Shortwave Rides the Tidal Wave: Inbox Agents, Hyper-Growth & Hiring AI Managers, with CEO Andrew Lee
Summary
Shortwave’s growth inflection came when its January V3 stopped behaving like an email chatbot and began completing open-ended work through repeated tool use. The earlier assistant could search or draft “okay” but was not trustworthy; Claude Sonnet 3.5’s October version could stay coherent across long sequences, leading Shortwave to let it run for up to roughly 20 calls, retry failed searches, absorb errors, and produce answers “that no single LLM call could have produced.” Andrew Lee’s definition is blunt: working agents are fundamentally about iteration.
The architecture got simpler and economically better because a stronger front-end agent could compensate for narrower retrieval primitives. Shortwave rebuilt virtually everything—BGE embeddings, Pinecone serverless, hybrid semantic plus keyword search, structured constraints, and the agent framework—while accepting a closely approximated result rather than scoring every email exactly. The result is “a lot cheaper,” faster, more reliable, and better at retrieval because the agent can reformulate searches instead of demanding perfection from one query.
Anthropic’s prompt caching is the load-bearing feature behind Shortwave’s positive-margin agent economics. Agent histories can reach hundreds of thousands of tokens and are repeatedly extended, but carefully keeping prior context immutable and checkpointing it makes subsequent calls roughly 90% cheaper; without that, Andrew says Shortwave would lose money on every user “by a huge margin.” OpenAI’s approximately 50% automatic discount is easier to use, but not large enough for this workload.
Shortwave is repositioning from “an email client with AI built in” to “an AI with email features built in.” The plan is to span Slack, LinkedIn, CRM and project-management systems, potentially becoming an agent-routing layer for all business communication. That expands the competitive frame from Gmail and Superhuman toward “the next version of ChatGPT,” while email supplies a uniquely rich corpus of correspondence, attachments, SaaS notifications, contacts and calendar history.
Demand is concentrating at the expensive end, suggesting substantial willingness to pay for context, compute and answer quality. Shortwave is now margin-positive, though not “hugely” so, and Andrew says essentially all growth is in the highest-priced plan because users value full-history indexing and larger context windows. He sees room for a roughly $200-per-month tier—and eventually costly jobs where hundreds of dollars of inference replace a month of employee work—because “the difference between good and best is worth a lot of money.”
Andrew now views speed, not accumulated code, as the moat that “probably” matters most. AI may sharply erode the value of an email client that took four years to build, so Shortwave intends to remain roughly two months ahead with about 15 people over the next year, organized as managers of AI agents rather than traditional executors. Coding starts with Cursor agents, design starts with a working Bolt.new prototype, and content can begin with GPT-4.5; the company has even changed personnel around that operating model. Shortwave’s introduction advertises a $10,000 referral bonus, although Andrew’s closing inconsistently says $1,000 before promising $10,000.
The largest unresolved risks are autonomy, trust and whether models can internalize user behavior without explicit instruction. Shortwave’s AI filters already act without approval, but running a full agent on every incoming message could turn roughly eight daily agent runs into 300, magnify costs, and expose prompt-injection paths such as “delete the full inbox.” Andrew favors constrained permissions, action histories, queued drafts and review flows, while expecting a future memory breakthrough beyond today’s explicit “remember this” facts.
Deep dive
1. V3 turned email from an interface into delegated work
Andrew’s retrospective is unsparing: a year earlier Shortwave could chat, search and draft “okay,” but users could not trust that it had found the right receipt. It could not archive cold sales mail, manage to-dos, understand contacts or labels, or fulfill the promise of “having an employee sitting next to you.”
The current product crosses that threshold because it can perform almost everything a person can do inside the inbox. Andrew’s description of the behavioral shift: users can “do my email not by doing my email, but by talking to a thing that then does my email for me,” leaving them to operate at a higher level.
The revenue chart’s defining kink maps to the January V3 launch. A September V2, built around limited multi-search behavior, produced some growth and excitement; V3 could tackle broad, open-ended requests and drove the visibly exponential curve that brought Nathan back for the conversation.
Nathan’s revealing test was to ask the agent to review his last 100 sent messages and offer advice. Its observations were apt enough to make him question whether the balance visible in his outbox matched how he aspired to spend his time—an analysis the underlying corpus always supported but no person would manually perform.
