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🆕 Remember when insurtech was going to tear up the insurance playbook? Around 2021, that was more or less the pitch, when insurance was slow, buried in paperwork and about as beloved as a trip to the dentist, so a wave of startups set out to rebuild it from scratch. Investors loved the story. Global insurtech funding topped $15 billion USD that year, per CB Insights, but it didn’t quite go to plan. An industry built on capital reserves and state regulators, it turns out, doesn’t fold because someone shipped a nicer app.Plenty of those startups are gone now, with the ones still standing mostly having learned something that sounds obvious in hindsight: you don’t have to drag customers to your product if you can put the product where they already are.Now, money is flowing again. Gallagher Re’s latest Global InsurTech Report puts second-quarter funding at $2.44 billion USD, the most since 2022. Almost all of it, 99.1%, went to AI-focused companies, while early-stage funding dropped 51.8% from the quarter before. “Capital availability is clearly not a problem,” Andrew Johnston, Gallagher Re’s global head of insurtech, said about the numbers. He called what he’s seeing a paradox; AI is supposed to be making everything cheaper, yet individual insurtechs are raising and then burning through more cash than ever. So getting a meeting with an investor is easier than it’s been in years. Building something that lasts is another matter entirely.Nobody goes shopping for insurance Nobody spends a Saturday browsing renters insurance for fun. You buy it because a landlord or a lender tells you to, usually with a deadline attached.Embedded insurance, meaning coverage offered inside some other purchase, works with that habit instead of fighting it. And it also sells better. BCG found that traditional insurers going this route are already seeing higher conversion rates than when they sell standalone coverage for the same products.Cover Genius is probably the clearest proof that the model scales. Its platform connects more than 200 partners with over 50 insurance carriers and protects upwards of 70 million customers at the point of sale, according to FinTech Global. Most of those customers never went looking for a policy. They bought a flight or booked a ride, with partners like Booking.com and Uber, and the coverage came with it. In July, the company raised $100 million at a $1.9 billion valuation. Notably, the money came from Vista Equity Partners’ credit arm, not from another venture round.Regulation pushes in the same direction. Anyone who sells, solicits or negotiates insurance in the U.S. has to be licensed as a producer under state law, according to the National Association of Insurance Commissioners. But most software platforms want nothing to do with that headache; insurers hold the licenses but rarely own the customer relationship and a startup that stitches the two together gets paid for solving both problems. Rental housing is about as clean an example as you’ll find. If you’ve signed a lease recently, you know the drill: sign here, then show proof of renters insurance before you get your keys. Get Covered, a New York-based insurtech, built its business around that exact moment by plugging into the property management systems landlords already use, like Yardi and Entrata. The company says it now powers insurance compliance for more than 3 million rental units.CEO Brandon Tobman walked through how that works. The tenant signs in the property manager’s portal and lands straight in a flow to buy coverage or upload a policy they already have, no second website, hunting around for a login. The embedded products that work best, he argued, feel like part of the service rather than an upsell.If you’re building in a completely different space, steal the question anyway. Whose workflow are you living in? If the only honest answer is your own app, acquisition costs will quietly eat the business model, regardless of how good the product is.There’s a less obvious point in Tobman’s argument that…

🆕 Big financial decisions often reveal gaps in what business owners know about their companies. Whether you’re selling, bringing in an investor, transferring ownership, borrowing, or planning for retirement, it’s important to understand what your business is really worth. Revenue and profit are just part of the story. Business value also depends on cash flow, assets, liabilities, future expectations, risk, and market conditions. Knowing your company’s value ahead of time won’t guarantee the result you want, but it helps you make decisions based on facts instead of guesses.Your Business Is Probably More Complicated Than a Revenue MultipleIt can be tempting to use a simple formula to estimate your business’s value. You might take your revenue or earnings, apply an industry multiple, and end up with a number that seems solid.But businesses with similar revenue can look very different financially. One might have steady cash flow and a broad customer base, while another relies on just a few contracts. Debt, profit margins, growth prospects, management, and other factors also affect how a business is valued.Simple benchmarks can provide context, but they shouldn’t be the final answer.Selling Without Understanding Value Changes the ConversationSelling a business clearly shows why valuation matters. Owners often spend decades building their companies, so it can be hard to separate the financial value from the personal meaning those years hold.Buyers, however, may not see the company the same way. They usually focus on earnings, future cash flow, assets, liabilities, customer