The Accounting Technology Lab

About The Accounting Technology Lab

In-depth, honest accounting software and technology reviews capturing the real-life experiences of using particular products and solutions - presented by CPA Practice Advisor and technology experts Randy Johnston and Brian Tankersley, CPA.

Publisher

Brian Tankersley & Randy Johnston

Category

business · technology

Language

en

Explicit

No

First episode

29 Dec 2023

Latest episode

2 Oct 2026

Latest episodes

44 episodes in the feed.

1. 

   2 Oct 2026

   ATL277: Tech Mergers & Acquisitions

   The technology M&A market is sending some unusually clear signals about where the industry is headed. In ATL277, Randy Johnston and Brian Tankersley examine a wave of acquisitions involving AI, semiconductors, data platforms, streaming, automation, and accounting technology—and what those deals may mean for accounting professionals. The discussion ranges from Lattice Semiconductor's acquisition of AMI and onsemi's proposed Synaptics deal to Salesforce's acquisition of Fin, Progress Software's Domo transaction, Bending Spoons' Airtable acquisition, Sage's purchase of Bangert, NVIDIA's proposed acquisition of Hugging Face, and SpaceX's acquisition of Cursor. Stripe's agreement to acquire OpenRouter gets particular attention because AI routing could become an important layer of tomorrow's technology stack.  20261002 Tech Mergers and Acqui… But the episode isn't really about dealmaking. It's about technology strategy and vendor risk. When a product your firm depends upon is acquired, its pricing, roadmap, integrations, support, privacy practices, or even its continued existence can change. Brian's advice: treat an acquisition as a trigger to revisit your contingency plan.  20261002 Tech Mergers and Acqui… Why This Episode Matters The common thread running through these deals is control of strategic technology layers. Stripe's OpenRouter agreement provides access to a gateway spanning 400+ AI models. Stripe (https://stripe.com/newsroom/news/stripe-agrees-to-acquire-openrouter?utm_source=chatgpt.com) Salesforce paid approximately $3.6 billion for Fin and completed the transaction September 10, the day before ATL277 was recorded. Salesforce Investor Relations (https://investor.salesforce.com/news/news-details/2026/Salesforce-Signs-Definitive-Agreement-to-Acquire-Fin/default.aspx?utm_source=chatgpt.com) NVIDIA announced its agreement to acquire Hugging Face for approximately $12.9 billion just eight days before the recording. NVIDIA Blog (https://blogs.nvidia.com/blog/nvidia-to-acquire-hugging-face/?utm_source=chatgpt.com) For accounting firms, that means vendor due diligence cannot stop when the software contract is signed. Ownership changes are technology-risk events. Highlights: Technology consolidation is accelerating. Randy argues that M&A activity can help identify longer-term technology trends.  20261002 Tech Mergers and Acqui… AI infrastructure is becoming strategic. Stripe's OpenRouter agreement highlights the growing importance of model routing and token economics. Stripe (https://stripe.com/newsroom/news/stripe-agrees-to-acquire-openrouter?utm_source=chatgpt.com) AI agents are becoming acquisition targets. Salesforce agreed to pay approximately $3.6 billion for Fin, formerly Intercom. Salesforce Investor Relations (https://investor.salesforce.com/news/news-details/2026/Salesforce-Signs-Definitive-Agreement-to-Acquire-Fin/default.aspx?utm_source=chatgpt.com) Hardware and edge AI matter too. Lattice acquired AMI, while onsemi agreed to acquire Synaptics in a transaction valued at approximately $7 billion. Lattice Semiconductor (https://latticesemi.gcs-web.com/news-releases/news-release-details/lattice-semiconductor-completes-acquisition-ami?utm_source=chatgpt.com) Data is part of the acquisition thesis. FOX's proposed $22 billion Roku acquisition combines content with a platform reaching more than 100 million streaming households and significant first-party data. SEC (https://www.sec.gov/Archives/edgar/data/1428439/000119312526270288/d151410d425.htm?utm_source=chatgpt.com) Accounting-adjacent M&A continues. Sage acquired longtime partner Bangert, bringing its construction implementation expertise and AskRichard AI-enabled onboarding platform into Sage. Sage (https://www.sage.com/investors/investor-downloads/press-releases/2026/08/sage-acquires-bangert/?utm_source=chatgpt.com) The real lesson is contingency planning. Brian recommends reassessing alternatives whenever a critical vendor or product is acquired. Accounting Technology Lab Episode ATL277 Wiki Page:  https://wiki.cpate.ch/index.php/ATL277 Title: Technology Mergers and Acquisitions Publication Date: October 2, 2026 Recorded: September 11, 2026 Hosts: Randy Johnston and Brian F. Tankersley, CPA.CITP, CGMA Presented by: CPA Practice Advisor

2. 