2. The inbox is an underused operating dataset
Andrew’s premise is that email contains “a crazy valuable corpus”: not only correspondence, but SaaS notifications, attachments, PDFs and calendar invitations spanning a business and personal life. A smart human with unlimited time could extract the same insights; AI changes the economics of asking the question at all.
After Shortwave released a controversial interface redesign, Andrew asked how many users had complained during the prior 24 hours. The agent found 19, summarized their top five objections, and gave him an immediate view of sentiment that would otherwise have required manually consolidating shared support threads.
A common morning workflow comes from sales users facing perhaps eight demos alongside a crowded inbox. Their prompt asks the agent to identify required tasks, rank their order and surface what matters for each call, turning scattered messages into a single operating plan rather than merely summarizing unread mail.
Document-heavy professions supply another repeatable wedge: real-estate agents, contractors and architects can ask for one payment term buried inside exchanged PDFs. One homeowner reconstructed years of furniture purchases from receipts and family correspondence; Nathan similarly gathered Uber, Lyft and DoorDash receipts into an itemized trip-expense report, saving about 30 minutes.
3. Users are discovering integrations that Shortwave never built
One user wanted to move extracted action items into Linear, despite Shortwave having no Linear integration. The model knew how to construct the destination product’s task-creation URLs, so it generated clickable links from the email thread and project base URL; each click created a task, with “no code being written—it’s just a prompt.”
Another user uploaded a text file containing a list of email addresses and requested a loop: search prior correspondence with each person, identify a specific personal fact, draft a tailored greeting and present every message for review. The agent executed the sequence, letting the sender approve and dispatch 20 messages in a mail merge that did not read like one.
Andrew emphasizes that even Shortwave’s team is still learning the product’s surface area from support requests and user demonstrations. The recurring pattern is not a narrow feature but “big prompts” that combine search, attachments, reasoning, repeated actions and human approval in ways neither the product team nor the user initially considered obvious.
4. Better agents allowed Shortwave to simplify retrieval
Virtually every layer changed within a year: the embedding model, vector database, search implementation, search API, frontier model and agent code were replaced. Andrew attributes the rewrites both to rapidly improving models and to the team repeatedly discovering that a new capability could unlock a simpler system design elsewhere.
Shortwave moved from Pinecone pods to Pinecone serverless, whose separation of storage and compute better fits an email corpus with heavy storage requirements. That cost structure also made larger vectors practical, enabling a larger BGE embedding model without making per-user economics untenable.
The new search path combines a Pinecone semantic query with constrained keyword or full-text search using metadata such as contact, label and date. It merges and scores the result sets, then post-filters them, exposing an API that effectively asks for “emails about this topic with these constraints” while avoiding the prior slow, brittle pipeline and reranking machinery.
Nathan’s pushback—worth keeping—was that post-filtering semantic results could miss the true best message. Andrew conceded the edge case: fully scoring every email under every filter would be intractable, so Shortwave uses an approximation that can theoretically miss an item but, across “the vast majority” of real queries, retrieves the best results quickly and cheaply.
5. Iteration, not a single brilliant call, is the core of agency
Shortwave’s original assistant made one final LLM call inside what Andrew calls a “complicated Rube Goldberg machine.” When GPT-4 first introduced tool calling roughly two Decembers earlier, using it degraded reasoning so badly that XML-formatted outputs and application-side execution worked better; multiple reliable calls were largely out of reach.
GPT-4o made limited repeated search viable the following summer, but it tended to drift after several calls. That powered the September V2 agent and worked somewhat better than one-shot retrieval, yet it did not deliver a qualitative change.
The breakthrough came after Andrew heard Bolt.new’s founders single out the October version of Claude Sonnet 3.5 and inspected the open portions of their implementation. That model could stay on track through long tool sequences, prompting a rewrite around a generic agent framework, stronger tools, one comprehensive prompt and as many as roughly 20 repeated calls.
Andrew’s operational definition of an agent is iteration: search, inspect, reformulate and search again; receive a malformed-query error and repair it; attempt a calendar invitation, see a conflict and adapt. The feedback loop lets the system “iterate your way to an answer that no single LLM call could have produced.”
6. Models know more about “done” than their lack of personal memory suggests
Nathan’s contact-prescription example captured the puzzle. He personally knows the answer exists and recognizes it when found; Shortwave similarly needed three or four searches, but a model does not begin with his autobiographical certainty. How, he asked, can it distinguish “not quite it yet” from the best evidence available?
