The Accounting Technology Lab

Brian F. Tankersley, CPA.CITP, CGMA: Welcome
to the Accounting Technology

Lab, brought to you by CPA
Practice Advisor, with your host

Randy Johnston and Brian
Tankersley.

Randy Johnston: Welcome to the
Accounting Technology Lab. I'm

Randy Johnston, with co-host
Brian Tankersley, and we are so

lucky today to have a guest, the
founder of Tax GPT cash, Ali,

and you know, cash. Appreciate
you taking time with us today.

Would you like to give our
listeners a little bit of your

background, please?

Kashif Ali: Yes, sure. Thank you
so much, Renny and Brian, for

having me. I really appreciate
that. A little bit background

about me, I pre.. I studied
accounting in college, but I

graduated with a journalism
degree, and I worked as a

journalist for six years. Then I
did another pivot, a career

pivot, and learned to program.
And then I ended up working for

Adobe for three years. I started
two other companies that didn't

go anywhere, and when I was
trying to shut those companies

down. I had a lot of tax. I was
looking up a lot of tax

information. I was not able to
find correct information, so in

a very short.. that's how I
ended up starting Tax CPD. Well,

Randy Johnston: you know, that's
kind of an interesting

background. I knew about your
Adobe background, but I didn't

know about the shutdown story,
so I appreciate knowing that.

Now, friends that are used to
being with us, we wanted to talk

with Cash for a bit, because the
application of AI in tax

research is pretty broad, and as
you know, the Bigs have this

with Thomson Reuters Checkpoint
Co Council and Answer Connect

from Walters Kluwer and the BNA
Bloomberg, all are AI powered

and authoritative, but I believe
that products and platforms like

Tax GPT or Blue Jay or
Accordance or more, and there

are about eight of those
products that we're tracking

right now, are just easier to
use for much of your team now.

That said, we really wanted to
just get a little bit more

insight on the application of
AI. Now, we've talked in other

podcast episodes in other labs
about the way this AI is

affecting the accounting
profession in such a big way. We

have the general tools,
including the announcements of

this week with CCH announcing
their new relationship with Chat

GPT, and you know we'll continue
to follow all those different

things, but you have the large
language models, the

productivity parts, you have the
AI built into platforms that

augment the platforms, I think.
Cash, that's where I put Tax

CPT, and then you have the
agents and the MCPS, so kind of

three different levels of
strategies that you need to be

able to address on all fronts.
And we've encouraged you to set

up the proper policies and to
get the right governance in

play, and so forth. So, cash,
you're in the thick of all this

bloody stuff, and we're, we're
thinking we're headed into a

token economy, in effect, and
you know, so just give us a

little bit of your insights on
the application of AI and tax

research, or AI in general,
across the profession, please.

Kashif Ali: Yeah, so when we
started, and it was very early

on, the old way of looking up
the information was sold, and we

all used to do that. You go on
Google, you do a keyword search,

you read 50 different articles,
summarize it, synthesize it,

form an opinion, write an email,
tell back to your client, like

very manual process, hours upon
hours used to go in there. So,

and talking about my own
frustration, that was my own

frustration about looking up the
information, and like I need, I

can read the laws, but I'm not a
professional. But how can I make

a judgment call on this? Right,
I didn't have enough money to

hire a tax lawyer or an
accountant, so I was like, let

me try to make a tool, and, and
that's how Tax CPD was born.

Very early on, we went viral,
and we had 1000s of people start

using, and it was more consumer
lens that we were building the

product, and eventually, right
after the tax season, we noticed

that people still coming, still
using Tax CPD, and turns out

those were accountants, lawyers,
enrolled agents, professionals

in the industry advisors. So we
did close to 300 discovery

calls, and this is I'm talking
about early 2023 to mid December

23 so the whole year with 300
back and forth calls with

customers to understand, really
understand, truly understand

their pain points, workflows. I
went into a lot of firms in

their meeting rooms, I saw them
work. And how they find

information, so the early, the
first one to building trust, the

easiest thing we were, the my
apologies, the first thing that

we did, we started giving the
sources, because at that time, I

mean, ready, you have seen the
conversation, how much it has

grown in last three years. At
that time, 2023 people were

like, there is no way that AI
can do what I do, and I don't

even trust AI. So, you know,

Randy Johnston: you'll
appreciate that that time the

hallucinations were so bad. Oh,

Kashif Ali: yeah, yeah, our
friendly face

Randy Johnston: later was
declared dead in a meeting that

we were in AI, and do remember
our conversation, because we've

known each other a few years at
this point, that you'd done all

these discovery calls to try to
get to this conclusion on the

platform, but this trust level,
just to go back, people didn't

trust, they didn't think that AI
would get there that fast to be

trustworthy, if you will, and
you know, for Brian and I, that

is one of the key things on AI,
is how do we build trustworthy

models and get accurate device,
so this citing of sources that

you were doing that was really
critical in my mind.