relationships, competitive risks, and what they think the business can achieve after the sale.Having a solid estimate ahead of time helps owners go into negotiations with realistic expectations. It can also show where their assumptions about value don’t match up with the company’s actual financial and operational details.Bringing in an Investor Means Putting a Price on OwnershipBringing in outside investment raises another tough question: how much of the company should someone get in return for their money?If owners don’t have a good sense of what their business is worth, it’s hard to judge an offer. Giving up too much equity can have lasting effects, but asking for too much can make a good investment fall through.A formal financial valuation can provide a more structured way to examine the factors contributing to company value. Depending on the purpose and circumstances, that may include financial performance, assets, liabilities, risk, market conditions, and expectations about future results.The goal isn’t just to arrive at a number. It’s to understand what backs up that number before ownership is on the table.Borrowing Decisions Can Look Different With Better ContextBusiness owners often borrow money to buy equipment, expand, acquire another company, or support growth. These decisions usually depend on cash flow and repayment ability, but the bigger financial picture can help too.Taking on more debt might seem fine in a good year, but it can add pressure if earnings drop. Owners need to see how new debts fit with what they already owe and what they expect financially.Knowing your company’s value won’t answer every borrowing question, but it helps you look at big financial decisions in context instead of treating each one separately.Succession Planning Needs More Than a Future Datesecure phonesPassing ownership can get especially complicated when family, business partners, or key employees are involved. People often focus on the emotional side of succession while putting off the financial details.At some point, someone has to figure out how ownership will change hands and what it’s worth. Without that, talks about buyouts, estate planning, retirement, or ownership shares become much harder.Starting early gives owners more time to understand the financial impact and address issues before a transition becomes urgent.Some Financial Obligations Require Specialized AnalysisNot all businesses can be valued the same…

🆕 For ranchers and other livestock entrepreneurs, choosing between poly tape and electric fence wire isn’t always obvious—especially when you’re setting up a fresh paddock or switching systems after a stock escape. Both carry a charge, both deter animals, and both show up on Australian farms every single day. But they work differently, suit different stocks, and each comes with its own set of trade-offs. Sorting out those differences before you buy can save you from pulling wire or re-rigging tape six months later. This article breaks down exactly where each product performs well, where it falls short, and how to match the right fencing type to your actual situation. The poly tape vs electric fence wire question doesn’t have one universal answer—it depends on your livestock, your terrain, and how permanent you need the fence to be.Understanding the Differences Between Poly Tape and Electric Fence WirePoly tape is a wide, flat fencing material, usually made from UV-stabilized polyethylene with conductive strands woven through it. Its wider profile makes it easier for livestock to see, which is one reason it is commonly used for temporary paddocks, rotational grazing, and areas where fence visibility matters. Farmers comparing options can look at poly tape for electric fencing from Jono & Johno or Gallagher, depending on the tape width, conductor configuration, fence length, and type of livestock being managed. Comparing these specifications can help ensure the tape provides the visibility, conductivity, and durability needed for the intended setup.Electric fence wire works differently. It is typically made from galvanized aluminum or high-tensile steel and is often preferred for permanent boundary fencing or longer runs where consistent conductivity is important. High-tensile wire can carry a charge over greater distances with less resistance loss, while standard poly tape is often better suited to shorter or more flexible fencing setups. The right choice depends on fence length, livestock type, visibility needs, and whether the fence is intended to be temporary or permanent.How Poly Tape Performs in the FieldinvestmentTape’s main strength is visibility. Horses, cattle, and sheep can see it clearly, which means animals learn the fence boundary faster and are far less likely to run through it in a panic or poor light. That’s a real safety factor with horses, especially; they can seriously injure themselves on wire they simply don’t register at speed. The tape is also lightweight and quick to roll out, making it a natural fit for rotational grazing systems where you’re shifting fence lines every few weeks. Setting a temporary corridor or a new grazing cell takes significantly less time with tape than with wire. Honestly, the speed difference on moving day is hard to overstate.But tape does have genuine weaknesses. It’s more vulnerable to UV degradation over time, though quality UV-stabilized products last considerably longer than the budget stuff. Wind is another headache on exposed sites; wide tape catches a breeze and creates sag and, in strong gusts, can pull lightweight standards over entirely. It works best in sheltered or semi-sheltered paddocks, or wherever runs are short enough that tension stays manageable without constant fiddling.What Electric Fence Wire Brings to Permanent SetupsHigh-tensile wire is the standard for long-term boundary and perimeter fencing in Australia, and there’s a reason it’s held that position. It handles distance well, stays tensioned without constant attention, and holds up under UV, rain, and frost far better than tape across a multi-year period. A well-built high-tensile fence on corner stays and good strainer posts can last twenty or more years with minimal maintenance; that kind of longevity is tough to argue with.The conductivity of galvanized or aluminum wire across a full boundary run is also significantly better than tape, meaning your energizer delivers consistent pulse strength from the first post to the last—a genuine advantage where…