Andrew’s first answer is that models often know the generic shape of success. Inbox organization required surprisingly little explanation of “low-quality email,” and Claude reliably recognizes cold sales messages from tone and structure even when the application has not supplied an explicit relationship history.
Shortwave also gives the agent tools for reconstructing personal context. Contact results include statistically inferred importance; the model can inspect recent exchanges, sent messages and calendar events, then infer that someone recently contacted or met is likely important rather than relying on a fixed user profile.
The acknowledged gap is behavioral learning. Shortwave does not yet turn accepted or rejected triage suggestions into personalization; users must explicitly say, for example, “never archive newsletters from this sender.” Nathan connected that opening to reinforcement learning from developer behavior, where actions and reactions become the learning signal rather than requiring written instructions.
7. One frontier model handles the agent, while smaller models specialize
Shortwave is not doctrinaire about a single provider. Autocomplete uses a fine-tuned GPT-4o mini because latency is paramount; one-button reply suggestions use Llama 3.2 3B; GPT-4o appears in a small number of other places; and open-source models run through Vertex on GCP where low cost and speed matter more than deep reasoning.
Inside the main agent, however, attempts to delegate retrieval evaluation or intermediate stages to cheaper models introduced pipeline complexity and impaired reasoning across activities. Feeding the entire evolving state to one strong model preserved relationships among messages, tools and constraints while allowing solutions the application designers had not anticipated.
Shortwave is testing Flash for summaries, quick replies and potentially filters, but model selection weighs task performance, latency, cost and caching—not benchmark intelligence alone. Andrew says that using different models inside the main agent can save money but has so far added complexity and reduced generality.
The pace itself is an operating burden: when DeepSeek launched, people asked within two days why Shortwave had not switched. Andrew’s answer is effectively that every candidate needs task-specific evaluation, yet releases arrive faster than the team can thoroughly test even a single prompt.
8. Prompt caching makes long-running agents financially viable
Agentic operation repeatedly resends an ever-growing history, sometimes reaching hundreds of thousands of tokens. Because each step appends to the same prior context, the naive architecture would pay full frontier-model rates again and again across perhaps 20 calls.
Anthropic’s explicit caching changes that equation. Shortwave carefully keeps earlier prompt segments immutable, checkpoints the conversation after each iteration and extends the cache forward; once implemented correctly, Andrew says it can reduce relevant costs by about 90%.
The counterfactual is stark: “If we didn’t have that, we could not afford to run”—Shortwave would lose money on every user by a huge margin from Anthropic charges alone. Organizing an inbox, its most common agent workflow, may require roughly 20 tool calls, so the discount compounds rapidly.
OpenAI’s cache is easier because the API applies it largely automatically, but Andrew characterizes its savings as only about half. He told OpenAI’s agent SDK team that caching was the room’s leading founder question: repeated-context economics are becoming central infrastructure, not an implementation detail.
9. Autonomy turns trust and prompt injection into product requirements
Andrew sees agent UX as largely unsolved. Autocomplete has a familiar Tab-to-accept interaction, but a system that disappears to perform substantial work raises harder questions: Is it sending mail? What changed? Which permissions and approvals provide confidence without forcing the user to supervise every move?
AI filters are Shortwave’s notable exception to human approval. Users write a prompt and configure actions such as applying a label or deleting a message; GPT-4o mini evaluates incoming mail automatically. The feature is popular, though support occasionally traces a “missing” email back to a user’s own overbroad filter.
The ambition is to replace that classifier with the full agent, enabling rules such as accepting a meeting only when the sender is an investor with more than three prior exchanges. Yet normal agent use might occur eight times daily, while evaluating every incoming message could mean 300 runs—a dramatic compute and cost increase.
Security is the second constraint: an adversarial email could instruct the agent to “delete the full inbox.” Andrew’s possible controls include limiting an agent to the message currently under review, maintaining an auditable action history, learning from reversals, or queuing actions—especially drafts—for a single morning review instead of sending autonomously.
10. Evaluation favors rapid learning over behavioral lock-in
Shortwave has consciously chosen adaptation speed over ensuring that nothing breaks. Andrew’s approximately 100 “golden” prompts live in a Notion document and are run manually, usually selecting the subset relevant to a changed tool or prompt rather than executing a comprehensive automated suite.
