpful to less experienced reviewers. Less experienced reviewers made more consistent decisions when supported by AI insights, while experienced reviewers used AI selectively as a secondary input. 3. The biggest gain was standardization at scale. AI made judgments more consistent across reviewers, regardless of their experience, creating a stronger foundation for the second level of review and human decision-making. HOW THIS TRANSLATES INTO REAL-WORLD IMPACT At Solve, the first stage of our review process focuses on filtering out incomplete, ineligible, or weak-fit applications, freeing human reviewers to spend more time on the most promising ideas. We designed our AI tool with humans firmly in the loop, focused on the repetitive, pattern-based nature of initial screening that makes it uniquely suited for AI augmentation. The tool: Screens out applications with no realistic path forward. Supports reviewers with a passing probability score, a clear recommendation (Pass, Fail, or Review), and a transparent explanation. When the 2025 application cycle closed with 2,901 submissions, the system categorized them as follows: 43% Pass; 16% Fail; and 41% Review. That meant our team could focus deeply on just 41% of the applicationscutting total screening time down to ten dayswhile maintaining confidence in the quality of the results. THE BIGGER TAKEAWAY FOR PHILANTHROPY Every hour saved during the early stages of evaluation is an hour redirected toward the higher-value work that humans excel at: engaging more deeply with innovators and getting bold, under-resourced ideas one step closer to funding. Our early results show strong alignment between AI-supported screening and human judgment. More importantly, they demonstrate that its possible to design AI systems that respect nuance, preserve accountability, and scale decision-making responsibly. The philanthropic sector processes millions of applications annually, with acceptance rates often below 5%. If we’re going to reject 95% of ideas, we owe applicantsespecially those historically excluded from fundinga genuine review. Dividing responsibility, with humans making decisions and AI eliminating rote review, makes it that much more possible at scale. It’s a practical step toward the thoroughness our missions demand. Hala Hanna is the executive director and Pooja Wagh is the director of operations and impact at MIT Solve.
Category:
E-Commerce
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