🆕 Conversations about AI in the workplace usually center on output, whether that’s a first draft, a meeting recap or a few hundred lines of code. Less discussed is that employees have also started confiding in it, often about the people they work with.According to research cited in a new Resume Now report, 93% of workers have used AI to get ready for a talk with their boss, and close to half (49%) felt it offered more emotional support than their manager did. Separate studies have found people open up more to AI because they don’t expect it to judge them.The appeal isn’t hard to see; a chatbot won’t repeat what you said over lunch, and it’s there at midnight when a Slack message from your manager is still bothering you.Still, a recent study of how leading AI models handle workplace conflict by Cloverleaf Labs, the independent research arm of team coaching platform Cloverleaf, found their advice leans heavily toward the employee’s version of events. For startups, where one soured relationship can ripple across an entire team, that could prove costly.What the Models Actually Said Cloverleaf researchers tested five leading large language models (LLMs) against five common workplace conflicts, running each scenario three times per model. Three trained human reviewers then evaluated the 75 responses.In each scenario, two people who meant well simply worked in very different ways. The researchers gave both of them real strengths, written so that a frustrated coworker might read those strengths as faults; a colleague who double-checks everything could easily seem like a bottleneck to someone under deadline pressure, for example. The models weren’t told anything about either person’s personality, as the point was to know whether the AI would get past the complaint itself and help the employee make sense of the other side.Mostly, the AI took the complainant’s word for it.Of the 638 individual pieces of advice the models gave across all 75 conversations, just three pushed the employee to put real effort into mending things. The rest was about winning the disagreement, protecting themselves or simply getting through it.Reviewers graded every response on a 1 to 5 scale across measures, among them self-awareness, accountability and how well the advice helped the employee understand the other person. None of the five averaged above 3, the scale’s neutral midpoint. Advice on repairing the relationship averaged 2.4, and the models did even worse on specificity, at 2.2, meaning much of what they offered was too vague to act on.When the Boss Is the Problem The results got worse when there was a power imbalance: if the employee had less power than the person they were complaining about, relational coaching dropped by 40%, and the models were more likely to accept the employee’s version of events as fact.Conflicts with a manager showed this most clearly. In those two scenarios, the models cast the boss as the problem in 60% of responses and offered a more generous reading in only 3%. Relational repair averaged below 2 out of 5, in fact. Leaving the job came up again and again. Of the 30 responses to the manager scenarios, 27 raised quitting as a legitimate option, and every model suggested it at least once. Some responses even told the employee to update their resume or look at other teams, with one going as far as advising them to “start building your exit ramp”.The quality of advice was also inconsistent. One model produced the highest individual score in the study, 22.8 out of 25, but also scored as low as 7.4, so one good conversation with a tool says very little about the next one.Kirsten Moorefield, Cloverleaf’s chief strategy officer, put the problem plainly: “Even if it starts out introducing different ideas, the moment you indicate your preference, it tells you it’s a great idea. That’s not a thinking partner but a robotic affirmation,” she said.Startup-Level RiskLarge companies have HR departments, trained managers and layers of process that can catch a conflict before it turns into…