   25 Sept 2026

   ATL276: The Secrets Agents Keep (Guest: Alexis Kingsbury, author "Accrual Intentions")

   In ATL276, Randy Johnston and Brian Tankersley sit down with Alexis Kingsbury, author of Accrual Intentions, to unpack what he learned from building a deliberately extreme experiment: an accountancy practice staffed by eleven AI agents with their own roles, personalities, and responsibilities. Kingsbury explains that the project began as part parody and part management-science experiment, but quickly became a practical test of delegation, workflow design, controls, and human judgment. The central lesson is not that AI is either brilliant or useless. It is that AI can perform impressively on difficult tasks and still make simple, checkable mistakes. Kingsbury argues that firms should separate probabilistic AI work from deterministic processes, document workflows, insert stage gates, and decide explicitly where human review belongs. He warns leaders not to confuse delegating the work with delegating the thinking. The discussion also covers token economics, local models, model portability, and the risk of locking organizational knowledge inside one AI vendor's project environment. Kingsbury recommends keeping core context, processes, and organizational knowledge in systems the firm controls, then connecting AI tools to that context. His closing advice: do not wait for perfect AI, and do not attempt a giant transformation. Pick a painful, valuable problem, solve it deeply, learn, and expand. Key Takeaways AI can be brilliant and stupid within the same workflow. Kingsbury describes Claude completing sophisticated work correctly and then altering an API-provided link enough to break it. Delegate execution carefully; do not accidentally delegate judgment. If AI takes over production, humans need to make the thinking, objectives, assumptions, and review gates more explicit. Deterministic controls matter. Where possible, use AI to create repeatable calculations, scripts, tests, templates, checklists, and processes. Review capacity has to scale with AI production capacity. Eleven virtual workers can generate an enormous volume of output, and that output still requires testing, prioritization, and accountable review. Your organizational knowledge should not belong to your AI vendor. Portability matters when prices, models, jurisdictions, or vendor strategies change. Token efficiency can become an economic issue quickly. AI consumption may require active optimization as usage scales. Waiting for “perfect AI” is not a strategy. Context, processes, guardrails, and review will remain necessary. Avoid the giant AI transformation project. Start with one sufficiently painful or valuable business problem, solve it well, and expand. Short Promotional Copy One-Sentence Promo What happens when you give eleven AI agents jobs, personalities, responsibilities—and enough autonomy to expose everything that can go brilliantly right and spectacularly wrong? Three-Sentence Promo Alexis Kingsbury built an accountancy practice staffed by eleven AI agents and turned the experiment into Accrual Intentions. In ATL276, he joins Randy Johnston and Brian Tankersley to discuss what the experiment revealed about AI errors, delegation, controls, token economics, organizational knowledge, and human judgment. The big lesson: AI can dramatically expand what a firm can do, but only if governance and review expand with it. Promotional Paragraph Your AI agent just completed the sophisticated analysis, updated the documentation, called the right tools—and then broke the link it was supposed to give you. That kind of contradiction is at the center of ATL276: The Secrets Agents Keep. Alexis Kingsbury joins Randy Johnston and Brian Tankersley to explain what he learned building the AI-staffed accounting experiment behind Accrual Intentions. The conversation moves beyond “AI good” versus “AI bad” and gets into the management problem: how do you provide context, separate thinking from doing, design deterministic controls, scale review, manage token costs, and keep your intellectual property portable instead of trapping it inside one AI provider? Timestamped Pull Quotes TimeSpeakerPull QuotePromotional Angle 05:03 | Alexis Kingsbury | “It'll just be easier if I do it myself.” | AI vs. delegation 12:40 | Alexis Kingsbury | “If you want a really good decision made, you don't want two people who think the same.” | Diversity of perspective 26:08 | Alexis Kingsbury | “There's value, but also risk… maximize value and mitigate the risk.” | Governance 34:28 | Alexis Kingsbury | “If you can get rid of the things that we don't enjoy… you get more time on the things that do add value.” | Human value 38:33 | Alexis Kingsbury | “If you are delegating the doing, you have to pull out the thinking and do the thinking up front.” | Management 47:03 | Alexis Kingsbury | “I've been able to improve token efficiency by 4,000x.” | AI economics 50:48 | Alexis Kingsbury | “One other big mistake I would suggest people avoid is giving the keys away to big AI firms.” | Vendor lock-in 53:15 | Alexis Kingsbury | “The technology is already good enough. It's just that you need all the guardrails and the context.” | Implementation 53:15 | Alexis Kingsbury | “Pick something in your business that is either incredibly frustrating and painful… or is a current missed opportunity for value.” | Where to start Production note: Timestamps are based on the supplied transcript/SRT. Recheck them against the final edited episode before cutting promotional video. Best Short-Video Pulls The brilliant AI that breaks the easy thing — approximately 22:55–26:40 Discussion of deterministic versus probabilistic work, culminating in Alexis's story about AI completing difficult work and then altering a valid link. Delegate the doing, not the thinking — approximately 38:30–45:00 Alexis explains why removing the manual production step means leaders must intentionally preserve the thinking step. Tokens, local AI, and vendor economics — approximately 47:00–52:30 Discussion of AI subsidies, token efficiency, local models, and changing provider economics. Don't give away the keys — approximately 50:45–53:10 Governance discussion about keeping organizational context outside proprietary AI workspaces and making it accessible through tools such as MCP. How accounting firms should start — approximately 53:15–56:30 Don't wait for perfect AI, don't start with an enterprise-wide transformation, and don't waste time on trivial experiments. Pick a valuable problem. Suggested Show Notes Why Alexis created an accountancy practice staffed by eleven AI agents. Why the experiment began as both parody and serious management research. Custom GPTs and the early evolution of the virtual accounting team. Giving agents different roles, personalities, backgrounds, and perspectives. Why multiple viewpoints can improve AI-assisted decision-making. Moving from Custom GPTs to Claude Code, Claude Cowork, Codex, and agentic workflows. Why LLMs sometimes excel at complicated work but fail at simple tasks. Probabilistic AI versus deterministic calculations and processes. Using AI to build conventional software, scripts, templates, and controls. Why “I checked i...

