Andrew Lee

Tasklet is betting that the agent transition leaves only three viable software archetypes: a few horizontal platforms, API-first companies, and outcome-selling solutions firms. Its ambition is to become “the AI agent platform that replaces your SaaS products for knowledge workers,” while Stripe-like infrastructure survives behind APIs and Nathan cited Fin’s model of $0.99 per customer-service ticket resolved as an example of selling outcomes rather than visible software.

Tasklet’s greatest supplier is also its clearest competitive threat. Andrew Lee says roughly 80% of departing users move to an Anthropic product, usually because they already pay for Claude Max; Nathan and Lee guess that Max may deliver “five to one or maybe even more” tokens per dollar than Tasklet can buy through the API. That subsidy distorts customer expectations and keeps Tasklet near razor-thin margins. Separately, Opus 4.7’s roughly 30% higher tokenizer-driven cost helped make it an optional model rather than the default.

The six-month rewrite replaced chat history with a cache-aware file-system architecture built for agents that may run 10,000 times. Tasklet now stores the complete history in files and sends the model hints plus a fixed-length, decreasing-fidelity summary: recent turns retain tool calls and thinking, while older material progressively loses responses, arguments, and detail before reaching LLM summarization. “What if the files are the agent?” is the central design call; the remaining weakness is that agents still sometimes forget and compaction still costs heavily.

“Always bet on the models” held up, but Tasklet is shifting from Claude maximalism to model-neutral orchestration as credible alternatives arrive. Claude 4.5 unlocked better computer use, Opus’s price move from $15 to $5 widened access, and 4.6 improved computer use and code generation enough to support Instant Apps. Lee says GPT-4.5 has become very good for Tasklet’s use case, can navigate its harness well, and gives Opus 4.6 “a run for its money”; Tasklet has signed a deal with OpenAI and expects Anthropic, OpenAI, Google, and open-source models to offer useful cost-performance choices. Kimi and DeepSeek are among the models it has tested.

Tasklet’s defensible wedge is not exclusive capability but the ergonomics and governance of persistent corporate automation. Lee concedes that “everyone is building the same thing” and that almost any general agent can code or perform knowledge work; Tasklet instead optimizes for cloud-hosted, 24/7 workflows with shared ownership, audit logs, guardrails, cost controls, and memory across thousands of triggers. Its enterprise pitch is therefore, “A bet on us is a bet on everybody,” with Tasklet serving as a neutral model and cost arbiter.

Generative UI is arriving fast enough to erase large portions of application-layer differentiation. Tasklet’s March Instant Apps release can generate connected dashboards or even a functioning email interface from one prompt; internally, the team now asks Tasklet for interactive pricing-analysis tools instead of building them in BigQuery or conventional dashboard software. Lee consequently thinks “Salesforce is in real trouble”: agents make schemas easier to recreate, data easier to move, and accumulated application code less valuable, though he expects a smaller Salesforce rather than extinction.

Better models do not eliminate the commercial value of the harness; they move that value toward cost, reliability, permissions, and reversible execution. Lee prefers “mecha suit” to harness: storage, compute, APIs, persistent context, and oversight multiply the model’s usefulness even if the intelligence advantage lasts only six months. The roadmap includes the ability to “roll back the world,” require approval only for consequential actions, and generate testable migration scripts rather than passing records through an LLM; meanwhile, Tasklet’s own internal token spend is estimated at 5–10% of payroll.

🔗 Original source & video: Three Kinds of Software Survive: Tasklet’s Andrew Lee on Competing to be a Horizontal Platform

Tasklet’s core bet is that model-led agents will overtake deterministic workflows because agents can route around the real-world exceptions that break flowcharts. Andrew Lee concedes that today’s models are “probably” somewhat less reliable for many business applications, but predicts that gap disappears within six months and eventually reverses: “Rather than having software wrapping LLMs, you have LLMs wrapping software.”

The product is evolving from recurring automation into a persistent, language-native virtual employee. A high-level agent retains responsibilities and feedback while sub-agents execute individual runs; users can also return for ad hoc work, and most messages already fall into that category even though nearly every paying user has an automation. Some customers name agents, give them dedicated email accounts, and treat them as colleagues: “This is Joe, my EA.”

Lee still selects models “on vibes,” with Anthropic decisively preferred for long tool-using sequences despite GPT-5’s stronger published task-length result and lower price. His claim is not that Sonnet always gives the best first answer, but that small advantages compound across 100 iterations; the fact that GPT-5 costs less than half as much without triggering broad switching is his market-based evidence that “the real-world utility” of Sonnet is higher.

Tasklet’s distribution wedge is universal connectivity rather than a curated handful of integrations. It combines 3,000-plus integrations and business tools, arbitrary APIs, MCP servers, and computer use; one paying customer even replaced Notion’s official MCP with Tasklet’s model-generated direct API connection because it worked better. Lee’s emerging MCP view is blunt: if tools merely mirror documented endpoints and models can discover those endpoints themselves, “what’s the point of MCP?”

Long-lived agents make context engineering and compute infrastructure—not the basic agent loop—the consequential technical work. Tasklet replaced an always-present mutable JSON memory that became unwieldy after a week or two with an agent-managed SQL database, while pursuing compaction, selective retrieval, and eventual “uncompacting” of old history. The north star is an economically impossible but experientially valuable illusion: “one big long chat” in which everything remains available and intelligently considered.

The economics are early-stage and unfavorable, but Lee sees familiar cost curves and a near-term Haiku 4.5 lever. Shortwave progressed from launches that might have bankrupted the company to healthy, though sub-90%, margins; Tasklet is currently “strongly margin negative,” consumes far more tokens, and must police zombie automations among free users. Haiku 4.5 costs roughly one-third as much as Sonnet, potentially allowing much larger quotas without equivalent margin damage.

The strategic endgame is a trusted horizontal agent platform, but Lee believes speed is the only present moat. He expects general-purpose models and generated interfaces to eliminate much vertical SaaS—eventually even Shortwave in its current form—while direct APIs and computer use erase connector libraries accumulated over years. Tasklet’s launch was adding revenue much faster per unit time than Shortwave ever had, yet the durable enterprise opportunity depends on becoming “the most trusted way” to deploy agents, with permissions, auditability, compliance, and potentially insurance.

🔗 Original source & video: How Tasklet Puts the Agency in Agents, with CEO Andrew Lee

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.

🔗 Original source & video: Shortwave Rides the Tidal Wave: Inbox Agents, Hyper-Growth & Hiring AI Managers, with CEO Andrew Lee