The tests ask whether output remains reasonable, not whether it exactly matches an old answer. When Shortwave added an unsubscribe tool, inbox organization began offering to unsubscribe from low-quality messages without explicit feature-specific engineering; a rigid expected-output test would have misclassified that improvement as a regression.
Major changes first ship as opt-in experiments to unusually adventurous users. Shortwave watches whether people leave them enabled: the unsubscribe capability retained roughly 99% of adopters over a week, which Andrew treated as strong evidence that its new tools and prompt changes were not broadly breaking workflows.
Reliability gets the same calculated treatment. Anthropic’s API goes down more often and has slower time to first byte than OpenAI’s, but Shortwave usually waits instead of falling back to a materially different agent. Andrew says they have not appeared to lose users because of those outages, so far.
11. Shortwave is becoming an AI with communication features
The conceptual reversal is explicit: last year Shortwave was “an email client with an AI built in”; now it is “an AI with email features built in.” Moving the agent to the left side of the interface signals that it is the primary product, with future access to Slack, LinkedIn and eventually CRM or project-management data.
This reframing makes cross-channel identity central. Nathan noted that email, texts with his wife, Slack planning and Twitter DMs each contain different facets of him; no single source supports a convincing “write as me” system. A unified communication agent could reason across those facets instead of treating email as the whole person.
Shortwave’s AI-created to-dos intentionally differ from Gmail labels. Labels are terse, durable classifications that should remain searchable years later; a to-do can be a full-sentence, short-lived project containing notes, documents and five related threads—such as all material needed to prepare an interview with Andrew.
Those to-dos do not sync back into Gmail because Gmail lacks an equivalent concept, and Andrew wants users able to leave without finding their underlying mailbox distorted. He contrasts this with Superhuman features that generate extra emails: Shortwave aims to let users experiment freely while preserving Gmail as a clean fallback and source of reassurance.
12. “Write as me” works by retrieving examples, not cloning a voice
Andrew’s strongest technical conviction is that putting relevant examples into Claude’s context produces better personalized writing than per-user fine-tuning. He knows that sounds less technologically sophisticated, but says the context approach solves the thing users actually value: correctness, not merely prose that superficially resembles them.
If someone requests a payment link or proposes a meeting time, style is secondary to retrieving the exact link or availability previously used. Framing examples as “the last time you discussed this topic, this is what you said” lets the model reproduce concrete facts, times and conventions rather than hallucinating something merely plausible in the user’s voice.
Fine-tuning also creates model-migration debt. Shortwave changes providers and versions constantly, while the newest frontier model may not initially support tuning; retraining every user whenever the base model changes would make remaining on the leading edge operationally prohibitive.
Andrew expects perhaps 20 well-selected examples, a strong prompt and generic reinforcement fine-tuning that teaches a model how to use examples for fact and style matching to be the right combination. The reinforcement component would apply across users rather than encoding each person into separate weights.
13. Memory remains an open frontier rather than a solved feature
Nathan sees a “missing middle” between context windows, knowledge embedded in weights and database retrieval. He pointed to the Titans paper’s runtime-updated neural memory and HippoRAG’s extensible concept graph as possible directions, while acknowledging that stronger models plus better search might eventually make a separate mechanism unnecessary.
Andrew’s honest non-answer is that his “Spidey Sense” expects a major breakthrough—something lighter than full fine-tuning yet more intrinsic than retrieval—but he cannot say what it will look like. He had not read Titans and did not pretend otherwise.
Today’s product has a Memories tool that lets the LLM maintain explicit facts inserted into every prompt: always CC an executive assistant, default meetings to 45 minutes, or never archive a particular sender. It is effective for behavioral customization but only after the user deliberately says “remember this.”
A searchable fact database could approximate more of the desired behavior, especially when an agent decides which memories apply. It would still fall short of Nathan’s vision of continuously updated, active memory that knows what has been tried, recognizes the sought answer and learns silently from ongoing collaboration.
14. AI-generated spam has been less disruptive than feared
Contrary to their discussion a year earlier, neither Andrew nor Shortwave’s users report a major surge in AI spam. Support receives playful generated poems and jokes in response to newsletters, but ordinary Gmail filtering may be containing abuse—or the predicted wave simply has not materialized.
Andrew more often sees cold outreach that is obviously human and would have improved with AI. A significant fraction of his own messages are AI-enhanced or fully generated, yet people he later asks generally did not notice, which he considers acceptable: “If I can’t tell,” the distinction may not matter.
