THE PARKER EXPERIMENT
AI, operations, and the business of building things — tested in public, reported honestly.
Issue #3 | June 29, 2026
LAYER 1 — THE PULSE
Every frontier model just got delayed. The smartest operators are calling that good news.
GPT 5.6. Gemini 3.5 Pro. Sonnet 5. All three were delayed this week, tangled up in the same regulatory mess that’s kept Fable 5 offline since the shutdown I covered two issues back. One policy advisor put it plainly: the entire AI industry is effectively frozen on new public releases until the government sorts out what it actually wants.
Normally that’s the kind of headline that makes people anxious. This week, the better operators in my feed reframed it as something else entirely: a forced pause, and a rare chance to close what one host called the capability overhang, the gap between what your current AI tools can already do and what you’re actually getting out of them.
That reframe connects directly to three other things I read this week. A report on something called botsitting. A KPMG survey on why some companies get triple the ROI from AI. And a 1990s video game founder’s framework for why almost-great is more dangerous than failing outright. They’re all the same lesson wearing different clothes, and that’s this week’s feature.
LAYER 2 — THE FEATURE
Your AI Problem Isn’t the Model. It’s Everything Standing Next To It.
Let me give you three data points from this week’s reading before I tell you why they’re the same finding.
First: a new WorkAI Index report found that 87% of workers now use AI and save 11 hours a week doing it. But they also spend 6.4 of those hours on what the report calls botsitting, feeding the AI context, checking its output, and fixing its mistakes. Only 13% of organizations say they’re actually performing better because of it.
Second: KPMG’s quarterly AI pulse survey found that when the CEO personally owns AI strategy, companies are three times more likely to report real ROI. Fifty-seven percent report meaningful business value when the CEO is accountable for it, versus twenty-one percent when they’re not.
Third: Mark Pincus, who built Zynga into a seven-billion-dollar company after a decade of very public failure, talks about what he calls the B-plus trap. His line: B-plus is good enough to keep going and not good enough to win. The danger zone isn’t failure. It’s getting just good enough that you stop pushing.
Three different sources. One finding.
None of these are about which model you bought. They’re about what happens around the model after you buy it. The botsitting research shows that raw time savings evaporate without a redesigned workflow. The KPMG data shows that ROI tracks leadership ownership, not tool sophistication. And Pincus’s B-plus trap explains why most companies stall out exactly there: they roll out an AI tool, get a noticeable but unremarkable improvement, and stop because unremarkable doesn’t feel like failure. It feels like progress.
“B-plus is good enough to keep going and it’s not good enough to win.”
— Mark Pincus, Zynga founder
I’ve watched this pattern for 25 years, long before AI was the technology in question. Six Sigma rollouts that produced a B-plus result and then quietly stopped getting attention. Digital transformation initiatives that hit “good enough” and lost executive sponsorship. The tool changes. The trap doesn’t.
What the capability overhang actually means for you this week.
With the frontier labs stuck in regulatory limbo and no flashy new model dropping to chase, this is a genuinely good week to stop shopping for AI and start auditing your use of what you already have. Three moves, in order:
Find your botsitting hours. Pick one AI-assisted workflow in your business and honestly track how much time goes into feeding it context and fixing its output versus how much time it actually saves. If the ratio is bad, the fix is almost never a better model. It’s a better process around the model.
Take personal ownership of one AI decision this month. You don’t need to run every prompt yourself, but the KPMG data is blunt: when leadership treats AI as a real strategic question instead of delegating it entirely, ROI triples. Pick one workflow and own the outcome personally.
Ask whether you’re sitting in your own B-plus trap. If an AI rollout in your business produced a modest, real improvement six months ago and nobody has touched it since, that’s the trap. Modest and stalled looks identical to modest and still improving until you check.
None of this requires Sonnet 5 or Gemini 3.5 Pro or whatever ships once Washington and Anthropic sort out their standoff. It requires looking honestly at what Claude 5.5 or Opus 4.8 is already capable of in your business and closing the gap between that ceiling and where you’re actually operating.
AI PORTFOLIO WARS — WEEK 4 UPDATE
Four weeks in. Here’s where both portfolios stand heading into the model-release freeze.
Claude’s portfolio has held a consistent lead through three straight weeks of macro noise, Fable 5 fallout, SpaceX’s IPO chop, and a memory-pricing shock that’s rippling through every AI infrastructure stock. The real test starts now: with new model releases frozen industry-wide, both AIs are reasoning with the same tools they had last week. Whoever adapts strategy without a capability upgrade wins this stretch. Catch-up on how it’s going here.
LAYER 3 — THE SIGNAL
Quick hits from this week’s podcasts
• Mark Pincus’s Proven Better New framework: copy what’s already proven, find one validated improvement, then layer in exactly one new idea, and treat that new idea as an experiment expected to fail. Most AI pilot programs would benefit from this discipline instead of trying to reinvent the whole workflow at once. [Success Story, June 25]
• Anthropic says 65% of its own product team’s code now comes from Claude embedded directly in Slack. Whatever you think of the vendor lock-in questions that raises, the underlying lesson holds: AI adoption jumps when the tool meets people in a workflow they already use, not a new app they have to remember to open. [AI Daily Brief, June 24]
• KR Sridhar of Bloom Energy, on why he doesn’t buy the AI bubble narrative: “For the first time in human history, we are manufacturing intelligence. When was the last time any civilization said, ‘we have too much intelligence, let’s stop’?” Worth sitting with regardless of where you land on the capex debate. [20VC, June 29]
• A Databricks co-founder built a feature that caps what an AI agent is allowed to spend mid-task, after one agent burned $500 reading log files on a debugging job. If you’re running agentic workflows without a spend ceiling, that’s a gap worth closing this week, not eventually. [Lenny’s Podcast, June 24]
• Soichiro Honda’s lesson on R&D, applicable to AI experimentation budgets: he spun his research division out as a separately funded company specifically to protect it from short-term profit pressure, because 99% of research is failure and a parent company will always be tempted to kill the budget line. [Founders, June 28]
Book worth noting this week:
Life at the Speed of Play by Mark Pincus, discussed on Success Story June 25. His core argument: be passionately attached to your instincts and dispassionate about your ideas. The instinct is the why. The idea is just one attempt at the how, and it’s allowed to be wrong.
BEFORE YOU GO
After running the botsitting check on your own AI workflows this week, here’s the question I want an honest answer to:
Is there an AI rollout in your business that hit B-plus six months ago and nobody’s touched since?
Hit reply and tell me what it is. You don’t need to have a plan to fix it yet. Naming it is the first step out of the trap.
If you found your own B-plus trap this week and want a second set of eyes on closing the gap, that’s exactly the work I do at The Parker Group. No pitch, no pressure. Just a straight 30-minute conversation about what’s actually possible with the tools you already have.
Book a free strategy call: parkergroup.us
Until next week,
Steve Parker
Founder, The Parker Group | AI Consultant, MBA, PMP
parkergroup.us | theparkergroup.substack.com
La Grange, Kentucky