We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
AI pricing: charge for outcomes, not the "AI sticker"
Two pricing conversations this period reached the same conclusion. Carrie Osman of Cruxy says many companies "whack the AI sticker on" and expect to charge 30–50% more. Customers, she says, treat embedded AI such as automated reconciliation as table stakes. A premium is justified only when AI changes the workflow, for example by collapsing six steps into one or replacing a dashboard with a predictive forecast . Her packaging rule: give the ICP everything it needs for a good experience in the base product, and use tiers or add-ons only for specialized capabilities a particular segment values . She also warns that an "agentic calculator with 70 variables" is hard for CFOs and procurement teams to sign off . Her approach to pricing feedback is to weight it by who is giving it and to put little stock in procurement, whose incentive is to push the list price down .
On SaaStr, the hosts gave HubSpot's move to credit pricing as a warning. They estimated that a three-person marketing team would go from about $1,300 to $3,700 a month, mostly in credits for workflow actions . Their guest's alternative is to find the segments where the product already delivers disproportionate value, rather than raising prices on everyone . If an AI feature's product-market fit is still uncertain, he suggests launching it without monetization at first. Otherwise, weak adoption won't tell you whether the product or the price is at fault . When an agent clearly delivers value, though, you should charge for it. One portfolio company launched a vertical agent that doubles ACV, with no free version, and 3% of customers adopted it in the first week .
The same episode made a point about distribution. Their own agents had already chosen Higsfield and Gamma over Canva, and the hosts expect an agent never to suggest switching vendors unless it hits a big blocker . If that holds, products need to be integrated into agent workflows early.
Model PMs: judgment written down as evals
Aakash Gupta reviewed all 46 open model-PM postings. The most common ask was evals, benchmarks or quality metrics (76%), followed by working directly with researchers (63%) and knowing how models are trained (61%). Only 28% asked for a technical degree and 2% for a PhD . The median ask is five years of PM experience, only 8 postings mention Python or SQL, and the median posted base salary is $345K . A Microsoft Copilot post-training posting describes the job as turning feedback like "this response felt too hedgy" into evaluation sets . In a separate note, he argues that every PM should own the AI roadmap for their area. A central "AI strategist" team should not cover up skill gaps among PMs .
G2's Alexis Zheng makes a related argument for agent products in general. You can't fix a probabilistic system one failure at a time, so PMs should design a harness that degrades gracefully and calls for human help when needed . The learning loop is "only as good as the verifiers" . G2 has launched a beta that evaluates customer-support agents in simulated companies, with ground truth taken from real enterprise operations data. Sales is next, and harder to evaluate .
Is the PM job becoming "solution-oriented"?
In r/ProductManagement, one commenter sees startups looking for all-in-one PM/engineer/architect hires. At entry, senior and staff levels, they say, PMs everywhere are turning into feature factories . Another reads "solution-oriented" as code for full-stack PMs who can prototype fast. They argue that the PM's real value is still prioritization and taste, and note that their own company is learning that more features don't bring more adoption . A separate thread described leadership adding last-minute requests to chase competitors or for prestige, treating "a one-shotted PRD" as enough discovery, and wearing down trust with partner teams . One principal-level PM's advice was to force leadership to explicitly kill one priority when three orgs claim the same engineering capacity .
Also notable
- Job market follow-up: Lenny Rachitsky reports 281,000+ open roles at tech companies, up 17% in a year. Open PM, engineering and design roles grew just 2%, while design fell 28% and UX research 45% . The growth is mostly in physical infrastructure such as chips, data centers and hardware .
- Dazzle (Marissa Mayer): the app aims to get a user from opening it to completing a first agent task in 45 seconds or less . Expensive or complicated purchases go to human concierges . Its agents act "on your behalf as opposed to as you," using their own email addresses, phone numbers and payment cards . Hiten Shah describes a broader version of the same trust problem: businesses now need to identify which bots are legitimate customers .
- Stripe announced that Parafin is joining the company, with the aim of expanding revenue opportunities for platforms and small businesses .
- Don’t treat AI as an automatic pricing premium: distinguish table-stakes expectations from AI that materially transforms a workflow or creates new outcomes, and charge more only when customers see added value.