Kashif Ali: Yeah, yeah, I mean,
I

Brian F. Tankersley, CPA.CITP, CGMA: mean,
it's a, as a, as a, as a

partner, you don't trust your,
your associate, your two year

associates without somebody
going through it in detail. So

why would you trust AI at, you
know, more than you trust the

human being that's been through,
that's been through, you know,

master's degree program, and you
know, pass the exam, and all

that.

Kashif Ali: Yeah, and 100%
agree, like neither should

people trust, trust but verify,
like checking, right. So we

started giving sources. There is
a lot of cool tech that we

developed under the hood. We
made the tax code consumable to

AI because tax code is written
for humans to read, it's start

from somewhere, right? And
without going into much

technical detail, we made it
consumable to AI and make it

make sense. And then we created
hallucination control mechanism,

so it does not make up stuff.
More than a million tax sources,

documents, 1000s of trusted
websites, where we look up the

information on a regular basis
in real time, and vetted by

humans, CPAs, tax lawyers that
are part of our team, that's how

we create the first version of
the product, where we were

giving people sources and
walking them through in into the

productivity, that was early
2024 Since then, we've been

building on top of this
foundation, and now conversation

has changed, going looking for
information to actually AI doing

the work in a workflow, so we
can chat more about it, but if

you want to focus, yeah,

Randy Johnston: but you know, as
it turns out, you know, part of

the reason I obviously we've
recorded a podcast on Tax GPT in

the past here for the lab, but I
believe it was March of this

year that you released your
autonomous tax workflow agent,

and you know our listeners have
had us or heard us talk about

agents and why they're important
in this differential of, you

know, are the agents separate
and developed with agents and

model context protocol, or are
they part of the product? In

your case, you've got this agent
that I think you've appended,

you helped me get the right
words, but you've wrapped your

product with this, so we don't

Kashif Ali: have an agent, we
have agents. So, what we created

is, we created a model
orchestration layer, because we

have the best tax knowledge
available, and as compared to,

you know, we don't have to talk
about, as compared to horizontal

model, like why their
information is faulty, everyone

knows that. Why people should
not upload their client

information into Chat CPT or
Claude, because they are not

secure. So all of that, with all
of that information, as we are

developing this product, we
created this memory layer right

after our research agent, where
users can put all the firms are

creating all of their client
information, so imagine we

create the best intelligence
brain, this is our tax research

product, and then we created the
memory layer, that is a client

intelligence product, where
people can dump all of the

information, now people were
asking for, like, can it do

this, can it do this, can it do
this, what. People were asking

was actually like to actually do
the work, so we started with the

review process, automating one
review in part of the

preparation. Then we launched
the preparation, and what we

created is the agent
orchestration layer, where we

have created 30 plus agents
where you can prepare, onboard,

create work papers, create an
organizer binder, do a month

hand bookkeeping, book closing,
you can create an R and D study.

We with one of our partner from
that, we brought down their 1r

and D study time from 60 hours
to less than 30 minutes. We

people can do advisory
projections and all of that. So

we created this new agent
architecture where people tell

agent what to do and agree, and
they an agent and go does that

and human are in the loop making
judgment call, so that's our

orchestrate the agent
orchestration layer, we call it

tax cpt co work. Okay,

Randy Johnston: I appreciate the
clarification on that, because I

remember when Agent Andrew was
initially released, because I

talked to Andrew about it, you
know, as it turns out, but you

know this, I'm going to call it
end to end. We'll

Kashif Ali: just

Randy Johnston: pick on the
onboarding as an example.

Onboarding being a very common
problem that firms are trying to

solve, but we try to get people
to think about the end to end

process, from PBC gathering and
the engagement letters all the

way to the delivery process and
talk about that in the context

of portals because there's so
much interest in 1040 work paper

prep products, the likes of
Black or Tax Autopilot or Filed

or Magnetic or Solomon, and then
the special DK one products, the

additives, the abacus, and the
like that are doing that type of

work. So, do you see yourself in
a where you are today and where

you're going as continuing to
build out your agent library and

supporting that end-to-end
workflow? And tell me just how

to think about that, please.