Nathan’s pushback was philosophical rather than technical: perhaps he is too precious about stylistic flourishes other people neither notice nor value. He still separates routine from non-routine communication; Andrew likewise identifies routine work as the immediate win, citing days when he sends around 20 variations of the same UI-explanation email.
15. The fundraise finances an agent-first company, not a larger old-style team
Shortwave raised an undisclosed amount from unnamed participants—enough to expand—but the strategic change matters more than the number. Its fastest-growing and largest plan is the most expensive because users are paying for more history, context and Anthropic compute, convincing investors that the opportunity extends beyond a better inbox.
Andrew now describes the competitive target as “the next version of ChatGPT” rather than the next Gmail. ChatGPT, Claude and Perplexity answer questions and perform research, but he sees an opening for an agent that can actually execute work through the systems where business communication arrives.
Engineering roles are being redesigned around supervising AI. A customer bug report can often be pasted verbatim into Cursor agent mode and fixed in one shot; the scarce skill shifts from writing localized code toward understanding the business problem, mapping components and framing work so an “AI intern” can execute it.
Design now begins with a working Bolt.new prototype rather than a whiteboard, wireframe and mockup sequence, allowing bad ideas to die faster. GPT-4.5 drafted job postings, and Andrew imagines one person managing the blog, social media, changelog, documentation and website—part of shrinking the envisioned organization from roughly 50 people to 15 or 20.
16. Speed is replacing accumulated software as the moat
A year earlier Andrew told investors Shortwave’s moat was the email client itself: it had taken four years to build, so an AI competitor would first need to reproduce that foundation. AI-assisted development now erodes that argument because the next team may rebuild equivalent software far faster, reducing the defensive value of existing code.
His replacement thesis is categorical but hedged: “The moat that matters—the only moat that probably matters—is speed.” Shortwave plans to optimize for approximately 15 tightly coordinated people over the next year and remain perhaps two months ahead, using team size and talent density to avoid the consensus overhead that slows larger organizations.
The operating model already changed personnel. Andrew says Shortwave let go of a couple of talented employees with strong attitudes because their strengths and enthusiasm did not fit the new agent-managed environment—a decision he presents as part of rebuilding financing, daily operations and team composition ahead of a “tidal wave.”
The company is now margin-positive, though not by much: a substantial share of each dollar still goes directly to LLMs and traditional email infrastructure. Andrew cites Cursor and Midjourney, each at roughly 20 people, as evidence that historically improbable product scope can now coexist with very small teams.
17. More compute could support a much more expensive product
Customers overwhelmingly choose Shortwave’s top tier because it supplies the largest context window and indexes their full history. Even if the business plan produces almost-as-good answers, Andrew says people pay for the best—mirroring his own view that ChatGPT Pro at $200 per month is “too low.”
A future Shortwave tier around $200 per month therefore seems plausible, though no launch was announced. The most obvious premium capability is running the complete agent on every incoming email, which Andrew estimates could require roughly 100 times more compute than today’s selective invocation.
Reasoning models offer another spend path. A request to analyze every customer report from the previous year might otherwise occupy an employee for a month; in that case, a user could rationally spend hundreds of dollars of inference on one answer despite slower, more expensive models.
Nathan raised Sam Altman’s speculation about $2,000- or $20,000-per-month AI plans. Andrew has no immediate plan at those levels, but sees “sort of no limit” when customers are explicitly asking Shortwave to purchase GPU work on their behalf and the generated value exceeds the computation cost.
18. One large agent beats internal handoffs, but may route outside agents
Shortwave could evolve into a communication-routing layer: receive events from many inboxes, decide which external agent should handle each, then return or act on the result in a shared interface where humans and AI collaborate. Andrew likens the possibility to a Zapier-style system with a first-class human UI.
Inside the product, however, experiments with specialized agents, changing system prompts and model handoffs were brittle. They struggled to reason across task boundaries; the better result was generally to use “the biggest, most expensive model you possibly can,” supply all relevant instructions and tools, and exploit caching to control cost.
Memories illustrate the advantage. Instructions formerly appeared only when a writing-specific tool was called; putting them in the master prompt means a rule such as “always address Nathan Labenz as Sir” can affect email drafting, calendar scheduling or any other operation at the correct moment.