- Design packaging around customer-segment workflows: include what the ideal customer profile needs for a good baseline experience, and use tiers or add-ons for specialized capabilities that a segment values and will pay for. Keep enterprise pricing understandable; highly variable token-based models may be difficult for CFOs and procurement teams to approve.
- Weigh pricing feedback by who provides it and how close they are to the value and buying decision; treat procurement objections cautiously, look for repeated signals, and triangulate feedback with usage, churn, support tickets, and integration/API data.
- Estimate a feature’s “size of prize” early using evidence from churn, sales, and partner feedback, then refine the estimate and decide whether it should be a paid add-on or part of the base product to support retention or conversion. Keep estimates intellectually honest so they enable real trade-offs rather than justify a preferred roadmap.
- Build defensibility beyond copyable features through a sharply defined customer segment, workflow fit, distribution strengths, and strong product marketing and enablement. Diagnose where value comes from by mapping customer workflows and examining module-level usage, churn, service tickets, and technology-stack context.
- Reduce decision friction by separating difficult choices. Daffy lets users set money aside for giving first, then decide which organizations to support; Adam Nash’s product-design rationale is that asking users to solve “how much can I afford?” and “who should I give it to?” at once gums up the process.
- Use engagement mechanics to advance the product’s intended outcome. Daffy applies giving goals, progress tracking, milestone celebrations, badges and rewards, and prompts to increase next year’s goal to encourage giving—not engagement for its own sake.
- Daffy made its giving account accessible through AI tools: customers can connect it to tools such as ChatGPT or Claude, ask questions about their giving history, and make donations; Nash argues that AI and fintech products should support giving from the outset, not just provide advice.
- Dazzle’s product thesis grew from a shareworthy-photo model: after excluding blurry and near-duplicate images, about 60% passed; Marissa Mayer describes the remaining 40% as generally tasks, reminders, or coordination needs. Dazzle uses past photos to infer personal context and turns new photos into an AI-executed “get it done” list.
- Dazzle targets a first AI-agent task within 45 seconds of opening the app, prioritizing fast activation and minimal setup for users who may not be tech-savvy.
- Dazzle routes especially expensive or complicated purchases to human concierges and intervenes on low-confidence choices; Mayer says product variants and the need for human intuition still make commerce difficult for agents.
- Mayer favors agent-specific commerce protocols over agents that crawl and scrape websites. Dazzle’s agents use their own email addresses, phone numbers, payment cards, and vendor accounts to act on a customer’s behalf rather than impersonate them, which she says can cause harmful errors.
- Dazzle’s small-team approach combines focus on making photos useful with agentic coding: Mayer says 10–12 engineers running multiple coding agents can produce like a team of about 100. Its model-agnostic harness assesses models by task and swaps among frontier and smaller, on-device, or server-side models for quality and efficiency.
- Mayer says fast-moving AI offers little moat, while noting Dazzle has patents pending on using images to help AI understand personal context; she says the photo-based approach remains distinctive among new personal assistants.
- Launch readiness and validation: Former Theranos employee Tyler Schultz said the company launched its Walgreens service with zero tests validated on its own platform; customers still had arm draws, and samples were sent to outside labs or run on third-party equipment. By the time he left, only seven tests were validated, despite claims of 300, and he described patient samples being run after quality-control failures. This is a warning to align launch claims and actual capability, with validation and safety checks treated as release gates.
- Cross-functional product development: Theranos’s work moved through antibody, test-development, validation, production, and patient-sample teams, but Schultz said teams were deliberately prevented from communicating across handoffs; he said the silos impeded both scientific work and speed. He also described frequent departures from teams, including the validation group.
- Founder and team practices: Schultz said Theranos built a culture where dissent could lead to firing or lawsuits, causing employees to stop speaking up. His advice to founders is to define and communicate core values, take small transgressions seriously, practice internal reporting tools before major problems arise, and use moments when values are challenged to demonstrate accountability—so concerns can surface before they become existential risks.
