The AI Market Right Now: “Autonomous software” over shiny demos

What investors care about has shifted fast. Things that wowed a year ago are now table-stakes. At the application layer (vs infrastructure), investors are now prioritizing products that behave like autonomous teammates, software you manage, not “use.” Think: set up once, then Slack, email, or call it like you would an employee.

The agent shake-out is real. AI SDRs were early darlings; many hit around $2M ARR quickly then churned when quality and retention fell short, some even faking numbers. Lesson: full end-to-end autonomy was premature. Winning teams now focus on fitting real workflows, not replacing them wholesale.

Takeaway: Pitch “autonomous outcomes” anchored in a clear, owned workflow. If your agent can run largely unattended and fit neatly into the way work already happens, you have investors’ attention.

Differentiation: Own a vertical, master the workflow

During the discussion, Danny shared how pivoting his AI tool from a broad productivity assistant into a VC-specific workflow instantly improved traction and feedback. Irina added an example from one of her portfolio startups in healthcare, which narrowed its focus to automating claim processing for dental offices rather than all medical practices. That specialization allowed them to achieve faster integrations, clearer messaging, and a defensible customer base.

Irina’s diligence lens: the best pitches aren’t feature tours; they demonstrate real customer understanding, edge-case handling, and end-to-end fit (e.g., customer service agents that handle the real “call me when things break” moments).

Danny’s founder-builder lens: he pivoted a horizontal “do-tasks from Notion + email” agent into a VC-specific workflow product and immediately found a moat and better feedback. The meta-lesson: specialization builds defensibility.

Founder checklist to sharpen differentiation

1. Pick one ICP and one “hair-on-fire” workflow.
2. Map the last 10% of tricky edge cases and build for those first.
3. Prove autonomous value inside that workflow before expanding.
4. Teach the product to speak the team’s native artifacts (e.g., CRM objects, case IDs, claims, tickets).

Services as a bridge

Historically, “services revenue” raised eyebrows. Not anymore.

• Collin: Quome sells AI-powered services today to deliver full outcomes while the pure product matures, treating LLMs like a swappable brain with guardrails and tests around it.
• Danny: Early revenue matters in an era where coding is easy. Services shorten time-to-cash, fund the product, and validate the workflow you’ll later productize.

Play it this way: Sell “AI-powered implementation” now; use those engagements to harvest reusable configs, prompts, evaluation suites, and adapters—the seeds of your product moat.

Building with LLMs: Guardrails over fine-tuning (early on)

Treat the LLM as an interchangeable core. Avoid owning training costs unless you’ve proven ROI. Invest instead in orchestration, evaluation, and deterministic rails. Most defensibility lives in problem-specific constraints, test harnesses, and integrations, not in the base model.

Practical stack guidance:

• Brains: flexible (closed or open-source as needed)
• Memory & facts: retrieval + structured stores
• Control: tools, policies, allowlists, and “don’t try” boundaries
• Quality: offline evals + live guardrails for the final 20% that makes or breaks production

IP & Legal Strategy: Offense and defense without burning runway

Don’t patent everything. File surgical patents only where you’ve got a truly novel, durable use case (your “knife in the fight” against giants). Otherwise, prioritize trade secrets, copyright in original coordination/selection/arrangement, and trademarks for brand.

If AI helped author your code/content, what do you own? The more purely AI-generated, the weaker your copyright claim. Layer in meaningful human expression where ownership matters (especially for creative assets or code structure).

Stage by stage: Engage counsel early for IP strategy and streamlined contracts to cut sales cycles. Save heavier spends (like privacy infra) for Series A scale-up unless customers demand it sooner.

Risk, Cost & Compliance: The 3:00am reality

Founders also discussed the importance of planning budgets and using operational tools to manage these risks. Common practices include setting clear monthly cloud spend limits with tools like AWS Cost Explorer or CloudZero, tracking uptime with monitoring platforms such as Datadog or New Relic, and maintaining clear incident playbooks in Notion or Linear. These habits keep operations sustainable and help prevent costly surprises.

• Cloud bills: Many teams forget compute can hit five figures per month quickly, so budget runway accordingly.
• Production is harder than the demo: Expect failures (often at bad hours); have an on-call plan and remediation runbooks.
• What to do now: If you’re a B2B enterprise, you’ll need SOC 2 or HIPAA to close big logos. If you’re D2C or early, ship with basic security and iterate, no one’s suing pre-traction, but data leaks will kill you.

Data Governance & Where the model runs

Healthcare buyers increasingly prefer on-system deployments and zero-data-retention setups. The good news: modern open-source models are now “good enough” for many on-prem use cases. Arm yourself with data governance docs/FAQs to speed procurement.

Caution on indemnities: Model-provider IP indemnities often arrive with many exceptions. Don’t over-rely on them.

Bias, UX, and “who gets served”

Bias isn’t academic, it shows up as failed appointments and dropped calls. Irina’s test: several voice agents hang up or fail to recognize her (female, English not first language). If you’re building “agents,” your QA set must represent real users, and not everything needs to be an agent, sometimes a calendar widget is better.

Regulation is fragmented (EU ahead, US state-by-state). Don’t freeze; build toward the strictest relevant standard when you start selling into that category (like Illinois biometrics), but don’t let compliance stall pre-product-market fit.

Founder Playbook (from the panel’s hard-won advice)

1) Nail the wedge

• Choose one vertical and one painful workflow.
• Define “autonomous outcome” and the exact human-in-the-loop boundaries.

2) Ship services-backed outcomes

• Sell implementation/services to generate revenue and capture repeatable product components (prompts, policies, adapters).

3) Engineer for the last 20%

• Add guardrails, eval suites, and operational runbooks. Budget real on-call.

4) Be smart on IP

• Reserve patents for truly novel, durable inventions. Document trade secrets, brand aggressively, and keep meaningful human authorship where ownership matters.

5) Close deals faster with trust artifacts

• Prepare a security overview, data-flow diagrams, zero-retention posture, and FAQs to bypass legal/procurement bottlenecks.

6) Test for inclusivity

• Include diverse voices, accents, and edge cases in QA. If your agent can’t book Irina’s appointment, it’s not ready.

Memorable lines & moments

• “It’s software you manage, not software you use.” – on agents as autonomous coworkers.
• “The more you use AI and the less you input, the less you own.” – on copyright and human expression.
• “It’s super easy to get a prototype that looks awesome. You get excited about it, maybe even land a sale, but that’s only 20% of the effort. The last 20%, actually getting it to work in production, is 80% of the real work..” – on getting to production.

Takeaways

• Investors want autonomous outcomes embedded in a real workflow.
• Services are a perfectly fine (and often optimal) on-ramp to revenue and product.
• Your moat is the boring stuff: domain depth, edge-case handling, rails, and GTM artifacts that let enterprises say “yes.”
• Own what matters (IP), secure what’s sensitive (data), and test with the people you aim to serve.