Kashif Ali: So, the best way to
place Tax CPT is thinking that

we're creating the super app for
accounting, tax, and advisory

firms. So, you have named a lot
of different people are doing a

lot of different things, they
all have their swim lane,

someone is in research, someone
is in practice management,

someone is in prep, someone is
in onboarding, someone is in

right. What I'm saying is, we're
creating the super app that you

can do everything in tax CPD,
and that's the goal, because the

number one pain point when I was
doing all of this discovery, and

people were walking me through
the workflows. I'm like, why do

you have 14 tabs open to find a
W-2? First of all, why do you

have so many different tools,
right? And maybe this industry

needed an outsider like me to
look at that and build something

better. Maybe we don't need a 19
step elaborate workflow to

collect a last year 1040 Maybe
we can do better, maybe we can

do easier. Right, there are so
many products that are so hard

to set up that actually setting
them up become a job in its own

self, right. So our goal is, and
we're not asking people to, by

the way, get rid of all of the
tools that they are using right

now. For we're building is we're
building agents that can go and

do things on your behalf. So
imagine you very similarly, like

prompting was such a foreign
word three years ago, right?

Today, agent is that word, so
you ask, you tell your agent,

you get, you said go in my
onboarding tool, xyz, whatever

that tool is, take that
information, extract that

information here now. Go to my
preparation tool, right? Do the

preparation right now. Take that
information and go to Agent

Andrew to do the review. Then
you do a review, human in the

loop, and now go deliver it to
in the client portal, and you

know the delivery, all of that
community agent can do all of

that, it can concatenate
different systems, right, so

that's how we see ourselves an
AI operating system, not just a.