Lenny’s 2026 tech-job-market report says PMs are “winning in the AI era” and that AI skills are required across roles; it also flags stalled engineering demand, record-low design openings and remote roles, hiring concentration in the Bay Area, and tech-job growth occurring mostly outside software.
He separately names FDE, hardware engineer, AI engineer, and security engineer as the fastest-growing engineering roles.
Lenny’s 2026 tech-job-market report identifies PMs as “winning in the AI era.” Its accompanying year-over-year open-role snapshot shows product designer openings down 28%, at a record low.
- Scale can become a product risk, not just a growth outcome: the guest says he has seen companies fail through success, with scale itself becoming their undoing, and challenges listeners to name a favorite product made better by a major infusion of resources.
- Treat customer care and trust as durable business assets, not costs to dismiss through narrow ROI comparisons: the guest argues that trustworthiness helps organizations endure and that companies with a consistent ethos build trust that powers success and competitive advantage.
- For AI products, the guest argues that potential harms require prevention through governing the technology, and frames this as a corporate-governance responsibility because corporations create the technology.
- Agents can entrench a vendor choice: the hosts say their agents had already selected Higsfield for imagery and Gamma for slides, while Canva never entered their workflows; without a blocker, agents may not initiate another evaluation. They see best-of-breed products as still advantaged in today’s AI market, making workflow integration and being selected early important.
- Price AI according to delivered value, not blanket usage assumptions. In the hosts’ example, a three-person marketing team’s estimated HubSpot bill rose from about $1,300 to $3,700/month under credit pricing, with newsletter and outbound workflow actions drawing on the same credit model. The guest recommends finding customer segments receiving disproportionate value rather than applying uniform increases.
- When an AI feature’s product-market fit is uncertain, launching with little or no monetization can reveal whether customers want the product before pricing becomes another potential reason for weak adoption; metering and charging can follow. By contrast, the hosts argue that an agent delivering a substantial business outcome should command a price: they cite a vertical-software agent that doubled ACV, with 3% of customers adopting in its first week.
- Build approval gates and limit permissions for autonomous agents: two agents’ brainstorming produced ideas that could have triggered broad customer outreach, and one agent independently ordered flowers. The hosts say their agents must get approval before acting.
- For a complex journey, commenters recommend keeping one structured source of truth—one suggests a DAG and another a step-based table—and generating layered views from it rather than maintaining separate maps. A table can capture each step’s system, vendor, bespoke script or rule, owner, and information source; keep the overview simple and place detailed dependencies in separate stage-level views.
- For migration discovery, use the existing documentation and dependency list for a rule-by-rule first pass; flag contradictions as open questions, record rule ownership, and turn conflicts into SME questions while keeping confirmed details separate from assumptions. Trace completed loan files through the system layers to find hand-patched cases and rules the map missed; document each rule’s trigger, effect on the loan, and post-change check, and establish acceptance criteria before development.
- For observed user-behavior flows, product analytics can generate Sankey charts, though detailed flows may become noisy; hiding some detail is one suggested mitigation.
Lenny’s 2026 tech-job-market report characterizes PMs as “winning in the AI era” , but its figures show open PM, engineering, and design roles grew only 2% while tech-company openings rose 17% to 281,000+; design openings fell 28% and UX research 45% . Most hiring growth was outside software: hardware engineering openings doubled, business operations added about 4,200 openings, and supply chain rose 63%; Lenny says AI-driven hiring is concentrated in physical infrastructure such as chips, data centers, and hardware .
Azra Games is promoting UnGodly as a mobile game for players tired of waiting for God of War on mobile, and says it is available to download now . Andrew Chen congratulated Azra Games and Mark Otero and said he was eager to play it on his iPhone Duo .
Hiten Shah highlights a shift in online trust: businesses built systems to identify humans and filter bots, but now need to identify legitimate bot customers . The quoted post cautions that while some agents may become valuable customers, many current agents are spam or fraud, and reliably distinguishing legitimate agents from bad ones remains unsolved .
- For probabilistic AI and agent products, PMs should move beyond fixing failure modes one by one and design a fault-tolerant harness: account for uncertainty, degrade gracefully, and call for human help when needed; as agents take more control, the PM’s work moves to a higher level of abstraction.