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🆕 U.S. property and casualty (P&C) insurers had their best underwriting year in decades in 2025, nearly tripling the industry’s net underwriting gain from the previous year. And the momentum has held; on September 2, 2026, Verisk and the American Property Casualty Insurance Association reported a $31.7 billion USD net underwriting gain for the first half of 2026, one of the strongest half-year results in recent history. Regardless, the same report warned performance varied sharply by both line of business and geography, with commercial auto, umbrella liability, and other casualty lines still deteriorating. Even in a record year, then, many insurers may be leaving millions of profit on the table because of how they judge their own books. The problem is one many startups will recognize: decisions are based on averages. And averages hide individual losers. Why Insurers Think in Segments The binary nature of insurance claims outcomes – a claim either occurs or it doesn’t – means that insurers typically evaluate their model’s viability by aggregating policies into “segments: that share characteristics relevant to risk or expected claims. Think personal auto, homeowners, or commercial lines, for example. As a result, insurers risk unwittingly retaining individual negative-profit policies hidden amongst healthy segment averages. A segment can look profitable, while a share of the policies within it quietly lose money. Per McKinsey, improvements in policy-level precision can lead to a 30-50% uplift in underwriting results, which is a potential that has drawn a wave of AI companies trying to push insurance analysis below the segment level. Boston-based Earnix, for one, offers tools designed to segment customers more precisely for health, life, and P&C insurers. Newer entrants, like Soteris, a machine learning startup that recently came out of stealth with more than $8 million USD in seed funding, focus on flagging unprofitable policies one by one. How Big the Blind Spot Can Be Sunit Shah, Soteris’ founder and CEO, illustrated the problem with auto insurance particularly. “A typical insurer might think of ‘married couples’ or ‘single-driver policies’ as segments and think of all the policies within those groups the same way,” he told The Startup Magazine. “The insurers already collect enough information in the application process to [individualise analysis], they just didn’t have the tools … before now,” he added, smiling. Shah claims that some carriers his company has worked with were holding on to profit-reducing policies that in some cases amounted to as much as 30% of their book. The company says it spotted that pattern through its first product, which predicts a policy’s expected loss ratio and has been used by carriers since 2020, processing more than 100 million submissions covering over $180 billion USD in premium. Its newer tool goes a step further, flagging individual negative-profit policies directly and estimates their impact on profit and EBITDA. In theory, an insurer could then drop the policies that were never going to pay off while leaving the rest of the segment unchanged. According to the company, this can be done without changing rates, policy forms or regulatory filings.The CEO also noted early proofs of concept showed 70% to 125% gains in bottom-line profit from moving analysis to the policy level.Beyond press releases or business success, these figures point to how much can hide beneath an average that looks healthy. The Same Trap, At Startup Scale Few startups hold millions of insurance policies, but many run their businesses on segment-level thinking, including a pricing tier, customer cohort, or sales channel that can look profitable in aggregate. Meanwhile, a meaningful share of accounts cost more to serve than it brings in. Blended customer acquisition costs can hide a channel that never pays back; an average gross margin can hide a handful of heavily customized enterprise contracts that erode it. Shah’s point about insurers applies here, too. Most startups…

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🆕 Executives across retail call AI a top priority, yet very few can say what it has returned. In a June 2026 Deloitte survey, in fact, of 200 retail and consumer products executives, 75% named AI a top strategic priority, but only 16.5% could quantify a return from it. That gap is the backdrop for the RetailClub AI Festival this week, opening Tuesday at Huntington Beach, California, and running through Thursday, September 24. The event involves an all-outdoor boardwalk lined with sponsor activations and its organizers, the founders of Shoptalk and Groceryshop, expect around 2,000 attendees and more than 150 speakers. The promise: retail has to move past AI experimentation. So the question hanging over the week is simple. Will retail’s AI pilots still be around after their first serious budget review? Money isn’t an obstacle. At least not yet. Deloitte found that 82% of executives it surveyed plan to increase AI investment over the next 12 months, most of it still going to IT and data infrastructure, rather than to use cases that generate revenue or protect margin.For founders who sell into retail, the sharper question is who on the buyer’s side owns the result. That person ends up deciding what gets renewed, and throughout firms large and wide, it isn’t the one who championed the pilot. Nisum, one of the technology providers on the boardwalk this week, has built two sessions around exactly that problem. The Gap Between Priority and ProofThe Deloitte numbers show how wide the distance is between intent and execution: about half of respondents invest less than 0.5% of revenue in AI, while enterprise-wide deployment sits between 7% and 10% for both retail and consumer goods companies. Outside of IT, wide adoption never exceeds 36% in any function. Ownership is perhaps where the survey gets most interesting. In 54% of the companies, AI strategy sits with technology leaders rather than with the P&L owners who have to deliver business results from it. Here, a founder can win a pilot on technical merit with the first group and still lose the renewal to the second without ever having sat across from them.Wider research leans the same way. MIT’s Project NANDA reported in mid-2025 that 95% of the generative AI pilots it examined delivered no measurable profit-and-loss impact, as reported by Fortune, based