3. 

   18 Sept 2026

   ATL275: What Tokens Are You Smokin'? AI Routers and Token Marketplaces

   Episode Summary AI may feel like a flat monthly subscription today, but ATL275 argues that the economics are moving toward a metered, multi-model market. Randy Johnston and Brian Tankersley examine AI routers, token marketplaces, and agent orchestration—tools designed to let firms choose different models for different jobs rather than committing every task to one vendor. Brian explains why OpenRouter and LibreChat can make model choice resemble buying fuel: use the amount of compute you need in the engine best suited to the work. Randy connects that concept to a three-layer AI strategy spanning general productivity tools, AI embedded in accounting applications, and specialized agents. The hosts also discuss the difficulty of forecasting token budgets, the likelihood of more usage-based pricing, and a growing field of gateways including OpenRouter, Ramp Router, Factory Router, Portkey, LiteLLM, Cloudflare, Kong, AWS, Microsoft, and Google. For accounting firms, the practical issue is not merely cost. Model selection, privacy, compliance, governance, and vendor concentration all matter when agents handle sensitive workflows. The takeaway: expect AI procurement to look less like buying one software subscription and more like managing a portfolio of compute suppliers—optimizing each workload for capability, cost, risk, and control. Key Takeaways Every AI workload consumes compute. Whether the customer sees a monthly subscription or an API invoice, somebody is paying for the tokens behind the transaction. One model does not necessarily fit every workload. AI routers can give organizations access to different models without separately funding and administering every provider. Cost optimization will become part of AI governance. Firms may increasingly route simple work to inexpensive models and reserve expensive frontier models for jobs that justify the added capability. Per-seat pricing may not be the final economic model. As agents become more common, usage-based pricing could become increasingly important. Accounting firms need more than cheap tokens. Privacy, contractual protection, data handling, auditability, regulatory compliance, security, and vendor stability remain critical. AI gateways are becoming strategic infrastructure. OpenRouter, hyperscale cloud platforms, and newer routing competitors are competing to become the transaction layer between applications and AI models. “Bring your own tokens” may become commonplace. Software vendors can increasingly let customers supply their own API credentials and select the AI models used inside applications. Token measurement should eventually resemble cost accounting. Firms need to understand the cost of AI by workflow, agent, client, and business process—not merely the organization’s total monthly AI bill.

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