Who Books This Speaker

AI conference speaker bookings come from a wider buyer set than agent-specific events. Knowing which buyer is calling helps shape the right talk.

• Conference programmers for major AI events: AI Engineer Summit, AI Agent Conference, MLOps World, AI4, Strata-style data and AI events. They want a credible practitioner anchor talk, not a sponsored pitch
• Industry conference organizers (finance, healthcare, manufacturing, defense, legal) adding AI tracks to a broader program. They want a translator: production AI for an audience that is not natively engineering
• DevRel and field marketing teams at AI infrastructure vendors: model providers, orchestration frameworks, eval platforms, vector databases, GPU clouds. They want customer-credible voices for their user conferences
• Internal AI day organizers at Fortune 500 companies: a one-day company-wide event with two outside speakers and a series of internal talks. The outside speaker anchors the day
• Executive summits and CEO offsites: 30 to 80 attendees, board members and operators, looking for a fireside that pressure-tests their portfolio companies or their internal strategy
• Investor LP days and venture firm offsites: VCs hosting LPs or portfolio CEOs, looking for a practitioner to ground the AI conversation in build-cost realism
• University centers and policy events: academic and government audiences that want production-grounded talks instead of trend reports

Common Speaking Topics

The talks span the full AI engineering surface, calibrated to the audience. The list below is the recurring core.

• The architecture decisions that determine agent and LLM application quality at scale: where most production wins actually come from, where teams burn months on the wrong abstraction
• What two years of running AI in production has actually taught us: the gap between demo and ship, eval debt, the operational disciplines that compound
• Why most AI ROI claims fail finance review: how to construct an ROI story that survives a CFO conversation, the failure modes of the typical board-deck claim
• The engineering discipline behind reliable LLM applications: evaluation, observability, regression sets, drift detection, golden trajectories
• The realistic shape of the AI talent market in 2026: who is actually senior, where comp lands, what hiring loops should test for, where contractor and fractional models work
• Agent vs workflow: the Anthropic distinction applied to real engineering choices, when to escalate, when to stay simple
• The retrieval problem: why RAG is harder than the demos look, what hybrid retrieval actually buys you, when to skip it
• Cost shapes for LLM applications: prompt caching, model routing, batch vs streaming, where the 80 percent reduction case studies actually come from
• AI strategy for non-technical leadership: how to read a vendor proposal, how to evaluate an AI hire, how to scope the first pilot, how to know when to kill it
• The MCP inflection point: how the Model Context Protocol reshapes tool design across Claude, ChatGPT, Cursor, and the rest of the agentic stack
• Governance and risk in production AI: regulated industries, audit trails, model risk management, the practical compliance shape in fintech and health

Formats Offered

The right format depends on the audience and the slot. Each format has a different prep shape and a different on-stage rhythm.

• Plenary keynote (30 to 45 minutes): one argument, three to five examples, designed for the largest room of the event. Closes with a memorable claim
• Deep-dive technical talk (45 to 60 minutes): more code-level detail, often paired with Q&A. The default for AI engineering conferences and focused summits
• Fireside chat (30 to 45 minutes): moderated, conversational, lower slide density. The best format for executive summits and customer events
• Panel (45 to 60 minutes): the practitioner role is to challenge hype-cycle claims and ground the conversation. Works only with a strong moderator
• Workshop (half-day to two days): hands-on training for engineering teams, separate from the speaking format. See the LLM workshop entry
• Closing keynote: distilling the event into a forward-looking talk that ties back to what the audience saw across two days
• Private executive session (60 to 90 minutes): off-the-record, one company, often run as a roadmap review with a technical guest
• Multi-talk residency: keynote plus workshop plus office hours over one or two days, common at customer conferences and internal company AI days

What the Audience Gets

A talk earns its place on the agenda when at least 30 percent of the room walks out with a decision they will make differently. The structure below is the contract.

• A defensible mental model for the design space being discussed: orchestration, retrieval, evaluation, cost design, hiring
• A short list of decisions to make differently in their own systems, with tradeoffs surfaced explicitly
• Concrete numbers: token costs, latency budgets, hiring comp, vendor pricing ranges, eval thresholds. Specifics, not handwaves
• A vendor-neutral pointer set: papers worth reading, frameworks worth trying, observability tools worth installing
• A debugging vocabulary the team can use back at work: context rot, planning drift, eval debt, prompt caching gap
• A list of things not to do: the negative space of the talk, often more useful than the recommendations

Logistics, Fees, and Lead Time

The 2026 keynote market has segmented into clear tiers. Practitioners with real production credibility, an independent voice, and a public track record cluster in a specific range. The numbers below reflect AI and technology speakers at the practitioner tier, distinct from generalist futurist headliners who command much higher fees.