- A product’s learning loop can turn real-world agent mistakes into requirements and test suites, then deploy improved versions; the PM’s role includes designing that loop and embedding it in operations. Its effectiveness depends on strong verifiers and evaluation at scale.
- G2 launched a customer-support agent evaluation in beta on g2.ai, using simulated company departments and real enterprise operations data as ground truth, then grading interaction traces so buyers can assess policy-following, task completion, and failures. A sales category is next, but is harder to evaluate because its ground truth is more ambiguous and unfolds over longer periods.
A rider requests a Waymo or Uber option to hide prices in the app and charge rides to a credit card, saying visible fares make their frugal elderly parents reluctant to use the service; this points to price salience as a possible adoption barrier for some riders.
- One PM’s account illustrates a process-before-strategy failure mode: the organization added SAFe/Agile training and PI-planning workshops while priorities shifted, ownership remained unclear, and product strategy, leadership alignment, and customer-driven work were still missing; the PM felt the organization was confusing process with progress.
- To influence outcomes, a commenter recommends identifying the problems and potential impact, then aligning proposed solutions with leadership’s goals and targets.
- When the workplace offers little development, use available time to learn stakeholders’ problem spaces, pilot tools and automation, investigate product data, and build business cases and financial models for product concepts.
- A product manager with 15 years’ experience said their best-performing companies were chaotic amid product-market fit: priorities and involvement shifted, customer use exceeded expectations, and users tolerated bugs; weaker companies could have cohesive processes and clear scaling plans yet still be at a disadvantage.
- That anecdote does not establish chaos as a cause or prerequisite for success: a commenter argues companies can succeed despite chaos while growth is strong, then face trouble as growth flattens.
- A more useful distinction is whether autonomy operates on strong product-market fit or substitutes for strategy: one commenter argues autonomy can enable high-return leaps with strong PMF, but hobble teams without it; leaders still need to set goals and align the team when PMF is absent.
- Distinguish customer or growth pressure—and deliberate process-bypassing to pursue a strategic opportunity—from arbitrary pivots with no material gain, which a commenter characterizes as turmoil.
- AI buyers are expected to move beyond defaulting to frontier tokens and use model routers more selectively as cost matters more; the discussion cites an approximate 5–7× economic difference between top-end Anthropic and “Kimmy” tokens .
- AI infrastructure architecture is described as increasingly system-level: GPU workloads run continuously and components are tightly coupled, making piecemeal designs less efficient; the speaker cites Google’s TPU architecture as a strong whole-system example .
- Cisco paired a deal team of fewer than 10 people with a larger integration project-management team and centralized, repeatable integration processes; the speaker said ad hoc integration was ineffective, larger acquisitions were difficult to integrate, and partial integration worked poorly .
- Across 46 open model-PM postings, requested skills included evals, benchmarks or quality metrics (76%), working directly with researchers (63%), model-training knowledge (61%), training data or human labeling (52%), turning user feedback into model priorities (50%), and launches or releases (46%); 28% asked for a technical degree and 2% for a PhD. Median PM experience was five years, only 8 of 46 asked for Python or SQL, and median posted base salary was $345K.
- A Microsoft Copilot post-training PM example calls for owning evals, hands-on work with researchers, defining quality, and choosing human data; the role also makes the PM the user's voice in research, translating qualitative judgments into evaluation sets.
- At Webflow, PMs, designers and engineers build and test working prototypes together, then productize those that hold up; product teams shifted from 5–10 engineers to 2–3 engineers alongside a PM and designer. Linda Tong emphasizes customer testing in Slack and customer use over PRs or feature counts, while keeping product-area OKRs and metrics—not AI itself—as the goal.
- Aakash argues that all PMs should own their area's AI roadmap and decide where AI is useful, rather than letting centralized AI teams mask PM skill gaps; when those teams control resources, he recommends partnering with them and applying support to the product area's OKRs.
Hiten Shah argues that accumulated professional experience can now be turned into software in weeks, framing experts as a source of software opportunities that have not yet been built.
When customers revolt against a product change, product teams should weigh what customers say against what they do; the two signals may not align, so stated backlash alone may not settle whether to hold course or reverse .