Single tool, or two tools, or
collection of two, three tools

that are in three different
lanes, because the power of

everything combining together is
best intelligence brain, tax

intelligence brain, and all the
information, the context, and

the memory, and agents doing the
work, so we believe that we will

be able to save people so much
time and make them way more

productive, and that's the goal
of Tax CPT building this AI

operating system, so people can
truly see the ROI of AI

Randy Johnston: well. That's a
beautiful explanation. Thank

you. And I, as you're saying
that, you know the good news,

bad news, and we try not to put
things that I'd call timely in a

lot of our podcasts, unless it's
really breaking news. But

earlier this week I was teaching
for a conference, and the very

last session I taught was AI in
tax, and the very last call I

made before having you join us
today was to a CPA firm in

Brooklyn, tax team, right? And
you know, I have to apologize to

both of those groups, saying,
you know what I said on the

call, and what I said publicly
in the session was, I am not

aware of anybody that's built a
family of agents for tax that

are effective yet now for those
of you attending today, that's

how you learn new stuff, but it
was part of the reason when cash

and his team reached out, we
said yeah, it would be great to

get an update, because I was
aware of, you know, Agent

Andrew, but I didn't realize
this, we'll call it Agent

Operating Platform, which, you
know is a nifty way to position

it, and reality is it's not
really even though it's a text

CPT product, it sounds like it's
really of the agentic MCP layer,

as the way I'm thinking about
it, and I also think your

comment earlier about, you know,
prompts are one thing, and we've

taught a lot of people to prompt
AI engines, but that the agents

are now your new prompting tool,
and of course, exactly during

this timeframe that we're
speaking, in the prior 30 days,

of course, we had Microsoft
released Agent 365 on May one,

and earlier this week also
released their Scout for

building agents, so you know
we've been watching for agent

environments, and over the next
few weeks, for me personally,

I'll be teaching how to build
these agent environments, but

you know, maybe for some of you
listening today, build versus

buy, maybe you want to buy
rather than build, because a lot

of firms have been down this
path of trying to build agents,

and you can build pretty simple
agents well, but these ones that

have text knowledge are are
different, and you were correct,

Kashif Ali: and complaints,

Randy Johnston: the general,
yeah, and complex, and these

general AI tools just don't do a
real good job at all of this. We

may think they do, but you also,
I think, cash correctly called

out the security risks, because
I have watched instructors at

conferences say, 'Well, let's
just upload this tax return into

ChatGPT. Like, no, that is one
of the dumbest, simplest things

you can do, and if you're a
listener and you just got

insulted, I think I'm okay with
that, because you, we shouldn't

be doing that type of work so

Brian F. Tankersley, CPA.CITP, CGMA: well,
and that's that's no different

from when people were emailing
tax returns 10 years ago, you

know, before they really, or 20
years ago, before they really

adopted portals. It's, you know,
they.. I think sometimes people

don't know what they don't know,
but I mean, I will say that my

experience, anyway, with, with,
with, with AI, and especially

with generative AI, is that the
thinner the information is, the

more likely it is to
hallucinate, because these,

these agents almost want to
please you, because they, and so

the problem, of course, of
creating your own agent here is

that you don't want, you don't
want the agent to tell you what

you want to hear, you know, you
have, you know, you have short,

short timer staff people that
for that to tell you what you

want to hear, you know, the
eight, you want the agent to

tell you the good, the good and
ugly truth, so that you can deal

with it, and that's the, that's,
you know, that beyond just the

complexity of getting all the
right stuff into the training,

it's also trying to keep the
mental health of the agent okay,

so that it doesn't hallucinate,
and just dream up whole new,

whole new things that don't
exist.

Kashif Ali: I would, I would add
two things here very quickly.

The general purpose AI, Open AI
Cloud, and all of Gemini, those

agents and those tools are
created, they are like social

media. They want your attention.
They want to keep you engaged.

They are not made for account
rents and tax work and advisory

work. They will, but Brian
correctly pointed out, in order

to please you, they can make up
stuff. We do side by side

comparison, and it's very easy
to gaslight them to say, like,

this tax law exists, is like, oh
yeah, I'm sorry, it does exist,

but it didn't, so I don't know
if you guys have tried it out in

tax CPD, will you get a
different experience, right,

like a professional tool should
be if someone tried to gaslight

Tax CPD, it's like, no, no, this
tack, this section does not

exist, and this is the
interpretation of this section

that exists. One thing, another
thing is we don't want your

attention, we want to help you
get your work done effective,

effectively. If you ask Tax GPD,
what is the weather is like

outside, it's gonna say that's
not a question that I'm designed

to answer, right? So we, it's a
work tool, it's a professional

work tool, and this is how we
like to keep it. So that's one

thing you know, that a
difference between a

professional or something,
general purpose. The second

thing is, as people are learning
through this information, it's

as important is that how to you,
we are at that level of AI cycle

that it's never been more
important to learn to wield AI,

and one very interesting thing
that I see is that AI matches

the capability of who is using
it. We see this in our tool, and

we see it overall in our
company. If you are a senior

person and you know what you're
doing, you get way more done as

compared to someone who's not
right. So that's an interesting

anecdote. I don't have very big
learning here or anything, but

just wanted to share that, that
how a lot of certain people are

can be 10x 50x 100x more
productive, and I'm talking

about engineers here that are
truly willing. Yeah, there is a

difference between people who
really go all in and people who

don't, and that, that, that I
see that in engineering, I see

that in a lot of different
professions, and we see that in

the tool, and that gap is
widening. So, my parting

thought, and I don't want to say
this conclusion of what I wanted

to say here is, if you haven't
ever tried AI or ever worked in

agents and MCP, this is the best
time to jump back in, because

this gap, it gap is widening,
and people should be on really

learning about this stuff.

Randy Johnston: Yeah, in fact,
I, as you were just saying,

parting thought, I'm thinking,
yeah, that is a super parting

thought, because here we've been
talking about my, and I hate to

use popular words, but the
sycophants of AI, you know,

trying to please us, if you
will, as opposed to I've got

real work to get done, and I was
thinking about the gap that you

just identified, because I've
been watching the AI gap widen,

and you do have people that are
consuming millions and 10s of

millions and hundreds of
millions of tokens in a day or a

week, and they are getting way
more done because they know what

they're doing, and so we've got
casual users. It's almost like

professional drivers versus
casual drivers. I notice so many

people think they know how to
drive, and I'm looking at them

saying, oh, mg, where did you
learn to drive, right? And I'm

not saying I'm a great driver.
Brian's written with me, so he

knows that I'm not a great
driver, but I'm safer than most

because I pay attention when I'm
driving, and paying attention

when using AI may be part of the
formula here. So, Brian,

questions, parting thoughts from
your side.