• Fee range (US, technology and AI practitioner tier): $10,000 to $30,000 for a single keynote or deep-dive session, $20,000 to $50,000 for top-end practitioner talks with customization and live demos
• For comparison: emerging tech speakers cluster at $2,500 to $7,500; established professional speakers at $10,000 to $30,000; futurist headliners at $25,000 to $150,000+
• Customization premium: 15 to 25 percent added when the organizer requests a deeply tailored deck for a specific audience or product context
• Live-demo premium: live AI demos require dedicated bandwidth, backup capture, and backup endpoints; AV cost typically borne by the organizer
• Travel: business-class flights for international, hotel, ground transport pass-through. Often waived for nearby events
• Virtual delivery: 30 to 50 percent below in-person, same prep depth, calibrated for the virtual room
• Lead time: 8 to 16 weeks comfortable for customized keynotes; 4 to 8 weeks workable for recurring topics; under 4 weeks possible only off-the-shelf
• Recording rights: standard organizer recording rights granted on a per-event basis; perpetual marketing use of the recording typically negotiated separately
• Cancellation: standard graduated cancellation fees if the event is moved or canceled within 30 days, with travel sunk costs reimbursed

The Practitioner Voice on Stage

The distinguishing feature of a practitioner talk is that every claim has shipped. The examples come from systems the speaker has actually built or operated. The numbers come from invoices, dashboards, and on-call rotations. The vocabulary stays engineer-legible even when the room is executive.

The standard the audience applies, often unconsciously, is whether the speaker has done the work. Most rooms can tell within 5 minutes. The practitioner who can describe a real failure mode with specifics wins the room; the futurist who cannot is found out quickly.

• Every architecture pattern presented has been built or operated, with a named scale and a named cost shape
• Every recommendation has a counterexample: when it does not work, and what to do instead
• Every vendor named appears with both its strength and its sharp edge
• Every claim about the market is backed by specifics: hiring comp bands, vendor pricing, eval benchmark numbers, production failure rates
• Every prediction is tagged with its confidence and a falsifier: how to know when the prediction is wrong

Past Talk Themes

The recurring themes below have anchored talks at agentic conferences, AI engineering summits, executive offsites, and industry tracks. They evolve as the field does.

• Agentic Architecture: composing models, tools, memory, and control flow into goal-seeking systems that survive production
• Building Effective Agents: the Anthropic workflow vs agent distinction applied to real engineering choices
• Eval Discipline for LLM Applications: rubrics, regression sets, LLM-as-judge calibration, golden trajectories
• Cost and Latency Design for AI Applications: prompt caching, model routing, batch vs streaming, where the savings actually come from
• The AI Talent Market in 2026: comp bands, where senior engineers actually are, hiring loops that work, how to evaluate AI engineers without a research background
• Why Most AI ROI Claims Fail Finance Review: anatomy of a credible ROI case, where the typical claim breaks down
• AI Strategy for Non-Technical Leadership: a talk for boards, CEOs, and operators who fund AI but do not build it
• The MCP Inflection: tool design after the Model Context Protocol changed the economics

Right Fit and Wrong Fit

Practitioner speakers are not the right answer for every event. Calibrating fit saves both sides money and audience attention.

• Right fit: AI engineering conferences, industry conferences with serious AI tracks, executive AI summits, customer conferences for AI infrastructure vendors, internal company AI days, board offsites with serious technical content
• Right fit: audiences that will recognize and reward specificity. Most engineering audiences, increasingly many executive audiences
• Right fit: organizers willing to push the speaker on customization rather than asking for the boilerplate deck
• Wrong fit: pure motivational events. The talks are operating-engineer talks, not pep rallies
• Wrong fit: vendor-pitch slots where the brief is to extol a specific product. The talks are vendor-neutral; sponsor logos appear only as examples
• Wrong fit: events that want a guarantee of audience laughter. The talks are direct and specific; they earn the room through credibility, not stagecraft

How to Book

Booking is a short, structured sequence. The decision usually closes within 10 business days for events more than 6 weeks out.

• Step 1: send a one-page brief with audience profile, date, slot length, format, and topic preferences
• Step 2: 30 minute alignment call to confirm the talk concept and the slot fit
• Step 3: contract issued within 5 business days. Fee, scope, AV requirements, recording rights, cancellation terms
• Step 4: prep cadence. One kick-off, one mid-prep alignment, one tech-check the day before
• Step 5: deliver. On stage, recorded, and available for follow-up Q&A by attendees through the organizer channel

FAQ