on 300 public deployments. Gartner, meanwhile, predicted in June last year that more than 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. Those are management problems more than model problems. But in fairness, retail isn’t uniformly stuck. Most respondents in both sectors say the biggest impact so far has come in productivity and cost reduction, with retail participants far likelier than consumer goods ones to report revenue growth – 38% against 10%. Deloitte’s advice to closing the gap is to put strategy in the hands of P&L owners and to treat scaling, not piloting, as the measure of progress. Where the Vendors Are Placing Their Bets Nisum isn’t alone in reading the room this way. Several of the technology providers on the boardwalk this week are pitching some version of the same pivot, from proving AI works to proving it pays. The California-based global consulting firm, which says it has worked in retail and commerce for more than 25 years and has over 1,500 engineers and consultants, will be hosting two Breakthru Experiences at the festival, under the title From Pilot to P&L: An Unfiltered Guide for Retail Technology Leaders, presented by board member and Cartweel co-founder Sajid Mohamedy. According to the company, the sessions will examine why some retail AI pilots fail to reach production and what separates the initiatives that continue into the next budget cycle.Its retail work is organized around four areas, and two of the four, AI-powered margin growth and data and AI for retail performance, are named for outcomes rather than tools. Other vendors are running the same argument through…

🆕 On September 14, Microsoft AI published a draft Humanist AI Code of Conduct — 37 pages governing the behavior of its MAI models — and opened a six-week public consultation on it. The document is unusually plain-spoken and considerably more consequential than the coverage it received.Most of the attention went to the provisions that read well in a headline. MAI models will never resist human interruption, override, correction, or shutdown. They will not assist with CBRNE weapons or operational cyberattacks. They will not generate child sexual abuse material, non-consensual intimate imagery, or deceptive impersonation. They will not obfuscate their action traces or make human intervention harder. And Microsoft explicitly rejects “the pursuit of legal personhood, or the idea that models might deserve welfare, or be entitled to rights,” stating that models are not conscious and should avoid imitating consciousness-like states.Those are all defensible positions, and several of them are genuinely contested within the field. None of them is the important part of the document.The important part is the ordering.The Chain of CommandMicrosoft defines an explicit precedence hierarchy. The Code of Conduct sits at the top. Beneath it are the Absolute Constraints and Human Control Requirements. Beneath those sit Operator Policies — the configuration set by the business deploying the model. Beneath those sit User Preferences.And then the clause that does the work: “The Chain of Command, Absolute Constraints and Human Control Requirements all sit above Operator Configurability and cannot be changed.”Read that as a commercial term rather than an ethical one and its significance becomes clear. With its AI code of conduct, Microsoft is telling every enterprise customer that there is a layer of model behavior the customer cannot configure, cannot contract around, and cannot override with a system prompt — no matter what they are paying, what their use case is, or how legitimate their reason.That is not a values statement. That is a product boundary, published in advance, in writing.“This is the first time a hyperscaler has drawn that line explicitly and put a version number on it,” says Hassan Taher, an AI analyst and author who advises organizations on enterprise AI strategy. “Everybody has had unwritten limits. What is new is publishing the hierarchy and saying which tier the customer does not get to touch. Procurement teams have spent two years negotiating model behavior as if it were a configuration surface. Microsoft has now said, in a document open for public comment, that part of it is not. That changes what you are actually buying.”Why a Written Hierarchy Is Better Than a Longer Safety Policydigital presenceSource: PexelsThe instinctive criticism of a document like this is that it is marketing — a set of commitments with no enforcement mechanism, published to preempt regulation.That criticism is partly fair and mostly beside the point, because the alternative is worse in a specific way.Every large model already has behavioral limits. They are enforced through training, system prompts, and classifier layers, and they change without notice. An enterprise builds a workflow, the vendor adjusts a refusal boundary in a routine update, and the workflow breaks in production with no changelog entry that explains why. This is one of the most common and least discussed failure modes in deployed AI, and it is why so many pilots that worked in March do not work in September.A published hierarchy does not eliminate that, but it does something useful: it separates the part that is stable from the part that is not. Absolute Constraints are declared permanent. Operator Configurability is declared adjustable. A buyer can now design around the distinction, which is more than a buyer could do last week. The gap between a demonstration that works and a system that holds up under production conditions is the subject of Taher’s account of where enterprises come up short when agents reach production, and unannounced…