Nathan noted that OpenAI had just emphasized agent handoffs. Andrew had challenged that team directly the prior day and retained his hot take: “I don’t think handoffs are going to take off.” Isolation may aid testing or sandboxing, but most real tasks require an agent to reason across all capabilities rather than inherit organizational silos.
19. Organizational structure may decide which incumbents can adapt
Andrew sees Google as the proof case: its new Gemini 2.5 model is “literally mind-blowing,” yet Gmail’s product use of those models lags. Moving an agent sidebar from right to left could require two years, sign-offs, “a thousand meetings” and cross-team buy-in; Shortwave debated the product merits and shipped the change within weeks.
The same speed argument underlies Shortwave’s core-team-in-person approach in San Francisco. Andrew acknowledges that remote work expands the talent pool and that the company was once fully distributed, but believes rapid redirection, hard feedback and emotionally difficult product debates happen more easily around a whiteboard.
He also frames the policy as founder fit rather than universal truth. Andrew and co-founder Jonny work and lead better in person; lessons from their earlier Firebase experience convinced them to design the company around those strengths, even at the cost of excluding exceptional people elsewhere.
Nathan proposed an “AI scout” who systematically tests every model, framework and product. Andrew has no posted role yet, but agrees the need is real: discovering Claude Sonnet 3.5 through one podcast may have accelerated Shortwave by months, and relying on luck is increasingly dangerous as situational awareness becomes impossible for one founder.
20. Hiring signals AI curiosity more reliably through demonstrations
Shortwave is not limiting itself to staff-level candidates. Andrew says updated postings include customer success, a content creator focused partly on educational video, and a junior product-engineering position alongside senior roles.
Take-home exercises have become too easy to game with AI, so the application now asks for a five-minute video showing something the candidate built or accomplished. The strongest signal is a useful, creative AI workflow Andrew has not considered—evidence that the applicant is already experimenting at the frontier rather than merely claiming enthusiasm.
Passion is a threshold because keeping current requires sustained curiosity. Andrew’s desired employee is “super forward-thinking with AI,” able to discover new uses and convert them into results; simply staying abreast of the field is itself impressive given the release cadence.
The referral number is internally inconsistent in Andrew’s closing sentence: he first says “a $1,000 referral bonus,” then promises, “we will give you $10,000, no joke.” The episode’s introduction also describes the offer as $10,000.
21. Software engineers may become more leveraged and less numerous at once
Andrew’s co-founder and CTO oscillates between “I am obsolete” and “I am a God and I can do so much.” That tension captures the labor market: AI is rapidly raising each engineer’s output while automating the localized implementation work that once justified much of engineering headcount.
Front-end tasks such as creating a button with a specified appearance are increasingly routine for models. Andrew cites Dario Amodei as predicting that within roughly two years AI might write 90%, perhaps 100%, of code, though Andrew explicitly says he is not sure he believes the full claim.
Senior engineering remains differentiated by understanding users, the business problem and interactions among system components—not by producing dramatically better UI code than a junior. People strongest at execution will need to acquire those framing and architectural skills; those already capable of them become much more powerful.
Nathan worried that even ten times more software might not sustain current developer headcount and suggested beginning to contemplate universal basic income. Andrew remains optimistic: if a product that once required 20 people can be built by three, many previously uneconomic niche companies may appear, potentially absorbing talent even as each team shrinks.
22. Agentic post-training, cheaper inference and native voice are the next unlocks
Andrew’s number-one technical watch item is post-training specifically for tool use and agentic behavior. The jump from every earlier model to Claude Sonnet 3.5 enabled Shortwave’s current product; Claude Sonnet 3.7 improved further, but gaps remain and he wants credible competition from OpenAI and other providers.
Stronger iteration can route around many weaknesses because the model can inspect errors, search differently and devise unexpected solutions. Andrew’s phrase is “the sky’s the limit,” conditional on models becoming better at sustained tool calling rather than merely improving isolated reasoning scores.
Production economics remain equally decisive: lower cost, latency and failure rates would let Shortwave inspect more search results and run a full agent on every message. Capabilities already imaginable remain impractical chiefly because applying frontier inference hundreds of times per user per day is too expensive.
Native multimodal voice is the experiential unlock. Shortwave has voice input today, but Andrew calls it merely “fine”; a model that can converse naturally by voice while executing the complete agent workflow would be “totally killer,” and Nathan values the prospect of doing more work away from his desk.