Brian F. Tankersley, CPA.CITP, CGMA: Yeah,
so, so, so, I guess I would

just, you know, you know, cash,
about three years ago we were

talking about prompting, now
we're talking about agents and

MCPS, and those kinds of things.
What do you think the future

looks like? What do you think
the future of work looks like

with AI, and what are some of
the things agents are going to

handle on an automated or
agentic basis in the near future

that people might not have
expected them to be able to

handle?

Kashif Ali: It's very hard to
predict the future. Sure.

Brian F. Tankersley, CPA.CITP, CGMA: Yeah,
and we understand that, that

you, you know, about, you know,
our good friend Dr. Bob Spencer

often said that 20 seconds in
the future is about as far as

you can be accurate, so we
understand this. Okay,

Kashif Ali: exactly. I, my
guesstimate, and where things

are going, you know, you can see
the future. What is going to

happen? And I remember being on
a podcast three years ago, and I

gave them this example. I told
them that I was an average

programmer, but where AI tools
were at that point, I was, I

became a 10x programmer, and 10x
programmer is an example, like

someone who's so productive,
like a sorcerer, but the 10x

programmer who learned AI was
100x 1,000x and this is trend

that continue, I draw a lot of
my inspiration for any future of

work. What is happening in
programming and engineering

today? And the coolest thing is
that I get to see it in Silicon

Valley, and in my team, and a
lot of my friends that have

companies, and so, so based on
that, this is my thesis. The

future of work is that all the
manual and repetitive redundant

work is gone. You don't need 17
step workflow. You actually

don't need to build the workflow
right. Agents does thing what

you want them to do, so for
example, you can have one agent

that is preparing and one agent
that is reviewing, and then you

can concatenate agent, and they
are doing 10 different things,

so you can spin up 1050,
hundreds of different agents,

sub-agents that are doing that
job, you can be sleeping, and

you can be traveling, and agents
are working, they're doing the

work, so that's like the swarm
of agents, that's that, and then

the last and very best part of
all of this, all of these

agents, and including, by the
way, this is I'm talking about

tax CPT agents that we are
creating. All of these agents

are recursive learners, so they
improve and they learn from

their mistake, right? So, what
it means for tax and accounting

world. So, all the redundant
work is done. All of that is

gone now. The judgment of the
people are gonna matter so much.

Your years of experience, right?
Agent is doing something, and it

can tell you that this is where
I want your judgment. If you

make this choice, here's the
risk for this client, and here's

the reward for this. Right, the
risk factor is nine, but reward

factor is this. But if you want
to have the reward risk, and you

can, by the way, you can do the
settings also. So we believe

this will enable a one person,
$1 million practice, and we

people will be able to get free
up and do more than compliance

work. People will be actually
able to do advisory work. I know

this is a big conversation in
the industry. I also believe

that billable hours, we know
there is a huge conversation

around that, probably going to
turn into outcome-based pricing.

There is a lot of people are
already doing it, and yeah, and

we all know that there is a
shortage of professionals, not

enough new talent is coming in,
and offshoring is a big thing,

but the quality concern is a big
thing also, so I think very, I'm

just barely scratching the
surface, because we launched

this form of agent three months
ago, the biggest thing people

are saying is bringing
everything on shore, right,

scaling the firm, and because
the actual bottleneck always has

been finding good people in
order to scale, right? So these

are the few trends that I'm for
seeing that's going to happen in

the next three six months, a
year, or ahead, until unless

there is a artificial super
intelligence comes in. The

things are moving so fast that
you know I can only predict what

I know in the next three six
months a year.

Randy Johnston: So I think what
I heard there, you try and tell

me all bets are off if super
intelligence arrives. I think I

got that,

Kashif Ali: but. And

Randy Johnston: as it turns out,
just thinking ahead, and this is

maybe a little collateral, I do
believe the days of outsourcing,

I do believe the days that have
of dashboards and several other

technologies we've used, and and
I'm going to think more

carefully about your workflow
steps, there's several things

that we thought had to be a
certain way that don't have to

be that way anymore, and that's,
that's really, I think, the big

things that I learned from you
today. Cash, well, listeners, we

are so pleased to have you along
for another county technology

lab podcast. We hope you'll be
with us again next week when we

speak about technologies in the
accounting profession again.

Have a good day.

Kashif Ali: Thank you so much,
Trinity. Thank you so much,

Brian, for your time today.

Brian F. Tankersley, CPA.CITP, CGMA: Thank
you for sharing your time with

us. We'll be back next Saturday
with a new episode of the

Technology Lab from CPA Practice
Advisor. Have a great

Unknown: week.