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The creator argues that live shopping is a seasonal business's growth engine: a trinket shop in Nantucket closed eight months of the year can go live from the store because no one is walking in anyway, turning the store into a studio. He notes many people have side hustles and seasonal businesses that could benefit.
A restaurant owner's part-time finance person should review how wholesaler prices have changed at least quarterly — and the speaker endorses running the analysis at least monthly, whenever prices change, or even daily if a wholesaler changes prices daily — to catch overpaying (e.g., a price up 20-30%) or to shift orders to different wholesalers.
advice attributed to the speaker; the 20-30% price-increase example carries a possible numeric error flag
GaryVee says that if he were starting an entire empire from a single laundromat today, the number one thing he would do is get into content.
GaryVee says the biggest opportunity for local businesses people visit in person is running 10-mile radius Facebook ads to grow traffic and average receipt size.
Oliver Kennon recommends using price per token as a practical proxy for delivered intelligence output, especially for comparable workloads and quality requirements. He notes the limitation that the North Star metric — price per successfully completed task at required quality — is not yet standardized.
If AI can do the work your software manages, build the AI agent yourself and start selling it rather than defending the legacy product. If your per-seat users are being replaced by AI agents, build the agent yourself — e.g., if you sell $50/month/seat receptionist software, charge $500-$1,000/month for a full-on AI agent and go all in on that. When pivoting from per-seat software to an AI agent, price dramatically higher — move from $50/month per seat to $500 or $1,000 per month.
Consequential creator advice requires independent verification before Today can promote it.
Sell the outcome instead of the tool: if you believe customers will stop clicking on your SaaS and instead buy outcomes, become the vendor who delivers the outcome directly — even if that means charging 10x the current price. Run the exercise: take your current price (e.g., $75/month) and ask what you would have to do so the same customer would pay $750/month — then identify the adjacent workflows the person clicking your tool performs that you could offer as a complete solution. Labor budgets are much larger than software budgets, so moving up into labor budgets is a bigger opportunity than charging more for software. Adopt the '10X is easier than 2X' mindset: AI creates the opportunity to 10x businesses that previously struggled to grow 2-3x.
endorsed
Commercial context: The creator presents this as their own offering and directs viewers to get it.
Roughly 40-50% of recent Y Combinator launches are agentic SaaS — companies selling outcomes rather than tools — indicating how mainstream the model has become. If you sell outcomes at 10x pricing, expect AI worker output to be only 70-80% reliable, not 100%, and plan to build safeguards, monitoring, and a sales force (replacing self-serve). Treat the workload as the cost of survival rather than a reason to opt out.
Lincoln Murr recommends that websites gate their content behind very small microtransactions charged specifically to agents — e.g. one ten-thousandth of a cent per read — and points to AWS and Cloudflare (which he says control roughly 40% of internet traffic combined) as evidence that microtransaction gating via X402 is already being enabled.
traffic share figure flagged as possible numeric error; publisher-reported
Websites can gate their content behind a very small microtransaction specifically for agents. As an example, a Substack publisher could charge an agent one ten-thousandth of a cent to read their content, which is trivial to the user but a meaningful revenue driver. Agent microtransaction payments should ultimately be fully abstracted away from the user, in the same way AI subscriptions are handled.
A method for understanding agent behavior: manually review production traces, filter the sample to specific cases (e.g., disputes, credit card topics), and accept that reviewing thousands of traces is infeasible so the process must be simplified.
A method for tuning LLM-as-a-judge: judge a representative sample yourself, run the LLM judge, and optimize for coherence with human raters; start loose with a few too many false positives on production data and tighten over time; check the judge periodically to keep it representative.
Build a Sales Navigator search on 'connections of' a senior executive against your account list and save it, so you're alerted whenever that executive makes a new connection in your territory. Prioritize tracking executives who speak often, travel often, or are very active on LinkedIn because their networks constantly expand.
Create a saved Sales Navigator search of your accounts filtered to first-degree connections and save it so you receive lead alerts whenever a first-degree connection changes jobs into your territory, letting you capitalize on the newness of the move.
Set up a 'viewed your profile recently' alert filtered to your current company account lists to catch anyone in your territory checking you out. Don't filter by seniority or geography — anyone in your territory looking at your profile is worth engaging, from BDR to marketing coordinator.
For a first-degree connection with no open opportunity, send a nurture note with a helpful article related to something you sell, can help with, or a past shared passion — not a sales pitch. When a senior manager from a company where you have an open opportunity views your profile, send a connection request referencing your work with their team without calling attention to their tie to the deal. For a stranger with no open opportunity, send a connection request noting you work with their industry peers on a specific challenge and offering help or a chat.
Save every deal's stakeholders — the people you're multi-threaded into — into a Sales Navigator list at close, so you can track them via 'client job hopper' searches and get notified when they change jobs and where they go. This approach uses lists rather than saved searches.
To track customer execs changing jobs, filter Sales Navigator by current company (your territory) and past company (an account list of your biggest logos, e.g., Fortune 50 that work with you, or by verticals like law firms or pharma). Narrow by seniority level and geography, or large companies with people moving around will flood you with results.
Set up a Sales Navigator search that alerts you anytime a new executive joins your own organization. New executives are eager to make a fast impact and can drive value in about 15 minutes, and connecting with them is one of the fastest ways they can make that impact.
Pamela Fox walks through the concrete setup for an LLM judge: put the LLM in judge mode and tell it it is an evaluator, tell it what it is evaluating, give it the query and response (plus context for groundedness), and force it to output a pass/fail verdict together with a reason.
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